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  • ICMS 2019
  • The 5th International Conference on Molecular Simulation
  • November 3-6, 2019 / Lotte Hotel Jeju, Korea

Program

Important Dates
Abstract Submission
March 1 ~ August 9, 2019
Acceptance Notification
August 30, 2019
Early Registration
~ September 30
Final Program Announcement
October 2, 2019
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Keynote Speaker

The list of speakers is not final, and is subject to be changed.

Theory & Methodology

  • Nov. 4 (Mon.) 14:00~14:30 Ryo Akiyama
  • Kyushu University, Japan
  • "Asakura-Oosawa theory: On the Origin of Excluded Volume Effects in a Crowding Media and the Progress."
    Biography and Abstract
Biography
Ryo Akiyama received his Bachelor and M. Eng. from Hokkaido University and his Ph.D. for physics from Nagoya University (1996). He did postdoctoral researches at Institute for Molecular Science and Cornell University. He moved to Kyushu University and is studying chemical physics and biophysics in the chemistry department since 2003. His current interests are various mediator-effects and they are studied based on methods of statistical mechanics, such as integral equation theories for liquid.
Abstract
Idea of depletion interaction was proposed by Asakura(1927-2016) and Oosawa(1922-2019) in 1954. Nowadays, the importance of the idea is recognized in a broad range of fields, such as colloidal dispersion, crowding problems in the cytoplasm, and so on. Let us think about the effective interaction between two colloidal particles. In Asakura-Oosawa theory, the depletants are excluded by the colloidal particles, but the depletants don't interact each other. So, the behaviors of depletants are “ideal gas” in this statistical mechanics theory. This modeling is reasonable in a dilute depletants solution. However, this simple model becomes worse as the packing fraction of depletants becomes higher. It is because that the excluded volume effect between depletants also becomes significant. In this talk, the recent progress of the related integral equation theory and of applications of the idea will be discussed.
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  • Nov. 4 (Mon.) 13:30~14:00 Chris Chipot
  • CNRS/University of Illinois, France
  • "Bridging microsecond computer simulations and millisecond biological events"
    Biography and Abstract
Biography
Chris Chipot received his PhD in Theoretical Chemistry in 1994 from the Henri Poincaré University, in France, conducting research on intermolecular potentials with a fellowship from the Roussel Uclaf Institute. Following a post-doctorate in the Department of Pharmaceutical Chemistry of the University of California in San Francisco, he became a National Research Council post-doctoral fellow at the NASA Ames Research Center. He joined the CNRS in 1996 at the University of Lorraine, where he worked on the modeling of the biological membranes by means of computer simulations, while developing original approaches for free-energy calculations and the exploration of rare events. His developments also extend to the accurate description of intermolecular interactions, including induction phenomena. He received his habilitation in 2000, and was promoted research director in 2006. Since 2012, he has been directing an Associate International Laboratory established between the CNRS and the University of Illinois at Urbana-Champaign, where he is affiliated to the Department of Physics.
Abstract
In a matter of three decades, free-energy calculations have emerged as an indispensable tool to tackle deep biological questions that experiment alone has left unresolved. In spite of recent advances on the hardware front that have pushed back the limitations of brute-force molecular dynamics simulations, opening the way to time and size scales hitherto never attained, they represent a cogent alternative to access with unparalleled accuracy the thermodynamics and possibly the kinetics that underlie the complex processes of the cell machinery. From a pragmatic perspective, the present lecture will draw a picture of how the field has been shaped and invigorated by milestone developments, application, and sometimes rediscovery of foundational principles laid down years ago to reach new frontiers in the exploration of intricate biological phenomena. Through a series of illustrative examples, distinguishing between alchemical and geometrical transformations, I will discuss how far free-energy calculations have come, what are the current hurdles they have to overcome, and the challenges they are facing for tomorrow.
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  • Nov. 5 (Tue.) 15:40~16:10 Jhih-Wei Chu
  • National Chiao Tung University, Taiwan
  • "Multiscale Decomposition of Mechanical Properties in Biomolecules"
    Biography and Abstract
Biography
Dr. Chu received his bachelor’s degree in chemical engineering from National Taiwan University in Taiwan and his PhD in chemical engineering from MIT. His research aims to elucidate of the manner by which the composing details of a complex molecular system determine the functional activities. He focuses on the problems of protein dynamics, protein conformational changes, and protein allostery. He develops and applies multiscale computational methods based on physical chemistry principles with emphasis on close collaborations with experimental counter parts.
Abstract
Biomolecules such as protein and nucleic acids duplexes are of vital importance in biology. A key question for such systems is how do subtle differences in the chemical composition cause extended variation in structure and mechanical properties in the system. In this work, all-atom molecular dynamics simulations and multiscale coarse grained modeling were conducted to resolve the structures and mechanical couplings in dsDNA, dsRNA, and an enzyme system. The multiscale computational framework developed here allowed quantitative comparison of the strengths of mechanical couplings for the different interactions in a molecule as well as across different systems. It was thus established that dsRNA has significantly higher strengths for mechanical couplings in backbone and sugar puckering than those of dsDNA. For nucleobase interactions of hydrogen bonding and stacking, on the other hand, dsDNA exhibited stronger mechanical couplings. Moreover, we showed that the mechanical couplings in the protein structure can be utilized to capture the patterns of sequence correlation in a multiple sequence alignment.
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  • Nov. 5 (Tue.) 17:00~17:30 Seung Soon Jang
  • Georgia Institute of Technology, USA
  • "Multiscale Modeling of Multicompartment Micelle Nanoreactors"
    Biography and Abstract
Biography
Prof. Seung Soon Jang obtained his B.S. (1992), M.S. (1994), and Ph.D. (1999) in Seoul National University, Korea. After his education, Dr. Jang worked at Samsung Electronics for two years as a senior engineer, and then joined the Materials and Process Simulation Center (MSC) of California Institute of Technology as a postdoc and then became research director. Dr. Jang joined the School of Materials Science and Engineering at the Georgia Institute of Technology in July 2007 as an assistant professor and was promoted to a tenured associate professor in 2013. His research interest is to investigate various nanoscale systems using the multiscale modeling methods to achieve molecular architecture-nanoscale structure-property relationship, which makes fundamental improvement in new material development for various applications. So far, he has made more than 120 peer-reviewed journal publications and more than 90 invited presentations for various topics.
