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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 ~ July 31, 2019
Acceptance Notification
August 14, 2019
Early Registration
(TBA)
Final Program Announcement
October 2, 2019
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Plenary Speaker

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


  • William A. Goddard III
  • California Institute of Technology (Caltech), USA
  • "New Strategies for Multiparadigm Simulations of Materials with Applications Ranging from Anomalous Properties of Water, Electrocatalysis on Complex Irregular Electrodes, Ammonia Synthesis, Ductility of Materials, and the Mechanism of G-Protein Activation by G-Protein Coupled Receptors"
    Biography and Abstract
Biography
Goddard received his BS Engineering from UCLA and his PhD in Engineering Science with a minor in Physics in Oct. 1964. He has been on the Caltech faculty since Nov. 1964 where he is the Charles and Mary Ferkel Professor of Chemistry, Materials Science, and Applied Physics and Director of the Materials and Process Simulation Center (MSC).

Goddard has been a pioneer in developing methods for quantum mechanics (QM), force fields (FF), reactive dynamics (ReaxFF RD), electron dynamics (eFF), molecular dynamics (MD), and Monte Carlo (MC) predictions on chemical, catalytic, and biochemical materials systems and is actively involved in applying these methods to ceramics, semiconductors, superconductors, thermoelectrics, metal alloys, polymers, proteins, nuclei acids, Pharma ligands, nanotechnology, and energetic materials. Current foci include developing new electrocatalysts for water splitting (producing H2 and O2 from water), CO2 reduction to organics, on the oxygen reduction reaction and development pf powerful methods for predicting the structures of membrane bound proteins and the binding sites of agonists and antagonists.

He was elected to the National Academy of Science (1984, age 47) and to the International Academy of Quantum Molecular Science (1986). He is a Fellow of the American Physical Society (1988), the American Association for the Advancement of Science (1990), the Royal Society Chemistry (2008), and the American Academy of Arts and Sciences (2010). He was Awarded Honoris Causa Philosophia Doctorem, Chemistry, Uppsala U., Sweden, January 2004. He was the winner of the American Chemical Society Award for Computers in Chemistry (1988), the Feynman Prize for Nanotechnology Theory (1999), the Richard Chase Tolman Prize from the Southern California Section ACS (2000), the American Chemical Society Award for Theoretical Chemistry (2007), the NASA Space Sciences Award for Space Shuttle Sensor (2009), the NASA Space Sciences Award for polymer films (2012), and the Distinguished Scientific Achievement Catalysis Award from the 7th World Congress Oxidation Catalysis (2013). He was named ISI Highly Cited Chemist for 1981-2001, 2014, 2015, 2016 and the Clarivate Analytics Highly Cited Researcher for 2018.
Abstract
Modern Density Function Methods can lead to accuracies of 0.05 eV in free energies reaction barriers for problems like electrocatalysis (Oxygen reduction reaction and CO2 reduction reaction) and Haber-Bosch (HB) synthesis of ammonia (NH3) from hydrogen and nitrogen, but these applications are limited to ~300 atoms and 20 ps of molecular dynamics. But the optimum catalysts often involve complex surfaces that may require millions of atoms for a realistic description. To overcome these limitations, we have previously developed the ReaxFF reactive force field, which has been successful for many applications, but the level of accuracy is not generally better than 0.25 eV. We report here the new RexPoN generation of reactive force field we aims at higher accuracy than DFT, describing bond breaking at the accuracy of ab initio CCSDT. With RexPoN we have explained the anomalous properties of water (supercooled critical point, 20 nm angular correlations). For catalysis the optimum catalysts often involve complex surfaces with 10,000 surface sites for a simple 10-20 nm nanoparticle. Thus even though ReaxFF may generate a realistic structure, there are too many sites to do the reaction mechanism at every site to find the best ones. Here we have developed machine learning strategies that can be trained to predict reliable the transition state energies with a few hundred calculations and then used to predict the energies for all 10,000 sites. This allows us to predict the target structure for optimum performance.

The free energy metadynamics methods now allow the reaction free energies to be obtained for very complex systems. We will demonstrate this with applications to determine the mechanism by which a G-Protein coupled receptors and an agonist activate a G-Protein.
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  • Kersti Hermansson
  • Uppsala University, Sweden
  • (TBA)
    Biography and Abstract
[ Biography and Abstract ] will be announced
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  • George Em Karniadakis
  • Division of Applied Mathematics, Brown University, USA
  • "Dissipative Particle Dynamics: Theory, Algorithms and Applications"
    Biography and Abstract
Biography

