Zobrazeno 1 - 10
of 235
pro vyhledávání: '"Subramanian K R S, Sankaranarayanan"'
A Continuous Action Space Tree search for INverse desiGn (CASTING) framework for materials discovery
Autor:
Suvo Banik, Troy Loefller, Sukriti Manna, Henry Chan, Srilok Srinivasan, Pierre Darancet, Alexander Hexemer, Subramanian K. R. S. Sankaranarayanan
Publikováno v:
npj Computational Materials, Vol 9, Iss 1, Pp 1-16 (2023)
Abstract Material properties share an intrinsic relationship with their structural attributes, making inverse design approaches crucial for discovering new materials with desired functionalities. Reinforcement Learning (RL) approaches are emerging as
Externí odkaz:
https://doaj.org/article/0cd9e43705a342c7862b45c7158152b5
Autor:
Aditya Koneru, Henry Chan, Sukriti Manna, Troy D. Loeffler, Debdas Dhabal, Andressa A. Bertolazzo, Valeria Molinero, Subramanian K. R. S. Sankaranarayanan
Publikováno v:
npj Computational Materials, Vol 9, Iss 1, Pp 1-13 (2023)
Abstract Silica is an abundant and technologically attractive material. Due to the structural complexities of silica polymorphs coupled with subtle differences in Si–O bonding characteristics, the development of accurate models to predict the struc
Externí odkaz:
https://doaj.org/article/3c88741166314ba68402d9365010783e
Autor:
Karthik Balasubramanian, Suvo Banik, Sukriti Manna, Srilok Srinivasan, Subramanian K. R. S. Sankaranarayanan
Publikováno v:
APL Machine Learning, Vol 2, Iss 1, Pp 016103-016103-8 (2024)
Boron, an element of captivating chemical intricacy, has been surrounded by controversies ever since its discovery in 1808. The complexities of boron stem from its unique position between metals and insulators in the Periodic Table. Recent computatio
Externí odkaz:
https://doaj.org/article/40bc1b9a84f744a98b8278bf5a13368b
Autor:
Karthik Balasubramanian, Suvo Banik, Sukriti Manna, Srilok Srinivasan, Subramanian K. R. S. Sankaranarayanan
Publikováno v:
APL Machine Learning, Vol 2, Iss 1, Pp 019901-019901-1 (2024)
Externí odkaz:
https://doaj.org/article/966944183e9d4667acf8f901e6800922
Autor:
Suvo Banik, Debdas Dhabal, Henry Chan, Sukriti Manna, Mathew Cherukara, Valeria Molinero, Subramanian K. R. S. Sankaranarayanan
Publikováno v:
npj Computational Materials, Vol 9, Iss 1, Pp 1-12 (2023)
Abstract We introduce Crystal Edge Graph Attention Neural Network (CEGANN) workflow that uses graph attention-based architecture to learn unique feature representations and perform classification of materials across multiple scales (from atomic to me
Externí odkaz:
https://doaj.org/article/8cb1aa1f7f3847a0a01d6affaf962a87
Accurate determination of solvation free energies of neutral organic compounds from first principles
Autor:
Leonid Pereyaslavets, Ganesh Kamath, Oleg Butin, Alexey Illarionov, Michael Olevanov, Igor Kurnikov, Serzhan Sakipov, Igor Leontyev, Ekaterina Voronina, Tyler Gannon, Grzegorz Nawrocki, Mikhail Darkhovskiy, Ilya Ivahnenko, Alexander Kostikov, Jessica Scaranto, Maria G. Kurnikova, Suvo Banik, Henry Chan, Michael G. Sternberg, Subramanian K. R. S. Sankaranarayanan, Brad Crawford, Jeffrey Potoff, Michael Levitt, Roger D. Kornberg, Boris Fain
Publikováno v:
Nature Communications, Vol 13, Iss 1, Pp 1-7 (2022)
Theoretical estimations of solvation free energy by continuum solvation models are generally not accurate. Here the authors report a polarizable force field fitted entirely to first-principles calculations for the estimation of free energy of solvati
Externí odkaz:
https://doaj.org/article/196a3e9a85954eee90c95898bb4c8175
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
Autor:
Xu Zhang, Hoang Nguyen, Jeffrey T. Paci, Subramanian K. R. S. Sankaranarayanan, Jose L. Mendoza-Cortes, Horacio D. Espinosa
Publikováno v:
npj Computational Materials, Vol 7, Iss 1, Pp 1-11 (2021)
Abstract This investigation presents a generally applicable framework for parameterizing interatomic potentials to accurately capture large deformation pathways. It incorporates a multi-objective genetic algorithm, training and screening property set
Externí odkaz:
https://doaj.org/article/66cdaba6ba7a4b70a40be14e87599a73
Autor:
Srilok Srinivasan, Rohit Batra, Henry Chan, Ganesh Kamath, Mathew J. Cherukara, Subramanian K. R. S. Sankaranarayanan
Publikováno v:
ACS Omega, Vol 6, Iss 19, Pp 12557-12566 (2021)
Externí odkaz:
https://doaj.org/article/a5492fddd7874a6093e8d4eb60d5da50
Autor:
Sandeep Madireddy, Ding-Wen Chung, Troy Loeffler, Subramanian K. R. S. Sankaranarayanan, David N. Seidman, Prasanna Balaprakash, Olle Heinonen
Publikováno v:
Scientific Reports, Vol 9, Iss 1, Pp 1-10 (2019)
Abstract Atom-probe tomography (APT) facilitates nano- and atomic-scale characterization and analysis of microstructural features. Specifically, APT is well suited to study the interfacial properties of granular or heterophase systems. Traditionally,
Externí odkaz:
https://doaj.org/article/44bbc6b308d1471da03b8e1a1ab3c668