Abstract
In recent years, research in industrial applications of polymeric materials has begun to explore the field of immobilized catalysis. In particular, the idea of catalysts bound to a micelle backbone, creating a nanoscale molecular reactor (commonly referred to as nanoreactor), has become an area of great interest. From a computational perspective, investigating the potential of micelles as nanoreactors requires analyzing the miscibility of block copolymers, both on a fully atomistic and on a mesoscale basis. The model proposed by Flory and Huggins offers an interaction parameter χ which quantifies the favorability of mixing between two polymers. This interaction parameter depends on many process conditions, not least of which are the temperature and composition of a solution, in order to properly estimate the strength of the interaction between a given pair of polymer molecules. Extensive work has already been completed in this group to establish a robust method of estimating the χ-value for a given pair of molecules; this information is necessary for preparing coarse-grained modeling and simulations (e.g., micellization simulations). In our present work, we apply miscibility analysis to a relatively nascent technology in immobilized catalysis science, viz. the multicompartment micelle nanoreactor. This technology offers a way to harness both the enhanced reactivity of homogeneous catalysis and the ease of separation traditionally enjoyed by heterogeneous catalysis. Through the use of mesoscale calculations, we will study the feasibility of a three-compartment micelle nanoreactor. For this purpose, we have developed a systematic strategy to calculate χ parameters, which has been applied and validated through mesoscale simulations of micelle consisting of triblock copolymers. We hope to demonstrate that this triblock copolymer can form a micelle capable of reaction compartmentalization and tandem catalysis, two hugely promising capabilities for highly selective multistep-catalyzed reactions.
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  • Nov. 6 (Wed.) 10:40~11:10 YounJoon Jung
  • Seoul National University, Korea
  • "Charging dynamics of an ionic liquid electric double layer capacitor studied by molecular simulation"
    Biography and Abstract
Biography
YounJoon Jung graduated from Seoul National University summa cum laude with a Bachelor's degree in Chemistry in 1994, and received a Master’s degree in 1997 from the same university. He obtained his Ph. D from Massachusetts Institute of Technology in 2002, where he worked under the guidance of Robert J. Silbey. His Ph. D. work focused on developing theoretical formulations of the single molecule spectroscopy and electron transfer reactions in condensed media. He was awarded with Miller Fellowship from the University of California, Berkeley, where he worked as a Miller Research Fellow with David Chandler from 2002 to 2005. After working as a research associate with George Schatz and Mark Ratner at Northwestern University, he joined the Seoul National University in 2006. He was a visiting scholar at the University of California, Berkeley in 2012. He served as Vice Director of Center for Space-Time Molecular Dynamics from 2013 to 2016. He is a recipient of Kook Joe Shin Award from the Korean Chemical Society this year. He is a theoretical chemist, specialized in statistical mechanics. He uses both statistical mechanical theories and computer simulation methods to elucidate structure and dynamics of complex chemical systems, including glass transitions, ionic liquids, polymeric systems, and interfacial phenomena. Currently, he is developing a novel approach to non-equilibrium statistical mechanics.
Abstract
We investigate the charging phenomena of an electric double layer capacitor (EDLC) by conducting both equilibrium and non-equilibrium molecular dynamics (MD) simulations. A graphene electrode and 1-ethyl-3-methylimidazolium thiocyanate ([EMIM]+[SCN]−) ionic liquid were used as a system for the EDLC. We clarify the ionic layer structure and show that an abrupt change of the ionic layers leads to a high differential capacitance of the EDLC. The charging simulations reveal that the charging dynamics of the EDLC is highly dependent on the rearrangement of the ionic layer structure. Particularly, the electrode charge during the charging process is consistent with the perpendicular displacement of ionic liquid molecules. From this property, we analyze the contribution of each molecular ion to the electrode charge stored during charging. Charging of the EDLC is largely dependent on the desorption of the co-ions from the electrode rather than the adsorption of the counter-ions. In addition, the contribution of bulk ions to the charge stored in the EDLC is as important as that of ions adjacent to the electrode surface contrary to the conventional viewpoint. From these results, we identify the charging mechanism of the EDLC and discuss the relevance to experimental results. Our findings in the present study are expected to play an important role in designing an efficient EDLC with a novel perspective on the charging of the EDLC.
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  • Nov. 5 (Tue.) 15:10~15:40 Ikuya Kinefuchi
  • University of Tokyo, Japan
  • "Bottom-up construction of non-Markovian coarse-grained models"
    Biography and Abstract
Kinefuchi received his B.S. (2001), S.M. (2003), and Ph.D. (2006) from The University of Tokyo. He is an associate professor at Department of Mechanical Engineering, The University of Tokyo. He is interested in micro/nanoscale heat and mass transfer, rarefied gas dynamics (kinetic modeling of evaporation at a liquid-vapor interface), and mesoscale modeling (non-Markovian dissipative particle dynamics).
Abstract
The dissipative particle dynamics (DPD) method is a powerful tool for simulating mesoscopic systems, where the computational cost prohibits the application of the molecular dynamics (MD) method. The conventional DPD method is based on a top-down coarse-graining approach, where the force parameters and scaling factors are chosen to recover macroscopic properties such as compressibility and diffusivity. However, such a simple matching procedure may not be applicable to a complex fluid system involving the matching of multiple properties. Here, we present a bottom-up coarse-graining approach to overcome this difficulty. The equation of motion of non-Markovian dissipative particle dynamics (NMDPD) is derived based on the Mori-Zwanzig (MZ) formalism. The interaction model between coarse-grained particles is directly constructed from an underlying MD system without any scaling procedure. To validate our formulation, we construct coarse-grained models of high-density Lennard-Jones systems, where the typical time scale of the coarse-grained particle motions is comparable to that of the fluctuating forces. The NMDPD models reproduce the temperatures, diffusion coefficients, and viscosities of the corresponding MD systems more accurately than the DPD models. Additionally, the pairwise interaction model can be further improved by the iterative Boltzmann inversion (IBI) correction to reproduce the static properties such as a radial distribution function and pressure, while this correction does not deteriorate the accuracy of the dynamic properties reproduced by the original model. Our result suggests that combining the advantages of both the MZ formalism and the IBI correction helps construct accurate coarse-grained models which reproduce both the static and dynamic properties of mesoscale systems.
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Big data & Machine learning

