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pro vyhledávání: '"Ock, Janghoon"'
The increasing popularity of machine learning (ML) in catalysis has spurred interest in leveraging these techniques to enhance catalyst design. Our study aims to bridge the gap between physics-based studies and data-driven methodologies by integratin
Externí odkaz:
http://arxiv.org/abs/2405.20397
The exploration of molecular systems' potential energy surface is important for comprehending their complex behaviors, particularly through identifying various metastable states. However, the transition between these states is often hindered by subst
Externí odkaz:
http://arxiv.org/abs/2403.10358
Autor:
Ock, Janghoon, Badrinarayanan, Srivathsan, Magar, Rishikesh, Antony, Akshay, Farimani, Amir Barati
Adsorption energy is a reactivity descriptor that must be accurately predicted for effective machine learning (ML) application in catalyst screening. This process involves determining the lowest energy across various adsorption configurations on a ca
Externí odkaz:
http://arxiv.org/abs/2401.07408
Efficient catalyst screening necessitates predictive models for adsorption energy, a key property of reactivity. However, prevailing methods, notably graph neural networks (GNNs), demand precise atomic coordinates for constructing graph representatio
Externí odkaz:
http://arxiv.org/abs/2309.00563
The practical applications of determining the relative difference in adsorption energies are extensive, such as identifying optimal catalysts, calculating reaction energies, and determining the lowest adsorption energy on a catalytic surface. Althoug
Externí odkaz:
http://arxiv.org/abs/2303.10797
Akademický článek
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Akademický článek
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Autor:
Ock J; Department of Chemical Engineering, Carnegie Mellon University, 5000 Forbes Street, Pittsburgh, Pennsylvania 15213, United States., Mollaei P; Department of Mechanical Engineering, Carnegie Mellon University, 5000 Forbes Street, Pittsburgh, Pennsylvania 15213, United States., Barati Farimani A; Department of Mechanical Engineering, Carnegie Mellon University, 5000 Forbes Street, Pittsburgh, Pennsylvania 15213, United States.
Publikováno v:
Journal of chemical theory and computation [J Chem Theory Comput] 2024 May 28; Vol. 20 (10), pp. 4088-4098. Date of Electronic Publication: 2024 May 10.