Simple and efficient algorithms for training machine learning potentials to force data
- Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Machine learning models, trained on data from ab initio quantum simulations, are yielding molecular dynamics potentials with unprecedented accuracy. One limiting factor is the quantity of available training data, which can be expensive to obtain. A quantum simulation often provides all atomic forces, in addition to the total energy of the system. These forces provide much more information than the energy alone. It may appear that training a model to this large quantity of force data would introduce significant computational costs. Actually, training to all available force data should only be a few times more expensive than training to energies alone. Here, we present a new algorithm for efficient force training, and benchmark its accuracy by training to forces from real-world datasets for organic chemistry and bulk aluminum.
- Research Organization:
- Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States); Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
- Sponsoring Organization:
- USDOE National Nuclear Security Administration (NNSA); USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE Office of Science (SC), Basic Energy Sciences (BES)
- DOE Contract Number:
- AC04-94AL85000; NA0003525
- OSTI ID:
- 1763572
- Report Number(s):
- SAND--2020-6014R; 686667
- Country of Publication:
- United States
- Language:
- English
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