  • Nov. 5 (Tue.) 10:40~11:10 Michele Ceriotti
  • Ecole Polytechnique Fédérale de Lausanne, Switzerland
  • "Machine learning for atomic and molecular simulations"
    Biography and Abstract
Biography
Michele Ceriotti received his Ph.D. in Physics from ETH Zürich. He spent three years in Oxford as a Junior Research Fellow at Merton College, funded from a Royal Society Newton Fellowship and a Marie Curie Fellowship. Since 2013 he works as an assistant professor at the Institute of Materials at EPFL, leading the laboratory for Computational Science and Modeling. His research interests focus on the development of methods for molecular dynamics and quantum simulations of hydrogen-bonded materials, machine-learning methods for the study of complex systems at the atomistic level, and on their application to problems in chemistry and materials science. He has been awarded the IBM Research Forschungspreis in 2010, the Volker Heine Young Investigator Award in 2013, an ERC Starting Grant in 2016, and the IUPAP C10 Young Scientist Prize in 2018.
Abstract

Machine learning is finding applications to more and more tasks, in science as much as in everyday life. In this talk I will focus on how atomic and molecular simulations are being transformed by the use of statistical regression models, that make it possible to approximate accurately and efficiently atomistic properties computed from a few reference electronic-structure calculations.

I will argue about the advantages that are brought about by a physically-motivated framework, and about the insights that can be obtained by a critical application of ML methods. Examples will be given spanning molecular and condensed matter systems, and properties as diverse as magnetic nuclear chemical shieldings and the electron charge density, underscoring the general applicability of the process.

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  • Nov. 5 (Tue.) 17:00~17:30 Hyunju Chang
  • Korea Research Institute of Chemical Technology, Korea
  • "Predicting the electronic properties of double perovskites using machine learning."
    Biography and Abstract
Biography
Hyunju Chang is currently a Principal Researcher at chemical data-driven research center in Korea Research Institute of Chemical Technology (KRICT). She received her Ph.D. in Physics from Michigan State University, USA, in 1995. Then she had worked as a postdoctoral researcher in Northwestern University, USA. Since 1996, she has been working at KRICT as a computational materials scientist. Her current research interests include computer-aided materials design and materials informatics to predict the materials properties from the materials database.
Abstract

Since many open-access databases of materials properties based on the first-principles calculations have been available, materials scientists have started to find the prediction tools for the materials properties from the databases. Recently, the various machine learning techniques have been applied to predict the electronic properties, such as band gap, melting point, and heat of formation.

In this presentation, I will introduce various methods of machine learning algorithms and feature engineering that we have attempted to predict various material properties using the open-access materials databases. We found that “Gradient Boosting Tree Model” is an efficient machine learning algorithm to predict band gaps in the inorganic compounds, comparing with the previous works. Then we applied the optimized machine learning algorithm to predict the band gaps of the halide double perovskites using our own database, which we had generated for high-throughput screening of Pb-free halide perovskites. I will discuss the machine learning approaches and statistical analysis of the selected features to identify design guidelines for the discovery of new lead-free perovskites.

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  • Nov. 4 (Mon.) 13:30~14:00 Yousung Jung
  • Korea Advanced Institute of Science and Technology, Korea
  • "Exploring solid-state chemical space by machine learning"
    Biography and Abstract
Biography
Yousung Jung has received the B.S. degree from Seoul National University and Ph.D. in Theoretical Chemistry from University of California, Berkeley with Martin Head-Gordon. After a postdoctoral work at Caltech with Rudy Marcus, he joined the faculty at KAIST in 2009. His research interests involve the developments of density functional methods and their applications for the discovery of new energy materials. He is now combining these efforts with machine learning techniques to significantly further expand the search space and increase the prediction accuracy towards reliable materials design. He is the recipient of Pole Medal (2018, Asia-Pacific Association of Theoretical and Computational Chemists), KCS Young Physical Chemist Award (2017), Chemical Society of Japan Distinguished Lectureship Award (2015), and KCS-Wiley Young Chemist Award (2013).
Abstract
Discovery of a new material with desired properties is the ultimate goal of materials research. To date, a generally successful strategy has been to use chemical intuition and empirical rules to design new materials, but these conventional approaches require a significant amount of time and cost due to almost unlimited combinatorial possibilities of inorganic materials to explore in chemical space. A promising way to significantly accelerate the latter process is to incorporate all available knowledge and data to plan the synthesis of the next material. In this talk, I will present a few initial frameworks we have developed along this line to perform machine-learned density functional calculations, to predict the properties of a material using simple representations, and to generate new materials for a target property using materials deep generative model.
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  • Nov. 4 (Mon.) 10:40~11:10 Alexandre Tkatchenko
  • University of Luxembourg, Luxembourg
  • "Towards Universal Machine-Learning/Physics Model of Molecular Properties in Chemical Space"
    Biography and Abstract
Biography
Alexandre Tkatchenko is a Professor of Theoretical Chemical Physics at the University of Luxembourg and Visiting Professor at the Berlin Big Data Center. He obtained his bachelor degree in Computer Science and a Ph.D. in Physical Chemistry at the Universidad Autonoma Metropolitana in Mexico City. In 2008−2010, he was an Alexander von Humboldt Fellow at the Fritz Haber Institute of the Max Planck Society in Berlin. Between 2011 and 2016, he led an independent research group at the same institute. Tkatchenko has given more than 200 invited talks, seminars and colloquia worldwide, published more than 140 articles in peer-reviewed academic journals (h-index=55), and serves on the editorial boards of Physical Review Letters and Science Advances (an open-access journal in the Science family). He received a number of awards, including the Gerhard Ertl Young Investigator Award of the German Physical Society, and two flagship grants from the European Research Council: a Starting Grant in 2011 and a Consolidator Grant in 2017. His group pushes the boundaries of quantum mechanics, statistical mechanics, and machine learning to develop efficient methods to enable accurate modeling and obtain new insights into complex materials.
Abstract
"Mindless" learning from data has led to paradigm shifts in a multitude of disciplines. Can machine learning enable similar breakthroughs in _understanding_ (quantum) molecules and materials? Here, the two main challenges are: (1) the disproportionately large size of chemical space, even when only counting small organic drug-like candidates, (2) the complex nature of quantum interactions on different length and time scales. Aiming towards a unified machine learning (ML) model of quantum interactions, I will discuss the potential and challenges for using ML techniques in chemistry and physics. ML methods can not only accurately estimate molecular properties of large datasets, but they can also lead to new insights into chemical similarity, aromaticity, reactivity, and molecular dynamics [1]. However, to do so one needs to carefully unify spatial and temporal physical symmetries with purpose-designed ML methods [2,3]. While the potential of machine learning for revealing insights into complex quantum-chemical systems is high, many challenges remain. I will conclude my talk by discussing these challenges.

