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


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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Plenary Speaker

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

  • Nov. 4 (Mon.) 16:10~16:50 Claudia Draxl
  • Humboldt-Universtat zu Berlin, Germany
  • "Open and Data-driven Science – the Need for a FAIR Data Infrastructure"
    Biography and Abstract
Claudia Draxl is Einstein Professor at the Humboldt-Universität zu Berlin, Germany and Max-Planck Fellow at the Max Planck Graduate Center for Quantum Materials. Her research interests cover theoretical concepts and methodology to get insight into a variety of materials and their properties. She is developer of the all-electron full-potential package exciting, implementing density-functional theory (DFT) and methods beyond, with a focus on excitations. A recently created package is the cluster-expansion code CELL. Actual research projects concern organic/inorganic hybrid structures, wide-gap oxides, thermoelectricity, solar-cell materials, film growth, and more. She is one of the founders of the Novel Materials Discovery (NOMAD) Repository, an open-access library of materials, and the non-profit association FAIR-DI (FAIR Data Infrastructure for Physics, Chemistry, Materials Science, and Astronomy). Based on this, her data-driven research aims at finding structure in Big Data of materials science.
Knowledge and understanding of materials is based on their characterization by a variety of properties and functions. Surprisingly though, for only a very small number of materials this information exists. Within the last decade, computational high-throughput studies are aiming at filling this gap, thereby also creating new hypothetical structures. Making this data available, opens avenues for data-driven research in terms of re-purposing – using materials for a different purpose than intended by the original work, detecting candidate materials for a given application, and finding descriptors by approaches of artificial intelligence. Prerequisite for all this is a FAIR (findable, accessible, interoperable, reusable) data infrastructure. I will discuss the approach of the NOMAD Laboratory (https://nomad-coe.eu), offering services like free upload to the NOMAD Repository and NOMAD Archive, the NOMAD Encyclopedia, and the NOMAD Analytics Toolkit. I will particularly address our concepts and first steps towards extension of this open-science platform in terms of experimental data and synthesis. Here, for instance volume and velocity are big issues for many measurement techniques, while large uncertainties may come from (often incompletely known) sample quality, instrumental resolution, or measurement conditions. These challenges are tackled within the non-profit association FAIR-DI (https://fairdi.eu).
  • Nov. 4 (Mon.) 09:00~09:40 William A. Goddard III
  • California Institute of Technology (Caltech), USA
  • "New Quantum Mechanics based methods for Multiscale simulations of Electrocatalysis reaction mechanisms and analysis of active sites using machine learning"
    Biography and Abstract
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.

Advances in theory and methods of quantum mechanics are making it practical for first principles (de novo) predictions of the mechanisms of complex electrocatalytic reactions. We will highlight some recent advances in such methodologies including:

• Grand canonical QM calculations of electrochemical catalysis at constant potential (instead of constant numbers of electrons)
• QM Metadynamics calculations of free energies of electrocatalysis at operational temperature and potential
• New generation reactive force fields including polarization and universal nonbond interactions
• Machine learning to identify active sites on NP and NW
• Hybrid QM-ReaxPQ dual embedding for battery and electrocatalysis applications We will illustrate these methods with recent applications to the reaction mechanisms for electrocatalysis selected from:
• The CO2 reduction reaction on Au ,Ag, Cu
• application of reactive force fields and machine learning to electrocatalysis on nanoparticles (NP) and nanowires (NW)
• QM derived Operando vibrational frequencies at the Electrode-Electrode Interface, identification of species identified via operando Raman studies
• The oxygen evolution on mixedmetal oxides
• the oxygen reduction reaction (ORR) on Pt (111) vs. dealloyed NiPt nanowires
• The hydrogen evolution reaction for (XNH3)PbI3 photocatalysts and for MoS2

