Výsledky vyhledávání - "Michael Sternberg"

Akademický článek

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

Autor: 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

Publikováno v: Nature Communications, Vol 13, Iss 1, Pp 1-10 (2022)

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 po

Externí odkaz: https://doaj.org/article/856fb35b358a4d51a94ca42f1d112763

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Multi-reward Reinforcement Learning Based Bond-Order Potential to Study Strain-Assisted Phase Transitions in Phosphorene

Autor: Aditya Koneru, Rohit Batra, Sukriti Manna, Troy D. Loeffler, Henry Chan, Michael Sternberg, Anthony Avarca, Harpal Singh, Mathew J. Cherukara, Subramanian K. R. S. Sankaranarayanan

Publikováno v: The Journal of Physical Chemistry Letters. 13:1886-1893

We introduce a multi-reward reinforcement learning (RL) approach to train a flexible bond-order potential (BOP) for 2D phosphorene based on ab initio training data sets. Our approach is based on a continuous action space Monte Carlo tree search algor

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7ad025ba8664b697bfe366fd2b34332b
https://doi.org/10.1021/acs.jpclett.1c03551

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High-Entropy 2D Carbide MXenes: TiVNbMoC3 and TiVCrMoC3

Autor: Zachary D. Hood, Babak Anasori, Brian C. Wyatt, Bowen Zhang, Rasoul Khaledialidusti, Michael Sternberg, Srinivasa Kartik Nemani, Sukriti Manna, Subramanian K. R. S. Sankaranarayanan, Weichen Hong

Publikováno v: ACS Nano. 15:12815-12825

Two-dimensional (2D) transition metal carbides and nitrides, known as MXenes, are a fast-growing family of 2D materials. MXenes 2D flakes have n + 1 (n = 1-4) atomic layers of transition metals interleaved by carbon/nitrogen layers, but to-date remai

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::cb0ad03c8409dfe7a6fe7a4d2c8a3085
https://doi.org/10.1021/acsnano.1c02775

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BLAST: bridging length/timescales via atomistic simulation toolkit

Autor: Badri Narayanan, Subramanian K. R. S. Sankaranarayanan, Michael Sternberg, Anthony Avarca, Henry Chan, Troy D. Loeffler, Mathew J. Cherukara

Publikováno v: MRS Advances. 6:21-31

The ever-increasing power of supercomputers coupled with highly scalable simulation codes has made molecular dynamics an indispensable tool in applications ranging from predictive modeling of materials to computational design and discovery of new mat

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::725ca204ee1c531b7b74ff9c3ede9e70
https://doi.org/10.1557/s43580-020-00002-z

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

Publikováno v: Nature Communications
Nature Communications, Vol 13, Iss 1, Pp 1-10 (2022)

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 desi

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::56f30f8853c084ad51532a4e17f38d78
https://pubmed.ncbi.nlm.nih.gov/35042872

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High-Entropy 2D Carbide MXenes

Autor: Babak Anasori, Srinivasa Kartik Nemani, Subramanian K. R. S. Sankaranarayanan, Zhang B, Michael Sternberg, Rasoul Khaledialidusti, Sukriti Manna, Hood Zd, Hong W, Brian C. Wyatt

Two-dimensional (2D) transition metal carbides and nitrides, known as MXenes, are a fast-growing family of 2D materials. MXenes 2D flakes have n + 1 (n = 1 – 4) atomic layers of transition metals interleaved by carbon/nitrogen layers, but to-date r

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::1e4bcc60ae8712fa188b384c57dbeb72
https://doi.org/10.26434/chemrxiv.14346953.v1

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Learning in Continuous Action Space for Determination of High Dimensional Potential Energy Surfaces

Autor: Subramanian K. R. S. Sankaranarayanan, Bobby G. Sumpter, Rohit Batra, Tom Peterka, Michael Sternberg, Suvo Banik, Stephen K. Gray, Kiran Sasikumar, Sukriti Manna, Henry Chan, Troy D. Loeffler, Mathew J. Cherukara

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 demonstrated recently in board games like chess, Shogi, and Go. Many

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::1a303dd906987f3266fef07c70f01d25
https://doi.org/10.21203/rs.3.rs-284625/v1

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