Results 111 to 120 of about 19,335,962 (161)

Machine Learning Potential for Identifying and Forecasting Complex Environmental Drivers of <i>Vibrio vulnificus</i> Infections in the United States. [PDF]

Environ Health Perspect
Campbell AM, Cabrera-Gumbau JM, Trinanes J, Baker-Austin C, Martinez-Urtaza J.   +4 more
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Unraveling individual differences in learning potential: A dynamic framework for the case of reading development. [PDF]

Dev Cogn Neurosci
Bonte M, Brem S.
europepmc   +1 more source

Parameter-Free and Electron Counting Satisfied Material Representation for Machine Learning Potential Energy and Force Fields. [PDF]

J Phys Chem Lett
Xi B, Chan MK, Bao K, Zhao W, Chan HM, Chen H, Zhu J.   +6 more
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Assessing Permutationally Invariant Polynomial and Symmetric Gradient Domain Machine Learning Potential Energy Surfaces for H₃O₂. [PDF]

J Phys Chem A
Pandey P, Arandhara M, Houston PL, Qu C, Conte R, Bowman JM, Ramesh SG.   +6 more
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Geometry Optimization Algorithms in Conjunction with the Machine Learning Potential ANI-2x Facilitate the Structure-Based Virtual Screening and Binding Mode Prediction. [PDF]

Biomolecules
Wang L, He X, Ji B, Han F, Niu T, Cai L, Zhai J, Hao D, Wang J.   +8 more
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Machine learning potential for modelling H₂ adsorption/diffusion in MOFs with open metal sites. [PDF]

Chem Sci
Liu S, Dupuis R, Fan D, Benzaria S, Bonneau M, Bhatt P, Eddaoudi M, Maurin G.   +7 more
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Auditory Cortex Morphology Predicts Language Learning Potential in Children and Teenagers. [PDF]

Front Neurosci, 2019
Turker S, Reiterer SM, Schneider P, Seither-Preisler A.   +3 more
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A Low-Order Permutationally Invariant Polynomial Approach to Learning Potential Energy Surfaces Using the Bond-Order Charge-Density Matrix: Application to C_n Clusters for n = 3-10, 20. [PDF]

J Phys Chem A
Gutierrez-Cardenas J, Gibbas BD, Whitaker K, Kaledin M, Kaledin AL.   +4 more
europepmc   +1 more source

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Understanding Alkaline Hydrogen Oxidation Reaction on PdNiRuIrRh High-Entropy-Alloy by Machine Learning Potential.

Angewandte Chemie, 2023
High-entropy alloy (HEA) catalysts have been widely studied in electrolysis. However, identifying atomic structure of HEA with complex atomic arrangement is challenging, which seriously hinders the fundamental understanding of catalytic mechanism.
Yana Men, Dean Wu, Youcheng Hu, Lei Li, Peng Li, Shuangfeng Jia, Jianbo Wang, Gongzhen Cheng, Shengli Chen, W. Luo   +9 more
semanticscholar   +1 more source