  • Nov. 6 (Wed.) 09:40~10:20 Kersti Hermansson
  • Uppsala University, Sweden
  • "Simulation of hydrated and hydroxylated surfaces"
    Biography and Abstract
Biography Hermansson's expertise lies in e-science for materials chemistry, namely the development of multiscale modelling strategies to bring chemical modelling closer to the complex dynamical systems of the real world. Various types of advanced, and more approximate, electronic structure methods as well as force-field approaches and composite schemes are in focus. Her main applications are dynamical phenomena on metal oxide surfaces, redox chemistry at liquid/solid interfaces (mostly water) and computational vibrational spectroscopy. She is Professor of Inorganic Chemistry at the Ångström Laboratory of Uppsala University, a member of the Royal Swedish Academy of Sciences (KVA), coordinator of the model development activities of the European Materials Modelling Council (www.emmc.info) under the European Commission, and coordinator of CECAM-Sweden.

The H2O molecule, the OH– ion and their H-bonding abilities (or not!) can seriously modify the properties of functional surfaces, whose hydroxylation & hydration deserve special attention in materials chemistry and physics. We have analysed the water structure, dynamics and reactivity at water/metal oxide interfaces using a range of computational methods: DFT methods, physics-based force-fields, or force-fields derived from machine learning. I will allso inform about the European Materials Modelling Council and its efforts to promote the trust in modelling results.

  • Nov. 5 (Tue.) 09:40~10:20 George Em Karniadakis
  • Division of Applied Mathematics, Brown University, USA
  • "Dissipative Particle Dynamics: Theory, Algorithms and Applications"
    Biography and Abstract

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.


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.

  • Nov. 5 (Tue.) 13:30~14:10 Kwang Soo Kim
  • Ulsan National Institute of Science and Technology, Korea
  • "Molecular/ionic and/or e-photon/phonon interactions driven structural organization, phases, and collective properties of water and materials"
    Biography and Abstract
- Ulsan National Institute of Science and Technology (UNIST),
- Director of Center for Superfunctional Materials,
- Distinguished Professor of Chemistry
- Pohang University of Science and Technology (POSTECH),
- Director of Center for Superfunctional Materials,
- Professor of Chemistry
Many intriguing collective phenomena of liquids and solid materials have hardly been understood. Here I address the interplay between theory and experiment to harness their collective properties. These include molecular assembly from gas to clusters including dimensional change and eventually to liquid and solid bulk materials and vapor/liquid/solid, liquid-liquid/solid, and solid-solid phase transitions due to the environmental changes including temperature, pressure, electromagnetic field and confinement effects. I discuss solvation of electron, proton, hydroxide ion, acids, bases, and electro/photo-catalytic water splitting towards hydrogen/oxygen evolution including proton/hydrioxide transfer and photo-excitation dynamics. For fundamental understanding of molecular assembly and organization, water will be particularly discussed in detail from a single water molecule to cluster formation, mist, moisture, liquid-water, cloud, frost, snow, and ice as well as the confinement effects in 1D and 2D systems. Vapor-liquid phase transitions including spinodals and critical points are investigated from simulation results. Also, controversial liquid-liquid phase transitions and Widom lines in water are discussed. Then, material simulations for better light/energy harvesting and energy storage of perovskite solar cells (Fröhlich polaron picture), hydrogen/oxygen evolution reaction (free energy profile), fuel cells, and Li-batteries (Li diffusion) are addressed. Finally, Fano resonance driven 2D molecular electronic spectroscopy for DNA sequencing and molecular fingerprinting on a graphene nanoribbon in water is also addressed.
  • Nov. 4 (Mon.) 16:50~17:30 Yuko Okamoto
  • Nagoya University, Japan
  • "Enhanced sampling techniques for classical and quantum molecular simulations"
    Biography and Abstract

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.


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.

  • Nov. 4 (Mon.) 09:40~10:20 Matthias Scheffler
  • Fritz-Haber-Institut der Max-Planck-Gesellschaft, Germany
  • "Finding (statistically) exceptional materials by artificial intelligence"
    Biography and Abstract
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.

