A universal strategy for the creation of machine learning-based atomistic force fields

By Tran Doan Huan, Rohit Batra, James Chapman, S. Krishnan, Lihua Chen, Rampi Ramprasad. 2017

224 citations recorded by the source corpus.

Emerging machine learning (ML)-based approaches provide powerful and novel tools to study a variety of physical and chemical problems. In this contribution, we outline a universal strategy to create ML-based atomistic force fields, which can be used to perform high-fidelity molecular dynamics simulations. This scheme involves (1) preparing a big reference dataset of atomic environments and forces with sufficiently low noise, e.g., using density functional theory or higher-level methods, (2) utilizing a generalizable class of structural fingerprints for representing atomic environments, (3) optimally selecting diverse and non-redundant training datasets from the reference data, and (4) proposing various learning approaches to predict atomic forces directly (and rapidly) from atomic configurations. From the atomistic forces, accurate potential energies can then be obtained by appropriate integration along a reaction coordinate or along a molecular dynamics trajectory. Based on this strategy, we have created model ML force fields for six elemental bulk solids, including Al, Cu, Ti, W, Si, and C, and show that all of them can reach chemical accuracy. The proposed procedure is general and universal, in that it can potentially be used to generate ML force fields for any material using the same unified workflow with little human intervention. Moreover, the force fields can be systematically improved by adding new training data progressively to represent atomic environments not encountered previously.Machine learning: molecular dynamics simulations feel the forceA machine learning-based strategy for calculating atomic force fields could expand the range of molecular dynamics simulations. Molecular dynamics simulations are a powerful tool for exploring how atomic-scale chemical and physical processes evolve over time. To perform such simulations, an initial atomic configuration needs to be defined, and atomic forces are input for each time step. Whilst quantum mechanics-based methods for calculating the fields are available, these approaches cannot easily be applied to large systems over long timescales. A team of researchers from the University of Connecticut, led by Rampi Ramprasad, now present a general and universal strategy for using machine learning-based methods to generate highly accurate atomic force fields that may provide a pathway for performing efficient molecular dynamics simulations on nanometer-sized systems over several nanoseconds.

Subfields: Computational Theory and Mathematics, Materials Chemistry

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

  1. Rohit Batra
  2. Le Song
  3. Rampi Ramprasad
  4. Yuri Mishin

In Materials Chemistry

  1. Rohit Batra
  2. Rampi Ramprasad
  3. Le Song
  4. Yuri Mishin
  5. Nour Makke
  6. Sanjay Chawla

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