[1] K.T. Schütt, F. Arbabzadah, S. Chmiela, K.R. Müller, and A. Tkatchenko, Quantum-chemical insights from deep tensor neural networks. Nature Commun. 8, 13890 (2017).

[2] S. Chmiela, A. Tkatchenko, H.E. Sauceda, I. Poltavsky, K.T. Schütt, and K.-R. Müller, Machine Learning of Accurate Energy-Conserving Molecular Force Fields. Science Adv. 3, 1603015 (2017).

[3] S. Chmiela, H. E. Sauceda, K. R. Mueller, and A. Tkatchenko, Towards exact molecular dynamics simulations with machine-learned force fields. Nature Commun. 9, 3887 (2018).
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  • Nov. 5 (Tue.) 15:10~15:40 Koji Tsuda
  • The University of Tokyo, Japan
  • "Expanding the horizon of automated metamaterials discovery via quantum annealing"
    Biography and Abstract
Biography
Koji Tsuda received B.E., M.E., and Ph.D degrees from Kyoto University, Japan, in 1994, 1995, and 1998, respectively. Subsequently, he joined former Electrotechnical Laboratory (ETL), Tsukuba, Japan, as Research Scientist. When ETL was reorganized as AIST in 2001, he joined newly established Computational Biology Research Center, Tokyo, Japan. In 2000–2001, he worked at GMD FIRST (currently Fraunhofer FIRST) in Berlin, Germany, as Visiting Scientist. In 2003–2004 and 2006–2008, he worked at Max Planck Institute for Biological Cybernetics, Tübingen, Germany, first as Research Scientist and later as Project Leader. Currently, he is Professor at Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, the University of Tokyo. He is also affiliated with National Institute of Material Science (NIMS) and RIKEN Center for Advanced Intelligence Project.
Abstract
Complexity of materials designed by machine learning is currently limited by the inefficiency of classical computers. We show how quantum annealing can be incorporated into automated materials discovery and conduct a proof-of-principle study on designing complex thermofunctional metamaterials consisting of SiO2, SiC, and Poly(methyl methacrylate). The difficulty of this black-box optimization problem grows exponentially in the number of variables. Our quantum-classical hybrid algorithm consists of a factorization machine, an atomistic simulator, and a D-Wave 2000Q quantum annealer. Apart from the computational time needed for simulation, quantum annealing reduced the processing time to near zero regardless of the problem size. Our method was used to design complex structures of wavelength selective radiators showing much better concordance with the thermal atmospheric transparency window in comparison to existing human-designed alternatives. This result shows that quantum annealing can be used effectively in real-world design problems and indicates the direction of further applications.
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Soft matter

