RESEARCH PAPER · Physical Chemistry Chemical Physics · 2022

Capturing the potential energy landscape of large size molecular clusters from atomic interactions up to a 4-body system using deep learning

Shweta Jindal, Po‐Jen Hsu, Huu Trong Phan, Pei‐Kang Tsou, Jer‐Lai Kuo

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Hydrogen bond network structures of protonated 2,2,2-trifluoroethanol/ethanol mixed clusters probed by infrared spectroscopy combined with a deep-learning structure sampling approach: the origin of the linear type network preference in protonated fluoroalcohol clusters
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Machine learning molecular dynamics simulations of liquid methanol
JUSTC · 2024-01-01
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Searching low-energy conformers of neutral and protonated di-, tri-, and tetra-glycine using first-principles accuracy assisted by the use of neural network potentials
Physical Chemistry Chemical Physics · 2024-01-01
Hieu Cao Dong, Po‐Jen Hsu, Jer‐Lai Kuo
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Chemical space-informed machine learning models for rapid predictions of x-ray photoelectron spectra of organic molecules
Machine Learning Science and Technology · 2024-10-15
Susmita Tripathy, Surajit Das, Shweta Jindal, Raghunathan Ramakrishnan
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Linear–cyclic isomer competition in protonated ethanol–methanol clusters probed by infrared spectroscopy and deep-learning structural and dynamical simulations
Physical Chemistry Chemical Physics · 2025-01-01
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Machine-learned potentials for solvation modeling
Journal of Physics Condensed Matter · 2025-12-30
Roopshree Banchode, Surajit Das, Shampa Raghunathan, Raghunathan Ramakrishnan
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Structure of poly-glycine, poly-alanine, and poly-sarcosine: a neural network assisted first-principles study on the influence of methylation and chain length
Physical Chemistry Chemical Physics · 2026-01-01
Hieu Cao Dong, Po‐Jen Hsu, Jer‐Lai Kuo
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Capturing the potential energy landscape of large size molecular clusters from atomic interactions up to a 4-body system using deep learning

See where this research leads.

Papers that cite this work

Hydrogen bond network structures of protonated 2,2,2-trifluoroethanol/ethanol mixed clusters probed by infrared spectroscopy combined with a deep-learning structure sampling approach: the origin of the linear type network preference in protonated fluoroalcohol clusters

Machine learning molecular dynamics simulations of liquid methanol

Searching low-energy conformers of neutral and protonated di-, tri-, and tetra-glycine using first-principles accuracy assisted by the use of neural network potentials

Chemical space-informed machine learning models for rapid predictions of x-ray photoelectron spectra of organic molecules

Linear–cyclic isomer competition in protonated ethanol–methanol clusters probed by infrared spectroscopy and deep-learning structural and dynamical simulations

Machine-learned potentials for solvation modeling

Structure of poly-glycine, poly-alanine, and poly-sarcosine: a neural network assisted first-principles study on the influence of methylation and chain length