Efficient and Accurate Local Equivariant Deep Neural Network Interatomic Potential for Large-Scale Porous Liquids Simulations

Efficient and Accurate Local Equivariant Deep Neural Network Interatomic Potential for Large-Scale Porous Liquids Simulations

<p dir="ltr">Porous materials serve diverse applications like molecular separations and catalysis, offering an energy-efficient method for capturing greenhouse gases (CO<sub>2</sub>, and CH<sub>4</sub>) and valuable noble gases (Xe, Ar, and Kr). Xenon — vital...

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Bibliographic Details
Main Author: Ouail Zakary (19740229) (author)
Other Authors: Perttu Lantto (19740246) (author)
Published: 2024
Subjects:
Computational chemistry
Theoretical quantum chemistry
Deep learning
Neural networks
Xenon NMR
4-(trifluoromethoxy) benzyl alcohol
2-hydroxyacetophenone
2,4-dichlorotoluene
CC3 Porous Organic Cages
Atomistic Machine Learning
Invariant and Equivariant Graph Neural Networks
DFT Calculations
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