  • Nov. 4 (Mon.) 13:30~14:00 Changbong Hyeon
  • Korea Institute of Advanced Study, Korea
  • "Polymer physics perspective to chromatin inside cell nuclei"
    Biography and Abstract
Biography
Changbong Hyeon received his B.S. and M.S. degrees from Seoul National University and a Ph.D. in Chemical Physics from the University of Maryland at College Park. Following post-doctoral work at the Center for Theoretical Biological Physics in the University of California at San Diego, he joined the Chemistry department at Chung-Ang University in 2008 as an assistant professor and has been a professor at the Korea Institute for Advanced Study since 2010. His current research interests are in molecular motors, genome dynamics, and many other molecular/subcellular processes.
Abstract
Interphase chromatins are a polymer subjected to many physical constraints. We first illustrate dynamical arrest in a highly confined space by modeling chromosomes inside a nucleus as a homopolymer confined to a sphere of varying sizes. The onset of glassy dynamics might be the reason for the segregated chromosome organization in humans, whereas chromosomes of budding yeast are equilibrated with no clear signature of such organization. Modeling chromosomes using a model of heteropolymer based on the information of Hi-C data, we study its dynamics at different time and length scales. The local chromosome structures, exemplified by topologically associated domains, are highly dynamic with fast relaxation time (≲ 1 sec), whereas the long-range spatial reorganization of chromatin compartment is realized on a much longer time scale (≳ hour), providing the dynamic basis of cell-to-cell variability. Even when the dynamics is passive, the hierarchical organization of chromosome can explain many facets of chromatin dynamics. Active forces, modeled as a scalar noise, speed up the chromatin chain relaxation and push active loci towards the surface of chromosome territory, promoting the phase separation between inactive and active loci. Our findings of chromatin dynamics under passive condition can be used as a benchmark to understand the effects of other physical constraints on chromatin dynamics.
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  • Nov. 5 (Tue.) 15:10~15:40 Nobuyuki Matubayasi
  • Osaka University, Japan
  • "All-Atom Simulation of Polymer toward Rational Design of Separation Membrane"
    Biography and Abstract
Biography
Nobuyuki Matubayasi received his PhD degree in chemistry from Rutgers University in 1995, and after working at Institute for Chemical Research, Kyoto University, he is a Professor in the Division of Chemical Engineering, Osaka University since 2014. Through the combination with state-of-the-art molecular simulation, he has been developing statistical–mechanical theories of solvation and transport properties, with molecular-level analysis of solvent effects on functional molecules such as proteins, partitioning functions of such molecular aggregates as micelles, lipid membranes, and polymers, and electrical conductivity and diffusion in ionic liquids.
Abstract
Polymer is effective as a material for separation membrane. The performance as a separation membrane is often governed by the dissolution free energy of the permeant, which reflects sensitively the atomic-level interaction between the permeant and polymer and the mode of aggregation of the polymer medium (crystalline vs amorphous, for example). The purpose of the present work is to formulate and apply a free-energy method with all-atom MD for assessing the extent of absorption of a small molecule into polymer. To do so, we treat the polymer as a solvent and the permeant as a solute and develop a theory of solutions to accurately and efficiently compute the free energy of dissolution. It is demonstrated that all-atom treatment is predictive for the free energy of water dissolution irrespective of the hydrophobicity and hydrophilicity of the polymer, and the computed free energy is discussed in connection to the structures of the polymers and their interactions with water.
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  • Nov. 4 (Mon.) 10:40~11:10 Arun Yethiraj
  • University of Wisconsin-Madison, USA
  • "Polymers in ionic liquids"
    Biography and Abstract
Biography
Arun Yethiraj was born in India and received his B. Tech. in Chemical Engineering at the Indian Institute of Technology, Bombay. He received an M.S. at Louisiana State University, a Ph.D. at North Carolina State University, working with Professor Carol Hall, and did postdoctoral research at the University of Illinois, working with Professor Kenneth Schweizer. He joined the faculty of the Chemistry department of the University of Wisconsin in 1993. His research is in the statistical mechanics of complex fluids. He is a senior editor of The Journal of Physical Chemistry. His hobbies include tennis, guitar, and marathon running.
Abstract
Ionic liquids have generated considerable excitement for their varied potential applications and their interesting physical properties. The viability of ionic liquids (ILs) in materials applications is limited by their lack of mechanical integrity, which may be provided by mixing them with a polymeric material. Recent experiments on polymers in ILs have unearthed a wealth of interesting phenomena that raise fundamental questions. This talk focuses on computational studies of PEO in imidazolium ILs. We develop a physically motivated first principles force field for PEO and [BMIM] [BF4]; this force field is in quantitative agreement with experiment with no adjustable parameters. Based on the same quantum calculations we develop a hierarchy of united atom models with decreasing resolution and increasing computational efficiency. Microsecond simulations are required to obtain converged properties of the polymer, which displays a combination of ring-like and extended conformations. The simulations show the existence of a lower critical solution temperature which arises from conformational restrictions on the polymer molecules at low temperatures
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  • Nov. 5 (Tue.) 10:40~11:10 Yaroslava Yingling
  • North Carolina State University, USA
  • "Design of nanoparticles for gene delivery using all-atom molecular dynamics simulations and machine learning algorithms"
    Biography and Abstract
Biography
Yaroslava G. Yingling is Professor of Materials Science and Engineering at North Carolina State University. She received her University Diploma in Computer Science and Engineering from St. Petersburg State Technical University of Russia in 1996 and her PhD in Materials Engineering and High Performance Computing from the Pennsylvania State University in 2002. She carried out postdoctoral research at the National Institutes of Health National Cancer Institute prior to joining North Carolina State University in 2007. She received the National Science Foundation CAREER award (2012), American Chemical Society Open Eye Young Investigator Award (2012) and was named a NCSU University Faculty Scholar in 2014. Research interests in Prof. Yingling’s group are focused on the development of soft materials informatics, advanced computational models and novel algorithms for multiscale molecular modeling of soft and biological materials. She has published more than 80 papers and has been serving as an editor for Journal of Materials Science and as an editorial board member of ACS Biomaterials Science and Engineering and ACS Applied Materials and Interfaces
Abstract
Gene therapy holds the promise of treatment of numerous diseases including many types of cancer, cardiovascular diseases and genetic disorders. Even though methods for gene delivery have been an active research area since early 90s, no gene therapeutic agents have been FDA approved for use in humans. The progress in gene therapy has been hindered by lack of safe, predictable, and reliable methods for packaging, delivery, and transport of genetic material. Efficient wrapping or packaging of DNA is a critical part enabling gene delivery, where nucleic acids are transported across cell membranes with the help of transfection vectors such as proteins, cationic dendrimers or nanoparticles. Because DNA/RNA transfection is dependent on the size, shape, and surface properties of the DNA/RNA-vector complex, control over assembly structure is critical for creating effective transfection agents. Evolving nanomaterials to the clinic requires optimization, which is prohibitively expensive, and a mechanistic understanding of carriers-NA interactions, which remains unknown. We attempt to advance tailored materials for gene delivery by a multiscale optimization employing all-atom simulations techniques and machine learning algorithms. I’ll discuss two avenues for designing nanomaterials for gene delivery: the design of ligand functionalized inorganic nanoparticles and self-assembling DNA-based nanomaterials. We were able to design novel nanoparticle ligands capable of controlled wrapping of NA around NP and also predict self-assembling polyelectrolytes materials and their morphological response to the changes in salt concentration. Our results will enable design of more efficient gene delivery systems with enhanced biocompatibility and selectivity.
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Solid & Nanotechnology