Karniadakis received his S.M. (1984) and Ph.D. (1987) from Massachusetts Institute of Technology . He was appointed Lecturer in the Department of Mechanical Engineering at MIT in 1987 and subsequently he joined the Center for Turbulence Research at Stanford / Nasa Ames . He joined Princeton University as Assistant Professor in the Department of Mechanical and Aerospace Engineering and as Associate Faculty in the Program of Applied and Computational Mathematics. He was a Visiting Professor at Caltech (1993) in the Aeronautics Department . He joined Brown University as Associate Professor of Applied Mathematics in the Center for Fluid Mechanics on January 1, 1994. He became a full professor on July 1, 1996. He has been a Visiting Professor and Senior Lecturer of Ocean/Mechanical Engineering at MIT since September 1, 2000. He was Visiting Professor at Peking University (Fall 2007 & 2013). He is a Fellow of the Society for Industrial and Applied Mathematics (SIAM, 2010-), Fellow of the American Physical Society (APS, 2004-), Fellow of the American Society of Mechanical Engineers (ASME, 2003-) and Associate Fellow of the American Institute of Aeronautics and Astronautics (AIAA, 2006-). He received the Ralf E Kleinman award from SIAM (2015), the (inaugural) J. Tinsley Oden Medal (2013), and the CFD award (2007) by the US Association in Computational Mechanics. His h-index is 90 and he has been cited over 42,000 times.

Abstract

The Dissipative Particle Dynamics method will be presented from the perspective of Mori-Zwanzig formulation for Markovian and non-Markovian systems. Examples will include coarse graining of polymers and applications to blood cells in malaria and sickle cell anemia as well as multiscale simulations using machine learning tools.

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  • Yuko Okamoto
  • Nagoya University, Japan
  • "Enhanced sampling techniques for classical and quantum molecular simulations"
    Biography and Abstract
Biography

Yuko Okamoto received his B.S.-M.S. in physics from Brown University in 1979 and Ph.D. in physics from Cornell University in 1984. After his postdoctoral work at Virginia Polytechnic Institute and State University, he worked as an assistant professor (later, an associate professor) at Nara Women’s University from 1986 to 1995. He moved to the Institute for Molecular Science in 1995 as an associate professor and then to the current position of professor of biophysics at Nagoya University in 2005. His research has focused on the development of enhanced sampling methods in molecular simulations, for example, replica-exchange molecular dynamics and other generalized-ensemble algorithms, and their applications to computational physics/chemistry/biology problems such as protein folding, ligand binding, and prediction of three-dimensional structures of molecules.

Abstract

Conventional Monte Carlo and molecular dynamics simulations are greatly hampered by the multiple-minima problem, where the simulations tend to get trapped in some of astronomically large number of local-minimum energy states. In order to overcome this difficulty, we have been advocating the uses of generalized-ensemble algorithms which are based on non-Boltzmann weight factors. With these algorithms we can explore a wide range of the conformational space. The advantage of generalized-ensemble algorithms such as replica-exchange method (or, parallel tempering) lies in the fact that from only one simulation run, one can obtain various thermodynamic quantities as functions of temperature and other physical parameters such as pressure, etc. by the reweighting techniques. In this talk, I will present the latest results of our applications of generalized-ensemble simulations to complex systems.

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  • Matthias Scheffler
  • Fritz-Haber-Institut der Max-Planck-Gesellschaft, Germary
  • "Big-Data-Driven Materials Science and its FAIR Data Infrastructure"
    Biography and Abstract
Biography
Matthias Scheffler is a Director at the FHI. He is known for his pioneering work linking density-functional theory with thermodynamics and statistical mechanics. Currently he leads the pan-European NOMAD project, which is a European Centre of Excellence that provides a central data repository for materials modelling as well as pioneering in the field of big data analytics for the advancement of materials design and engineering. He is honorary professor at all three universities of Berlin and “distinguished visiting professor for materials science and engineering” at UC Santa Barbara.
Abstract
This talk addresses the forth paradigm of materials research ˗ big-data driven materials science. Its concepts and state-of-the-art are described, and its challenges and chances are addressed. For furthering the field, Open Data and an all-embracing sharing, an efficient data infrastructure, and the rich ecosystem of computer codes used in the community are of critical importance. For shaping this forth paradigm and contributing to the development or discovery of improved and novel materials, data must be what is now called FAIR ˗ Findable, Accessible, Interoperable and Re-purposable. This sets the stage for advances of methods from artificial intelligence that operate on large data sets to find trends and patterns that cannot be obtained from individual calculations and not even from high-throughput studies. Recent progress is reviewed and demonstrated, and we conclude with a forward-looking perspective, addressing important not yet solved challenges.
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  • Berend Smit
  • Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland
  • "The Nanoporous Materials Genome in Action"
    Biography and Abstract
Biography

Berend Smit received an MSc in Chemical Engineering in 1987 and an MSc in Physics both from the Technical University in Delft (the Netherlands). He received in 1990 cum laude PhD in Chemistry from Utrecht University (the Netherlands). He was a (senior) Research Physicists at Shell Research from 1988-1997, Professor of Computational Chemistry at the University of Amsterdam (the Netherlands) 1997-2007. In 2004 Berend Smit was elected Director of the European Center of Atomic and Molecular Computations (CECAM) Lyon France. Since 2007 he is Professor of Chemical Engineering and Chemistry at U.C. Berkeley and Faculty Chemist at Materials Sciences Division, Lawrence Berkeley National Laboratory. Since 2014 he is director of the laboratory for molecular simulations (LSMO) at EPFL.