The true breakthrough in data-driven materials science will depend on appropriate data-analytics methodology [1]. Consider that the number of possible materials is practically infinite, but only about 10 of them may be relevant for a certain purpose. In simple words, in materials science and engineering, we are often looking for “needles in a hay stack”. Fitting or machine-learning all available data with a single, global model means fitting the hay, where one may average away the specialties of the interesting minority -- the needles. I will discuss methods that identify statistically-exceptional subgroups in a large amount of data, and I will demonstrate this for catalytic CO2 activation (turning a greenhouse gas into fuels and useful chemicals). Furthermore, I will discuss how one can estimate the domains of applicability of machine-learning models [2].

[1] C. Draxl and M. Scheffler, Big-Data-Driven Materials Science and its FAIR Data Infrastructure. Plenary Chapter in Handbook of Materials Modeling (eds. S. Yip and W. Andreoni), Springer (2019). https://arxiv.org/ftp/arxiv/papers/1904/1904.05859.pdf
[2] Ch. Sutton, M. Boley, L. M. Ghiringhelli, M. Rupp, J. Vreeken, M. Scheffler, Domains of Applicability of Machine-Learning Models for Novel Materials Discovery, to be published.

  • Nov. 5 (Tue.) 14:10~14:50 Huai Sun
  • Shanghai Jiao Tong University, China
  • "Prediction of Physical Properties of Molecular Liquids using Molecular Simulation and Artificial Intelligence"
    Biography and Abstract

Prof. Huai Sun has received BS and MS from Sichuan University of China, Ph.D. from University of Washington of USA. In his early career he worked in one of the leading software companies and made the well-known COMPASS force field. Since 2003 he accepted the professorship at Shanghai Jiao Tong University, extending his reputation in force field developments and molecular simulations on real-world applications. Quantitative prediction in precision comparable with experimental measurement is his long-lasting goal. He has published more than 120 papers cited over 10,000 times, won several championships in the Industrial Fluid Property Simulation Challenges, and awarded by research funds from Nature Science Foundation of China, National Basic Research Project of China and major chemical corporations. He serves as the vice chairman of Computer in Chemistry Committee of Chinese Chemical Society.


MD simulation can be used to predict physical properties of condensed matters much more efficiently than experimental measurement. However, in practice this approach is limited by the quality of force field and the efficiency of sampling. We extended the TEAM force field1-3 to cover most common organic molecules consisting of H, C, N, O, F, Cl, and Br elements, and predicted basic physical properties of molecular liquids using available simulation techniques. The force field parameters were optimized using ca. 300 molecules and validated using ca. 2500 molecules, these molecules have experimental data for comparison.4 The force field was then used to predict physical properties for ca. 10,000 molecules whose experimental data are not available. The properties predicted include equation of state (PVT) curves, vapor-liquid-equilibrium (VLE) curves, critical points, heat capacities, internal energies, and surface tensions. The high-throughput simulation (HTS) procedure1, 5 generated over 2.5 million data points. Using the large number of simulation data, we are developing a machine learning (ML) model to expand the scope of prediction. The ML model has a feedback mechanism to guide the simulation supplementing data. The coupling between HTS and ML enables an ecosystem which increases its power of prediction with time. In this talk, we will present the results and discuss the challenges of this work.

1. Gong, Z.; Wu, Y.; Wu, L.; Sun, H., Predicting Thermodynamic Properties of Alkanes by High-Throughput Force Field Simulation and Machine Learning. Journal of Chemical Information and Modeling 2018, 58 (12), 2502-2516.
2. Gong, Z.; Sun, H.; Eichinger, B. E., On the Temperature Transferability of Force Field Parameters for Dispersion Interactions. Journal of Chemical Theory and Computation 2018.
3. Jin, Z.; Yang, C.; Cao, F.; Li, F.; Jing, Z.; Chen, L.; Shen, Z.; Xin, L.; Tong, S.; Sun, H., Hierarchical atom type definitions and extensible all-atom force fields. Journal of Computational Chemistry 2016, 37 (7), 653-664. 4. NIST Standard Reference Database 103a: ThermoData Engine Version 10.1. https://www.nist.gov/srd/nist-standard-reference-database-103a. 2019.
5. Cao, F.; Gong, Z.; Wu, Y.; Sun, H., A high-throughput computing procedure for predicting vapor-liquid equilibria of binary mixtures – Using carbon dioxide and n-alkanes as examples. Fluid Phase Equilibria 2017, 452 (Supplement C), 58-68.