  • Nov. 4 (Mon.) 13:30~14:00 Ya-Fang Guo
  • Beijing Jiaotong University, China
  • Plastic deformation mechanisms of double contraction nanotwinned Mg alloys
    Biography and Abstract
Biography
Ya-Fang Guo received her B.S. degree from Northeast University (China) and Ph.D. from Central Iron and Steel Research Institute (China) in Materials science. She joined the department of mechanics at Beijing Jiaotong University in 2002 and now she is a professor in Solid Mechanics. She was a visiting scholar in the University of Hong Kong, Massachusetts Institute of Technology, and Georgia Institute of Technology. Her current research interests are in the deformation behaviors of metals and polymers by atomistic simulation.
Abstract
Recent experimental study demonstrated that double contraction nanotwins (DCTWs). i.e., {101-1}-{101-1} nanotwinned structures, can simultaneously improve the strength and ductility of Mg-Li alloys. In this work, we characterize structural characters associated with double contraction nanotwins and investigate plastic deformation mechanisms of such hierarchical {101-1}-{101-1} nanotwinned structures in Mg by molecular dynamics simulations. The boundaries associated with DCTWs are composed of {101-1} coherent twin boundaries (CTBs), asymmetrical tilt grain boundaries (ASTGBs), and symmetrical tilt grain boundaries (STGBs). The corresponding grain boundary dislocations along ASTGB and STGB are characterized according atomistic simulations and defect theory. Under mechanical loadings, ASTGBs and STGBs act as sources for nucleating and emitting basal and pyramidal dislocations, enhancing the plasticity. Meanwhile, {101-1} CTBs act as strong barriers for dislocation motion, strengthening the material.
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  • Nov. 5 (Tue.) 17:00~17:30 Yun Hee Jang
  • Daegu Gyeongbuk Institute of Science and Technology, Korea
  • "Curiosity-Driven Molecular Modeling: Shuttlecock-Shaped Molecular Rectifier’s Asymmetric Electron Transport Coupled with Controlled Molecular Motion"
    Biography and Abstract
Biography
Yun Hee Jang received B.S. in Chemistry (1990) and Ph.D. in Physical Chemistry (1995) from Seoul National University (Korea). After working as postdoctoral scholar at Caltech (USA) and as assistant/associate professor of Materials Science & Engineering at GIST (Korea), she has been professor of Energy Science & Engineering, DGIST (Korea) since 2016. Her group, Curious Minds’ Molecular Modeling (CMMM) Laboratory, employs multiscale methods of computational chemistry (quantum mechanics, molecular dynamics, and Monte Carlo) for molecular-level understanding of structures and functions of various materials at interfaces.
Abstract
A Langmuir-Blodgett (LB) monolayer of a shuttlecock-shaped asymmetric fullerene derivative on Au(111) exhibits a rectification (i.e., asymmetric I-V curve or ON/OFF switching) under a conducting AFM measurement. Its rectification ratio at high voltage (1.5 V) is an order-of-magnitude higher than at low voltage (1.0 V). Using a multiscale molecular modeling combining molecular dynamics (MD), density functional theory (DFT), and non-equilibrium Green’s-function formalism (NEGF), we identify the origin of such rectification behavior of this plausible molecular diode, which can be a low-energy-consumption alternative of conventional Si-based diodes. The low rectification ratio exhibited at 1.0 V is reproduced by DFT-NEGF calculations on a metal-molecule-metal sandwich-type model device at a standing-up molecular orientation expected for a fresh LB monolayer. It originates from the slight asymmetry in molecular structure, which creates a non-negligible dipole moment. It is, however, insufficient to explain higher rectification ratios observed in experiments performed at 1.5 V. MD simulations of its self-assembled monolayer on Au(111) show that its molecular orientation can switch between standing-up and lying-on-the-side configurations in order to align its molecular dipole moment with the direction of the applied electric field. Indeed, DFT-NEGF calculations considering such field-induced reorientation yield an order-of-magnitude higher rectification ratio, explaining the experimental observation.
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  • Nov. 4 (Mon.) 10:40~11:10 Jianwen Jiang
  • National University of Singapore, Singapore
  • "Computational Membrane Separations"
    Biography and Abstract
Biography
His research expertise is computational materials modeling and statistical thermodynamics, currently focused on nanoporous and membrane materials for energy, environmental, and pharmaceutical applications (e.g. carbon capture, water desalination and drug delivery). He has published over 200 technical manuscripts, as well as over 10 invited reviews and book chapters. He is a Fellow of the Royal Society of Chemistry (RSC), on the editorial boards of Scientific Reports, Frontier in Materials, Advances in Materials Research, and Colloid and Interface Science Communications, among others. He received the IES Prestigious Engineering Achievement Award from the Institution of Engineers, Singapore.
Abstract
Membranes are commonly utilized in chemical separations, and the fundamental understanding of membrane properties and separation processes is indispensable. With ever increasing computational power and resources, computations have become an indispensable tool in membrane science and engineering.

In this presentation, recent computational studies will be discussed for a wide variety of separations by both crystalline and amorphous membranes. For the crystalline membranes, the focus is on metal-organic frameworks (MOFs), which provide a wealth of opportunities for engineering new membrane materials and have been considered as versatile candidates for many important applications. I will discuss the current status of computational studies for biofuel pervaporation, CO2 capture, water desalination, etc. in MOF membranes. For the amorphous membranes, polymeric membranes will be discussed for gas and liquid separations, particularly the recently emerging organic solvent nanofiltration (OSN) through microporous polymer membranes.

The presentation will demonstrate that computations at an atomic/molecular level can secure the quantitative interpretation of experimental observations, provide microscopic insights from the bottom-up, and facilitate the development of new membranes.
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  • Nov. 5 (Tue.) 10:40~11:10 Byeong-Joo Lee
  • Pohang University of Science and Technology, Korea
  • "Computational Materials and Process Design of Advanced Alloys"
    Biography and Abstract
Biography
Byeong-Joo Lee received his BS, MS and Ph.D (1989) degrees in Materials Science and Engineering from Seoul National University. He has been a principal researcher at Korea Research Institute of Standards and Science until 2002 and joined Pohang University of Science and Technology (POSTECH) in 2002.
He is known with the development of thermodynamic database TCFE2000 and its upgraded versions for computational thermodynamics and with the development of the second nearest-neighbor MEAM and 2NNMEAM+Qeq interatomic potential formalisms for atomistic simulations on metallic, semiconducting and oxide systems.
He is an associate editor of the journal CALPHAD (1998~), a fellow of the Korean Academy of Science and Technology (2017~) and the National Academy of Engineering of Korea (2019~).
Abstract
Computational approaches such as first-principles calculations, atomistic simulations, phase field simulations, computational thermodynamics and finite element method simulations are widely used in metals and materials community. Even with their successful applications to understand materials phenomena and design new materials or processes, the gap between computational approaches and experimental data, which originates from the difference between computational and experimental conditions, limits the wider application of computational approaches. In this talk, some successful examples of computational materials and process design will be outlined, focusing on how to utilize the results of computational approaches. It will be emphasized that to notice the governing mechanism in materials phenomena from the effects of individual experimental variables on simulation results is more important rather than to make an effort to obtain a good agreement between simulations and experiments in phenomenological issues. It will be also emphasized that research efforts to extend the applicability of the computational approaches are continuously required.
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  • Nov. 5 (Tue.) 15:10~15:40 Aiichiro Nakano
  • University of Southern California, USA
  • "Toward exascale quantum-dynamical simulations and learning of nanosystems"
    Biography and Abstract
Biography
Aiichiro Nakano is a professor of Computer Science with joint appointments in Physics & Astronomy, Chemical Engineering & Materials Science, Biological Sciences, and the Collaboratory for Advanced Computing and Simulations at the University of Southern California (USC). He received a Ph.D. in physics from the University of Tokyo, Japan, in 1989. He has authored 401 refereed articles, including 262 journal papers, in the areas of scalable scientific algorithms, high-end parallel supercomputing, massive data visualization and analysis, and computational materials science.
Abstract
We have developed a divide-conquer-recombine algorithmic framework to make nonadiabatic quantum molecular dynamics (NAQMD) and reactive molecular dynamics (RMD) simulations scalable on the Nation’s first exascale supercomputer called Aurora A21. I will describe scalable NAQMD and RMD simulations, supported by machine-learning approaches, to study the growth, optical and mechanical control of material phases, and emergent physical properties and their optimization in various nanosystems including atomically-thin layered materials. This work was supported by U.S. Department of Energy and Office of Naval Research.
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Biological and Bio-inspired Systems

