Anmol Kumar
5 Papers
Anmol Kumar is an academic researcher. The author has contributed to research in topics: Medicine & Force field (fiction). The author has an hindex of 2, co-authored 4 publications.
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Papers
Deep Neural Network Model to Predict the Electrostatic Parameters in the Polarizable Classical Drude Oscillator Force Field.
TL;DR: In this article , deep neural network (DNN) models are trained on quantum mechanical (QM)-based partial charges and atomic polarizabilities along with Thole scale factors trained to target QM molecular dipole moments and polarizability.
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Constructing Potential Energy Surface With Correlated Theory For Dipeptides Using Molecular Tailoring Approach.
TL;DR: In this paper , a fragment-based molecular tailoring approach (MTA) was employed for building the potential energy surface (PES) for two dipeptides viz alanine-alanine and alaninea-proline employing correlated theory, with augmented Dunning basis sets.
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Increasing the Accuracy and Robustness of the CHARMM General Force Field with an Expanded Training Set.
Anastasia Croitoru,Anmol Kumar,Jean-Christophe Lambry,Jihyeon Lee,Suliman Sharif,Wenbo Yu,Alexander D. MacKerell,Alexey Aleksandrov +7 more
Drude Polarizable Lipid Force Field with Explicit Treatment of Long-Range Dispersion: Parametrization and Validation for Saturated and Monounsaturated Zwitterionic Lipids.
Ya-Xian Yu,Richard M. Venable,Jonathan Thirman,Payal Chatterjee,Anmol Kumar,Richard W. Pastor,Benoît Roux,Alexander D. MacKerell,Jeffery B. Klauda +8 more
TL;DR: In this paper , a further optimization of the Drude lipid force field, termed Drude2023, was proposed, including improved treatment of the phosphate and glycerol linker region of PC and PE headgroups, additional optimization of monounsaturated lipids, and inclusion of long-range Lennard-Jones interactions using the particle-mesh Ewald method.
Harnessing Deep Learning for Optimization of Lennard-Jones Parameters for the Polarizable Classical Drude Oscillator Force Field.
TL;DR: In this article , a deep learning-based parametrization framework is developed, allowing for sampling of wide ranges of Lennard-Jones parameters targeting experimental condensed phase thermodynamic properties.