Learning in continuous action space for developing high dimensional potential energy models
oleh: Sukriti Manna, Troy D. Loeffler, Rohit Batra, Suvo Banik, Henry Chan, Bilvin Varughese, Kiran Sasikumar, Michael Sternberg, Tom Peterka, Mathew J. Cherukara, Stephen K. Gray, Bobby G. Sumpter, Subramanian K. R. S. Sankaranarayanan
| Format: | Article |
|---|---|
| Diterbitkan: | Nature Portfolio 2022-01-01 |
Deskripsi
Reinforcement learning algorithms are emerging as powerful machine learning approaches. This paper introduces a novel machine-learning approach for learning in continuous action space and applies this strategy to the generation of high dimensional potential models for a wide variety of materials.