Adaptive machine learning framework to accelerate ab initio molecular dynamics

By V. Botu, Rampi Ramprasad. 2015

374 citations recorded by the source corpus.

Subfields: Materials Chemistry, Molecular Biology

How researchers see this paper

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

In Materials Chemistry

1. Yuri Mishin
2. Rampi Ramprasad
3. Rohit Batra
4. Evgeny V. Podryabinkin
5. Alexander V. Shapeev
6. Konstantin Gubaev
7. G. P. P. Pun
8. A. Pais

In Molecular Biology

1. Yuri Mishin
2. A. Pais
3. Tânia F Cova
4. Rohit Batra
5. G. P. P. Pun
6. Rampi Ramprasad
7. Alexander V. Shapeev
8. Evgeny V. Podryabinkin

Citations in the corpus

• Active learning of linearly parametrized interatomic potentials (2016, background): “Finally, the authors of [33, 34] train a machine learning model predicting the force errors based on the distance be-tween a given atomic configuration and the training set.”
• Machine Learning Force Fields: Construction, Validation, and Outlook (2016, background): “16,17 The force fi eld is henceforth dubbed AGNI.”
• Physically informed artificial neural networks for atomistic modeling of materials (2018, methodology): “The energies predicted by the PINN (a,c,e) and NN (b,d,f) potentials are compared with DFT calculations from [20, 21].”