Berend Smit's research focuses on the application and development of novel molecular simulation techniques, with emphasis on energy related applications. Together with Daan Frenkel he wrote the textbook Understanding Molecular Simulations and together with Jeff Reimer, Curt Oldenburg, and Ian Bourg the textbook Introduction to Carbon Capture and Sequestration.

Abstract

The attractive feature of Metal Organic Frameworks (MOFs) is that by changing the ligand and/or metal, they can be chemically tuned to perform optimally for a given application. This unique chemical tunability allows us to tailor-make materials that are optimal for a given application. The promise of finding just the right material seems remote however: because of practical limitations we can only ever synthesize, characterize, and test a tiny fraction of all possible materials. To take full advantage of this development, therefore, we need to develop alternative techniques, collectively referred to as Materials Genomics, to rapidly screen large numbers of materials and obtain fundamental insights into the chemical nature of the ideal material for a given application.

These computational materials genomics initiatives have been so successful that we have created a new problem: what to do with so much data? In this presentation we will discuss different computational strategies to deal with a large amount of data. We illustrate on the use of these strategies by addressing the following questions: How the find the best material for a given application? How to find materials with similar pore shape? How to design a material that optimally binds CO2? And, what can we learn from failed experiments?

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  • Huai Sun
  • Shanghai Jiao Tong University, China
  • (TBA)
    Biography and Abstract
[ Biography and Abstract ] will be announced
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  • Yoshitaka Tanimura
  • Kyoto University, Japan
  • (TBA)
    Biography and Abstract
[ Biography and Abstract ] will be announced
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  • Gregory Voth
  • University of Chicago, USA
  • "Recent Advances in Systematic Coarse-grained Simulation"
    Biography and Abstract
Biography

Gregory A. Voth is the Haig P. Papazian Distinguished Professor of Chemistry at The University of Chicago. He is also a Professor of the James Franck Institute and the Institute for Biophysical Dynamics. He received a Ph.D. in Theoretical Chemistry from the California Institute of Technology in 1987 and was an IBM Postdoctoral Fellow at the University of California, Berkeley from 1987-89. Voth is a Fellow of the American Chemical Society, American Physical Society, The Biophysical Society, and the American Association for the Advancement of Science. He has received a number of awards and other forms of recognition for his work, including most recently the Joel Henry Hildebrand National American Chemical Society Award in the Theoretical and Experimental Chemistry of Liquids, the American Chemical Society Division of Physical Chemistry Award in Theoretical Chemistry, and Election to the International Academy of Quantum Molecular Science. He has mentored more than 175 postdoctoral fellows and graduate students. Professor Voth is a leader in the development and application of theoretical and computational methods to study problems involving the structure and dynamics of complex condense phase systems, including proteins, membranes, liquids, and materials. He has pioneered a method known as “multiscale coarse-graining” in which the resolution of the molecular-scale entities is reduced into simpler structures, but key information on their interactions is accurately retained (or renormalized) so the resulting computer simulation can accurately and efficiently predict the properties of large assemblies of complex molecules such as lipids and proteins. This method is multiscale, meaning it describes complex condensed phase and biomolecular systems from the molecular scale to the mesoscale and ultimately to the macroscopic scale. Professor Voth’s other research interests include the study of charge transport (protons and electrons) in water and biomolecules – a fundamental process in living organisms and other systems that has been poorly understood because of its complexity. He also studies the exotic behavior of room-temperature ionic liquids and other complex materials such a nanoparticle self-assembly, polymer electrolyte membranes for fuel cells, and electrode-electrolyte interfaces in energy storage devices. In the earlier part of his career, Professor Voth extensively developed and applied new methods to study quantum and electron transfer dynamics in condensed phase systems-much of this work was based on the Feynman path integral description of quantum mechanics.

Abstract

Recent advances in theoretical and computational methodology will be presented that are designed to simulate complex (biomolecular and other soft matter) systems across multiple length and time scales. The approach provides a systematic connection between all-atom molecular dynamics, coarse-grained modeling, and mesoscopic phenomena. At the heart of these concepts are methods for deriving coarse-grained models from molecular structures and their underlying atomic-scale interactions. This particular aspect of the work has strong connections to the procedure of renormalization, but in the context of CG models it is developed and implemented for more heterogeneous systems. An important new component of our work has been the concept of the “ultra-coarse-grained” (UCG) model and its associated computational implementation. In the UCG approach, the CG sites or “beads” can have internal states, much like quantum mechanical states. These internal states help to self-consistently quantify a more complicated set of possible interactions within and between the CG sites, while still maintaining a high degree of coarse-graining in the modeling. The presence of the CG site internal states greatly expands the possible range of systems amenable to accurate CG modeling, including quite heterogeneous systems such as aggregation of hydrophobes in solution, liquid-vapor and liquid-solid interfaces, and complex self-assembly processes such as occurs for large multi-protein complexes. The development of CG models from the underlying atomistic interactions also presents special challenges for free energy sampling that will be discussed in my talk, such as the utilization of virtual sites to describe semi-explicit solvation effects and other known phenomena.

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