  • Nov. 5 (Tue.) 10:40~11:10 Yuan-Chung Cheng
  • National Taiwan University, Taiwan
  • "Molecular Modeling of Light Harvesting in Large Photosynthetic Pigment-Protein Complexes"
    Biography and Abstract
Biography
Prof. Yuan-Chung Cheng received his Ph.D. from the Department of Chemistry in the Massachusetts Institute of Technology in 2006, and he then worked as a Postdoctoral Research Associate from 2006 to 2009 in the University of California, Berkeley. In 2009, he returned to the Department of Chemistry in the National Taiwan University as an Assistant Professor, and he was promoted to the rank of Associated Professor in 2015. Prof. Cheng’s research interests covers a broad range in Theoretical Physical Chemistry, with an emphasis on the development of theoretical tools for quantum dynamics in condensed-phase molecular systems.
Abstract
Sophisticated pigment-protein complexes (PPCs) enable photosynthetic organisms to achieve remarkable near-unity quantum efficiency in the light reactions of photosynthesis, and a clear understanding of the molecular architecture and mechanisms responsible for the high efficiency could have significant implications in artificial photosynthesis and light harvesting. In this talk, I will present our theoretical investigations into excitation energy transfer (EET) in several photosynthetic systems. In particular, I will illustrate a first-principle approach that combines quantum chemistry calculations and molecular dynamics (MD) simulations to model chromophore-protein interactions in chlorophyll-binding PPCs. The theoretical framework, when carefully parameterized, is able to yield excellent agreement with experimental multidimensional electronic spectra in the water-soluble chlorophyll binding protein, leading to unprecedented details of energy relaxation processes in the PPC. We further apply the approach to investigate energy transfer dynamics in the dimeric Photosystem II core complex to reveal energy transfer pathways and the functional role of the dimeric structure in the complex . More importantly, the results allowed us to identify several key elements that play important roles in the speedup of energy trapping in photosynthesis. In summary, we have developed an effective approach that combines quantum chemical calculations, MD simulations, and the quantum dynamic method for describing spectra and energy transfer dynamics in PPCs. This framework should be applicable to general organic molecular aggregates and useful for the design of efficient light-harvesting materials.
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  • Nov. 4 (Mon.) 10:40~11:10 Sihyun Ham
  • Sookmyung Women’s University, Korea
  • "Fluctuating Thermodynamics for Biomolecular Interactions"
    Biography and Abstract
Biography
Professor Ham received her B.S. degree in Chemistry summa cum laude from Sookmyung Women's University and her Ph.D. in Chemistry from Texas Tech University. After postdoctoral fellow at the University of Washington, Seattle, she joined the faculty at the Sookmyung Women’s University in the Department of Chemistry in 2003. She serves as the Director of the Research Institute of National Science and the Director of the NanoBio Molecular Network Research Center at her University. She is also leading the BK21+ Chemistry team at the University. She is a Senior Fellow of the Canadian Institute For Advanced Research (CIFAR) program in Molecular Architecture of Life and a Fellow of the Royal Society of Chemistry, Royal Society of Chemistry. Her research aim is to understand why and how a normal cell becomes a disease cell at a molecular-level by using theoretical and computational methods. She has received several awards including the Asian Young Scientist Award from the Japanese Chemical Society (2009), Female Scientists of the Year Award (2014), and Scientist of the Month Award (2016) from the Korean Government.
Abstract
Why and how a normal cell becomes a disease cell? How can we characterize fluctuating biomolecular processes at a molecular level? To uncover the mechanisms and driving factors of those fluctuating processes, dynamic extension of thermodynamics is necessary since biomolecular processes are largely under thermodynamic control. The fluctuating thermodynamics technology developed in my group offers a practical means for the thermodynamic characterization of conformational dynamics in biomolecules. The use of fluctuating thermodynamics has the potential to provide a comprehensive picture of fluctuating phenomena in diverse biological processes. Through the application of fluctuating thermodynamics, we provide a thermodynamic perspective on the misfolding and aggregation of the various proteins associated with human diseases. Here I will present the detailed concepts and applications of the fluctuating thermodynamics technology for elucidating biological processes. These tools and new paradigm provide a unified view on how biomolecules operate and are applied to design a new function of specific interest in cellular network.
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  • Nov. 5 (Tue.) 17:00~17:30 Shigehiko Hayashi
  • Kyoto University, Japan
  • "Atomistically deciphering functional processes of transporter and redox proteins with molecular simulations"
    Biography and Abstract
Biography
Shigehiko Hayashi is a Professor in Department of Chemistry at Graduate School of Science, Kyoto University since 2013. He received his Ph. D. at Kyoto University in 1997. He did his postdoctoral works at Nagoya University, University of Illinois at Urbana-Champaign, and Kyoto University during 1998-2005. He was an Associate Professor at Kyoto University during 2005-2013.
Abstract
Functional processes of transporter and redox proteins are often fulfilled by dynamic and global molecular conformational changes of complex protein systems which correlate with local molecular events at ligand binding sites and reaction centers. Hence the multi-scale functional coupling of local chemical events with protein global molecular dynamics need to be revealed for understanding of molecular nature of protein functions. In this talk, I will present our recent studies on photo-activation processes of a channelrhodopsin photo-sensitive ion transporter and redox processes of cytochrome c and photosystem II by a hybrid QM/MM free energy geometry optimization technique, which allows one to optimize electronic wave function and molecular geometry of a reaction center at the ab initio quantum chemistry level of theory on a free energy surface constructed with statistically extensive conformational ensemble of the protein environment obtained by long-time MD simulations. I will also present an atomistic MD study of alternating access of mitochondria ADP/ATP transporter with a linear response path following method which is a biasing MD technique accelerating global protein conformational changes coupled to local ligand binding events.
