November 29 - December 2, 2021
Boston, Massachusetts
December 6 - 8, 2021 (Virtual)
2021 MRS Fall Meeting

Symposium DS02-Advanced Atomistic Algorithms in Materials Science

The symposium will focus on recent advances in algorithm development of novel atomistic simulation methodologies, both at the level of electronic structure calculations and of empirical-potential-based simulations, and on their applications. The symposium will be centered on methods that aim at addressing size and time-scale limitations of conventional techniques, two problems that often severely limit the scope of atomistic simulations in materials science. As a first-principle method, density functional theory (DFT) has become an invaluable tool for materials modeling. However, with conventional implementation of Kohn-Sham DFT, one is usually limited to systems containing at most several hundred atoms. In recent years, tremendous progress towards relaxing the time and lengthscale limitations has been made in the DFT community. This symposium will address these new exciting advances in DFT such as orbital-free DFT, time-reversible ab-initio molecular dynamics, quasi-continuum DFT, hybrid quantum/classical modeling and machine learning approaches. At the other end of the spectrum, Molecular Dynamics (MD) algorithms based on empirical or semi-empirical potentials allow for greatly extended simulation sizes and times. However, these traditional algorithms are not suitable to study long time phenomena, such as defect diffusion, as they become communication bound. In systems where the dynamics is activated, advanced simulation techniques, such as accelerated molecular dynamics and kinetic Monte Carlo methods, can be leveraged to extend the simulation times up to experimentally relevant scales. These methods often provide invaluable insight into the microstructural evolution of materials. The symposium will focus on recent advances in the development of these accelerated techniques, such as adaptive KMC methods, and on the new physics that can be learned as the timescale horizon is pushed further. Atomistic to continuum approaches and their recent coupling with accelerated MD models, and the phase field crystal method are promising methodologies under development with the potential of extending the time and size scales of the atomistic systems under consideration. Another active field of research is the development of accurate and efficient interatomic potentials based on Machine Learning approaches, which combined with accelerated MD, KMC or Quasi-continuum methods provide powerful tools to study materials behavior.

Topics will include:

  • Addressing size and time limitations in DFT-based methods - Orbital-free DFT, Time-reversible Ab-Initio Molecular Dynamics, Quasi-continuum DFT and hybrid quantum/classical modeling, and Adaptive kinetic Monte Carlo
  • Long-time atomistic simulation methods - Accelerated Molecular Dynamics, Adaptive kinetic Monte Carlo and Acceleration techniques for Kinetic Monte Carlo
  • Atomistic-Continuum Approaches - Linking scales (Quasi-Continuum and related developments), Accelerated methods coupled to quasicontinuum approaches and Phase Field Crystal methods
  • Machine Learning Interatomic Potentials - Development and Combination with accelerated methods

Invited Speakers:

  • Manuel Athenes (Commissariat à l’énergie atomique et aux énergies alternatives, France)
  • Livia Bartók-Pártay (The University of Warwick, United Kingdom)
  • Laurent Beland (Queen's University, USA)
  • Youping Chen (University of Florida, USA)
  • William Curtin (École Polytechnique Fédérale de Lausanne, Switzerland)
  • Claudia Draxl (Humboldt-Universität zu Berlin, Germany)
  • Kristen Fichthorn (The Pennsylvaia State University, USA)
  • Vikram Gavini (University of Michigan, USA)
  • Hannes Jonsson (University of Iceland, Iceland)
  • Steven Kenny (Loughborough University, United Kingdom)
  • James Kermode (The University of Warwick, United Kingdom)
  • Tony Lelievre (École des Ponts ParisTech, France)
  • Ju Li (Massachusetts Institute of Technology, USA)
  • Laura J.S. Lopez (Los Alamos National Laboratory, USA)
  • Gang Lu (California State University, Northridge, USA)
  • Mitchell Luskin (University of Minnesota, USA)
  • Manon Michel (Clermont Auvergne University, France)
  • Yuri Mishin (George Mason University, USA)
  • Marco Nardelli (University of North Texas, USA)
  • Anders Niklasson (Los Alamos National Laboratory, USA)
  • Christoph Ortner (The University of Warwick, United Kingdom)
  • Danny Perez (Los Alamos National Laboratory, USA)
  • Nikolas Provatas (McGill University, Canada)
  • Talat Rahman (University of Central Florida, USA)
  • Celia Reina (University of Pennsylvania, USA)
  • Thomas Swinburne (Centre Interdisciplinaire de Nanoscience de Marseille, France)
  • Ellad Tadmor (University of Minnesota, USA)
  • Mira Todorova (Max-Planck-Institut für Eisenforschung, Germany)
  • Milica Todorovic (Aalto University, Finland)
  • Lin-Wang Wang (Lawrence Berkeley National Laboratory, USA)

Symposium Organizers

Enrique Martinez
Clemson University
Department of Mechanical Engineering
USA

David Aristoff
Colorado State
USA

Jutta Rogal
Interdisciplinary Centre for Advanced Materials Simulation (ICAMS)
Germany

Gideon Simpson
Drexel University
USA

Topics

kinetics machine learning modeling