Zobrazeno 1 - 10
of 17
pro vyhledávání: '"Suvo Banik"'
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
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:
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:
Suvo Banik, Karthik Balasubramanian, Sukriti Manna, Sybil Derrible, Subramanian Sankaranarayananan
Insights into the unique characteristics across different classes of materials are crucial for Machine Learning (ML) tools and reveal the physics behind the studied process. Traditional predictive modeling of elastic properties of materials is limite
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bb420ec829bc984fe5a7b9ec96fe1bdc
https://doi.org/10.26434/chemrxiv-2023-07vcr
https://doi.org/10.26434/chemrxiv-2023-07vcr
Autor:
Hai-Tian Zhang, Tae Joon Park, A. N. M. Nafiul Islam, Dat S. J. Tran, Sukriti Manna, Qi Wang, Sandip Mondal, Haoming Yu, Suvo Banik, Shaobo Cheng, Hua Zhou, Sampath Gamage, Sayantan Mahapatra, Yimei Zhu, Yohannes Abate, Nan Jiang, Subramanian K. R. S. Sankaranarayanan, Abhronil Sengupta, Christof Teuscher, Shriram Ramanathan
Publikováno v:
Science. 375:533-539
Reconfigurable devices offer the ability to program electronic circuits on demand. In this work, we demonstrated on-demand creation of artificial neurons, synapses, and memory capacitors in post-fabricated perovskite NdNiO3devices that can be simply
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::770908bb298c00f0e8ba8ba266456770
https://doi.org/10.1126/science.abj7943
https://doi.org/10.1126/science.abj7943
A Continuous Action Space Tree search for INverse desiGn (CASTING) Framework for Materials Discovery
Autor:
Subramanian Sankaranarayanan, Suvo Banik, Troy Loeffler, Sukriti Manna, Srilok Sriniva, Pierre Darancet, Henry Chan, Alex Hexemer
Fast and accurate prediction of optimal crystal structure, topology, and microstructures is important for accelerating the design and discovery of new materials for energy applications. As material properties are strongly correlated to the underlying
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8667007c40185233d1baa63af296e1ff
https://doi.org/10.21203/rs.3.rs-2407165/v1
https://doi.org/10.21203/rs.3.rs-2407165/v1
Autor:
Subramanian Sankaranarayanan, Suvo Banik, Debdas Dhabal, Henry Chan, Sukriti Manna, Mathew Cherukara, Valeria Molinero
Machine learning models and applications in materials design and discovery typically involve the use of feature representations or “descriptors” followed by a learning algorithm that maps it to a user-desired property of interest. Most popular ma
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8733f4bd154fff59daa236a966b08210
https://doi.org/10.21203/rs.3.rs-1907453/v1
https://doi.org/10.21203/rs.3.rs-1907453/v1