Learning in continuous action space for developing high dimensional potential energy models

Reinforcement learning (RL) approaches that combine a tree search with deep learning have found remarkable success in searching exorbitantly large, albeit discrete action spaces, as in chess, Shogi and Go. Many real-world materials discovery and design applications, however, involve multi-dimensiona...

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Detalles Bibliográficos
Autores principales: Manna, Sukriti, Loeffler, Troy D., Batra, Rohit, Banik, Suvo, Chan, Henry, Varughese, Bilvin, Sasikumar, Kiran, Sternberg, Michael, Peterka, Tom, Cherukara, Mathew J., Gray, Stephen K., Sumpter, Bobby G., Sankaranarayanan, Subramanian K. R. S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8766468/
https://www.ncbi.nlm.nih.gov/pubmed/35042872
http://dx.doi.org/10.1038/s41467-021-27849-6

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8766468/
https://www.ncbi.nlm.nih.gov/pubmed/35042872
http://dx.doi.org/10.1038/s41467-021-27849-6

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