The quantum nature of ubiquitous vibrational features revealed for ethylene glycol

Authors: Nandi, Apurba, Conte, Riccardo, Pandey, Priyanka, Houston, Paul L., Qu, Chen, Yu, Qi, Bowman, Joel M.

Subject Terms: Physics - Chemical Physics

Access URL: http://arxiv.org/abs/2501.18833

Cluster Shape Detection Using Dual Attention Induced Convolutional-Clustering Algorithm

Authors: Nandi, Apurba, Halder, Rupayan, Ghosh, Sayantani, Konar, Amit

Source: 2024 IEEE International Conference on Information Technology, Electronics and Intelligent Communication Systems (ICITEICS) Information Technology, Electronics and Intelligent Communication Systems (ICITEICS), 2024 IEEE International Conference on. :1-6 Jun, 2024

Relation: 2024 IEEE International Conference on Information Technology, Electronics and Intelligent Communication Systems (ICITEICS)

Extending the atomic decomposition and many-body representation, a chemistry-motivated monomer-centered approach for machine learning potentials

Authors: Yu, Qi, Ma, Ruitao, Qu, Chen, Conte, Riccardo, Nandi, Apurba, Pandey, Priyanka, Houston, Paul L., Zhang, Dong H., Bowman, Joel M.

Subject Terms: Physics - Chemical Physics, Condensed Matter - Disordered Systems and Neural Networks, Physics - Atomic and Molecular Clusters, Physics - Computational Physics

Access URL: http://arxiv.org/abs/2412.00522

$\Delta$-Machine Learning to Elevate DFT-based Potentials and a Force Field to the CCSD(T) Level Illustrated for Ethanol

Authors: Nandi, Apurba, Pandey, Priyanka, Houston, Paul L., Qu, Chen, Yu, Qi, Conte, Riccardo, Tkatchenko, Alexandre, Bowman, Joel M.

Subject Terms: Physics - Chemical Physics

Access URL: http://arxiv.org/abs/2407.20050

Tell machine learning potentials what they are needed for: Simulation-oriented training exemplified for glycine

Authors: Ge, Fuchun, Wang, Ran, Qu, Chen, Zheng, Peikun, Nandi, Apurba, Conte, Riccardo, Houston, Paul L., Bowman, Joel M., Dral, Pavlo O.

Subject Terms: Physics - Chemical Physics

Access URL: http://arxiv.org/abs/2403.11216

No Headache for PIPs: A PIP Potential for Aspirin Outperforms Other Machine-Learned Potentials

Authors: Houston, Paul L., Qu, Chen, Yu, Qi, Pandey, Priyanka, Conte, Riccardo, Nandi, Apurba, Bowman, Joel M.

Source: J. Chem. Theory Comput. 2024

Subject Terms: Physics - Chemical Physics

Access URL: http://arxiv.org/abs/2401.09316

Stable isotope equilibria in the dihydrogen-water-methane-ethane-propane system. Part 1: Path-integral calculations with CCSD(T) quality potentials

Authors: Korol, Roman ^{a, b, ⁎}, Turner, Andrew C. ^{c, d, e}, Nandi, Apurba ^{f, g}, Bowman, Joel M. ^f, Goddard, William A., III ^{a, h}, Stolper, Daniel A. ^{c, d}

Source: In Geochimica et Cosmochimica Acta February 2025

A Machine Learning Approach for Rate Constants III: Application to the Cl($^2$P) + CH$_4$ $\rightarrow$ CH$_3$ + HCl Reaction

Authors: Houston, Paul L., Nandi, Apurba, Bowman, Joel M.

Source: J. Phys. Chem. A. 2022

Subject Terms: Physics - Chemical Physics

Access URL: http://arxiv.org/abs/2206.12443

A $\Delta$-Machine Learning Approach for Force Fields, Illustrated by a CCSD(T) 4-body Correction to the MB-pol Water Potential

Authors: Qu, Chen, Yu, Qi, Conte, Riccardo, Houston, Paul L., Nandi, Apurba, Bowman, Joel M.

Source: Digital Discovery, 2022

Subject Terms: Physics - Chemical Physics

Access URL: http://arxiv.org/abs/2206.04254

Quantum calculations on a new CCSD(T) machine-learned PES reveal the leaky nature of gas-phase $trans$ and $gauche$ ethanol conformers

Authors: Nandi, Apurba, Conte, Riccardo, Qu, Chen, Houston, Paul L., Yu, Qi, Bowman, Joel M.