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  • Nov. 4 (Mon.) 13:30~14:00 Xuhui Huang
  • Hong Kong University of Science and Technology, Hongkong
  • "Constructing Markov State Models to Elucidate the Functional Conformational Changes of Complex Biomolecules"
    Biography and Abstract
Biography
Xuhui Huang obtained his Ph.D. degree from Columbia University in 2006 with Prof. Bruce Berne. He did his postdoc research at Stanford University with Profs. Michael Levitt (Nobel Laureate in Chemistry) and Vijay Pande, He joined HKUST at an assistant professor in 2010, and received an early promotion to the tenured Associated Professor at Jan 2015, and between 2017 to 2019, he was the endowed Padma Harilela Associate Professor of Science at HKUST. At July 2019, he was promoted to full professor. His research is focused on developing and applying statistical mechanics-based algorithms to model conformational dynamics of complex biological systems. He has received a series of awards including the American Chemical Society OpenEye Outstanding Junior Faculty Award (2014); School Research Award, HKUST School of Science (2013), Hong Kong Research Grant Council Early Career Award (2013); and American Chemical Society CCG Excellence Award (2006). In 2017, he was selected as a founding member and currently serves as Vice President of Young Academy of Sciences of Hong Kong.
Abstract
Simulating biologically relevant timescales at atomic resolution is a challenging task since typical atomistic simulations are at least two orders of magnitude shorter. Markov State Models (MSMs), a kinetic network model, built from molecular dynamics (MD) simulations provide one means of overcoming this gap without sacrificing atomic resolution by extracting long time dynamics from short MD simulations through the coarse graining on the phase space and time. In this talk, I will demonstrate the power of kinetic network models by applying it to simulate the complex conformational changes, that occurs at tens to hundreds of microsecond timescales for a large RNA Polymerase II complex containing nearly half million atoms. Furthermore, I will introduce a new efficient dynamic clustering algorithm for the automatic construction of MSMs for multi-body systems. We have successfully applied this new algorithm to model the protein-ligand recognition and self-assembly of co-polymers. Finally, I will introduce a new algorithm using the projection operator approach to identify optimal kinetic lumping and recover slowest conformational dynamics of complex systems.
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  • Nov. 5 (Tue.) 15:10~15:40 Wan-Zhen Liang
  • Xiamen University, China
  • "Excited-state Properties of Molecules in Condensed Phases: Implementation and Applications of TDDFT/MM Schemes"
    Biography and Abstract
Biography
2001, PhD, Department of Chemistry, The University of Hong Kong
2001-2003, Postdo., Department of Chemistry, University of California at Berkeley
2003-2011, Professor, Department of Chemical Physics, University of Science & Technology of China
2012-, Professor, Department of Chemistry, Xiamen University, China
Abstract
The time-dependent density functional theory (TDDFT) has become the most popular methods to calculate the excitation energies, describe the excited-state properties and perform the geometrical optimization of medium-sized molecules due to the implementation of analytic energy gradient and Hessian of the excited states in many electronic structure software packages. To describe the molecules in condensed phase, one usually adopts the computationally efficient Quantum Mechanics/Molecular Mechanics hybrid scheme. In this talk I will show you our works on extending the analytic energy derivative approaches to account for the molecular condensed environments by coupling TDDFT with polarizable or non-polarizable molecular mechanics, and show you our applications on calculating the one and two-photon vibronic spectra and exploring the excited-state dynamics of fluorescent proteins.
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  • Nov. 6 (Wed.) 10:40~11:10 Jaeyoung Sung
  • Chung-Ang University, Korea
  • "Chemical Dynamics in Living Cells"
    Biography and Abstract
Biography
Director, National Creative Research Initiative Center for Chemical Dynamics in Living Cells
Review Board Member, National Research Foundation of Korea
Winner of The 1st Kook-Joe Shin Academic Excellent Prize from KCS
Visiting Scholar, Massachusetts Institute of Technology
Postdoctoral Associate, Massachusetts Institute of Technology
Distinguished Ph.D. Dissertation Award, Seoul National University
Ph. D., M.S., B.S., Department of Chemistry, Seoul National University
Abstract
We introduce a new type of kinetic network model and kinetic theory for biological networks, enabling an accurate quantitative description of chemical dynamics of complex biological networks. An advantage of this approach is its applicability to biological networks producing biomolecules with arbitrary lifetime distributions to which the classical chemical kinetics, chemical master equation, and chemical Langevin equation are not directly applicable. Another advantage of our approach is that it enables quantitative investigation into biological networks composed of complicated chemical processes, e.g., multi-step or multi-channel reactions whose rates have both intrinsic and extrinsic fluctuation. We demonstrate the advantages of our approach by providing an unprecedented quantitative explanation of non-classical chemical dynamics observed in various biological systems including single enzymes, in vivo motor-protein multiplexes, and cell systems with various gene networks. Time-permitting, we will also discuss how cell signal response is related to the structure and dynamics of a signaling network and the lifetime distribution of biomolecules constituting the network.
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