Machine Learning Force Fields: Construction, Validation, and Outlook

By V. Botu, Rohit Batra, James Chapman, Rampi Ramprasad. 2016

485 citations recorded by the source corpus.

Force fields developed with machine learning methods in tandem with quantum mechanics are beginning to find merit, given their (i) low cost, (ii) accuracy, and (iii) versatility. Recently, we proposed one such approach, wherein, the vectorial force on an atom is computed directly from its environment. Here, we discuss the multistep workflow required for their construction, which begins with generating diverse reference atomic environments and force data, choosing a numerical representation for the atomic environments, down selecting a representative training set, and lastly the learning method itself, for the case of Al. The constructed force field is then validated by simulating complex materials phenomena such as surface melting and stress–strain behavior, that truly go beyond the realm of ab initio methods, both in length and time scales. To make such force fields truly versatile an attempt to estimate the uncertainty in force predictions is put forth, allowing one to identify areas of poor performance...

Subfields: Atomic and Molecular Physics, and Optics, Computational Mechanics, Materials Chemistry

View Paper: https://www.semanticscholar.org/paper/9fe0ec4855136007fa73d306f1ff59365002bc58

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In Atomic and Molecular Physics, and Optics

  1. Rohit Batra
  2. Zhen Zhou
  3. An Chen
  4. Xu Zhang
  5. A. Mannodi‐Kanakkithodi
  6. Rampi Ramprasad
  7. Ghanshyam Pilania
  8. Yuri Mishin

In Computational Mechanics

  1. Rohit Batra
  2. Le Song
  3. Rampi Ramprasad

In Materials Chemistry

  1. Rohit Batra
  2. Rampi Ramprasad
  3. Chiho Kim
  4. A. Mannodi‐Kanakkithodi
  5. Ghanshyam Pilania
  6. Xu Zhang
  7. Zhen Zhou
  8. An Chen

Citations in the corpus