Source: J. Chem. Theory Comput. 2022

Subject Terms: Physics - Chemical Physics

Access URL: http://arxiv.org/abs/2206.02297

The MD17 Datasets from the Perspective of Datasets for Gas-Phase 'Small' Molecule Potentials

Authors: Bowman, Joel M., Conte, Chen Qu Riccardo, Nandi, Apurba, Houston, Paul L., Yu, Qi

Source: J. Chem. Phys. 156, (2022) 240901

Subject Terms: Physics - Chemical Physics

Access URL: http://arxiv.org/abs/2205.11663

q-AQUA: a many-body CCSD(T) water potential, including 4-body interactions, demonstrates the quantum nature of water from clusters to the liquid phase

Authors: Yu, Qi, Qu, Chen, Houston, Paul L., Conte, Riccardo, Nandi, Apurba, Bowman, Joel M.

Source: J. Phys. Chem. Lett. 2022, 13, 5068-5074

Subject Terms: Physics - Chemical Physics

Access URL: http://arxiv.org/abs/2204.01804

Permutationally invariant polynomial regression for energies and gradients, using reverse differentiation, achieves orders of magnitude speed-up with high precision compared to other machine learning methods

Authors: Houston, Paul L., Qu, Chen, Nandi, Apurba, Conte, Riccardo, Yu, Qi, Bowman, Joel M.

Source: J. Chem. Phys. 2022

Subject Terms: Physics - Chemical Physics

Access URL: http://arxiv.org/abs/2112.01734

Enabling rapid and accurate construction of CCSD(T)-level potential energy surface of large molecules using molecular tailoring approach

Authors: Khire, Subodh S., Gurav, Nalini D., Nandi, Apurba, Gadre, Shridhar R.

Subject Terms: Physics - Chemical Physics

Access URL: http://arxiv.org/abs/2111.02628

A CCSD(T)-based permutationally invariant polynomial 4-body potential for water

Authors: Nandi, Apurba, Qu, Chen, Houston, Paul L., Conte, Riccardo, Bowman, Joel M.

Source: J. Phys. Chem. Lett. 2021, 12, 10318-10324

Subject Terms: Physics - Chemical Physics

Access URL: http://arxiv.org/abs/2107.05881

Combining state-of-the-art quantum chemistry and machine learning make gold standard potential energy surfaces accessible for medium-sized molecules

Authors: Nandi, Apurba, Nagy, Péter R.

Source: In Artificial Intelligence Chemistry June 2024 2(1)

Breaking the Coupled Cluster Barrier for Machine Learned Potentials of Large Molecules: The Case of 15-atom Acetylacetone

Authors: Qu, Chen, Houston, Paul, Conte, Riccardo, Nandi, Apurba, Bowman, Joel M.

Source: J. Phys. Chem. Lett. 12 (2021) 4902-4909

Subject Terms: Physics - Chemical Physics

Access URL: http://arxiv.org/abs/2103.12333

$\Delta$-Machine Learning for Potential Energy Surfaces: A PIP approach to bring a DFT-based PES to CCSD(T) Level of Theory

Authors: Nandi, Apurba, Qu, Chen, Houston, Paul, Conte, Riccardo, Bowman, Joel M.

Source: J. Chem. Phys. 154 (2021) 051102

Subject Terms: Physics - Chemical Physics

Access URL: http://arxiv.org/abs/2011.11601

Δ-Machine Learned Potential Energy Surfaces and Force Fields

Authors: Bowman, Joel M., Qu, Chen, Conte, Riccardo, Nandi, Apurba, Houston, Paul L., Yu, Qi

Source: JOURNAL OF CHEMICAL THEORY AND COMPUTATION. 19(1):1-17

Availability: http://explore.bl.uk/primo_library/libweb/action/display.do?tabs=detailsTab&gathStatTab=true&ct=display&fn=search&doc=ETOCRN632744251&indx=1&recIds=ETOCRN632744251

Interfacing q-AQUA with a Polarizable Force Field: The Best of Both Worlds

Authors: Qu, Chen, Yu, Qi, Houston, Paul L., Conte, Riccardo, Nandi, Apurba, Bowman, Joel M.

Source: Journal of chemical theory and computation. 19(12):3446-3459

Availability: http://explore.bl.uk/primo_library/libweb/action/display.do?tabs=detailsTab&gathStatTab=true&ct=display&fn=search&doc=ETOCRN633025317&indx=1&recIds=ETOCRN633025317