  • Nov. 6 (Wed.) 09:00~09:40 Yoshitaka Tanimura
  • Kyoto University, Japan
  • "Simulating nonadiabatic dynamics and electronic-vibrational spectra: The multi-state quantum Fokker-Planck approaches"
    Biography and Abstract

Yoshitaka Tanimura received his Ph.D. under the guidance of Professor Ryogo Kubo at Department of Physics from Keio University in 1989. He was at the University of Illinois and the University of Rochester as a postdoctoral fellow, respectively. After spending nine years as associate professor at the Institute for Molecular Science, he promoted to the professor at Kyoto University in 2003. Research in his group is broadly concerned with the dynamic theory of processes of chemical interest in condensed matter.


Open quantum system refers to a primary quantum system that is embedded in a heat bath represented by an infinite number of sub quantum systems. The key feature of this system is that it describes irreversible dynamics refers to open quantum dynamics through which the primary system evolves toward the thermal equilibrium state at finite temperature. Quantum coherence and its dephasing or relaxation by coupling to an environment plays an important role in nonadiabatic transition, photoexcitation and tunneling processes as well as ultrafast nonlinear spectroscopies. By generalizing the quantum hierarchal equations (QHFPE)[1,2] to a multi-electric states, we can investigate photoisomerization process described by anharmonic potential surfaces with multi-electric state numerically rigorously.[3-7] We developed a computer code that can treat the multi-state system in a phase space with any profile of laser pulse and any strength of non-adiabatic coupling under non-perturbative and non-Markovian system-bath interactions.[4,5] The approach applies to the calculation of linear and nonlinear spectra for a system described by the multistate 1D[3-6] and 2D[7] potential surfaces. We computed nuclear wave packets in Wigner representation and their monitoring by linear absorption, transient absorption, and multi-dimensional electric spectra for various heat-bath parameters to explore photoisomerization dynamics by means of spectroscopic measurements. The movement of excitation and ground state wave packets and their coherence involved in the process were observed as the profiles of positive and negative peaks of 2DEVS spectrum.

[1] Y. Tanimura, J. Chem. Phys. 141 (2014) 044114.
[2] Y. Tanimura, J. Chem. Phys. 142 (2015)144110.
[3] Y. Tanimura and Y. Maruyama, J. Chem. Phys. 107 (1997) 1779.
[4] T. Ikeda and Y. Tanimura, J. Chem. Phys. 146 (2017) 014102.
[5] T. Ikeda and Y. Tanimura, J. Chem. Theor. Comp. 15 (2019) 2517.
[6] T. Ikeda, Y. Tanimura, and A. Dijkstra, J. Chem. Phys. 150 (2019)114103.
[7] T. Ikeda and Y. Tanimura, Chem. Phys. 515 (2018) 203.

  • Nov. 5 (Tue.) 09:00~09:40 Gregory A. Voth
  • University of Chicago, USA
  • "Overcoming the Multiscale Simulation Challenge for Biomolecular Systems"
    Biography and Abstract

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.


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 (CG) 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 also 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 UCG site internal states greatly expands the possible range of systems amenable to accurate CG modeling, i.e., quite heterogeneous systems, including interfacial systems and complex self-assembly processes involving large multi-protein complexes. Applications to experimentally important targets such as cytoskeleton actin filaments and HIV virions will be given.



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