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Symposium DS03—Combining Machine Learning with Simulations for Materials Modeling

The advent of high-speed computation has significantly accelerated the materials modeling and simulation paradigm in the past few decades. These simulations cover wide length-and time-scales using ab-initio first principle, atomistic, mesoscale, and continuum simulations. Despite the economical nature of the simulations in comparison to experiments, they still suffer from the major deficiencies, namely, limitations on the systems size, simulation time, accuracy of simulations, transferability of a simulation to different scenarios, to name a few. For example, while first principle simulations can provide accurate predictions on the material response to the electronic level, they are limited to a few hundreds of atoms. Recently, machine learning has shown promising means to address some of these challenges successfully. Some of these developments include machine-learned interatomic potentials, physics-informed neural networks, convolutional neural network based microstructure modeling, and graph neural networks for structure–property correlations. This symposium will highlight the latest development in machine learning for simulations with specific focus in three major areas: (i) supporting and accelerating simulations using machine learning (for example, machine learned potentials), (ii) interpreting and decoding simulations and high-throughput data using machine learning, (iii) replacing traditional differential equation-based simulations with machine-learned simulations.

Topics will include:

  • Development of machine learned inter-atomic potentials
  • Physics-informed machine learning models for materials simulation
  • Graph neural networks for material modeling
  • Development of realistic material models using image processing
  • Transfer learning for material modeling
  • Topology optimization using machine learning
  • Reduced order machine learning models for atomistic simulations
  • Development of tailored microstructure using machine learning
  • Machine learning for continuum simulations
  • Using natural language processing for materials modeling
  • Active learning-based hybrid simulations
  • Inferring material descriptors from simulations through machine learning

Invited Speakers:

  • Jörg Behler (Georg-August-Universität Göttingen, Germany)
  • Markus Buehler (Massachusetts Institute of Technology, USA)
  • Michelle Ceriotti (École Polytechnique Fédérale de Lausanne, Switzerland)
  • Mathew Cherukara (Argonne National Laboratory, USA)
  • Jacqueline Cole (University of Cambridge, United Kingdom)
  • Ekin Cubuk (Google Brain, USA)
  • Marivi Fernández-Serra (Stony Brook University, The State University of New York, USA)
  • Shirley Ho (Flatiron Institute, USA)
  • James Kermode (University of Warwick, United Kingdom)
  • Heather Kulik (Massachusetts Institute of Technology, USA)
  • Kristin Persson (University of California, Berkeley, USA)
  • Abhishek Singh (Indian Institute of Science, Bengaluru, India)
  • Yizhou Sun (University of California, Los Angeles, USA)

Symposium Organizers

N M Anoop Krishnan
Indian Institute of Technology Delhi
Civil Engineering

Mathieu Bauchy
University of California, Los Angeles
Civil and Environmental Engineering
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Sumanta Das
University of Rhode Island
Civil Engineering

Christian Hoover
Arizona State University
Civil Engineering

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MRS publishes with Springer Nature


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