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Apurba Nandi
Apurba Nandi
Student , IIT Kanpur
Verified email at iitk.ac.in
Title
Cited by
Cited by
Year
Δ-machine learning for potential energy surfaces: A PIP approach to bring a DFT-based PES to CCSD (T) level of theory
A Nandi, C Qu, PL Houston, R Conte, JM Bowman
The Journal of Chemical Physics 154 (5), 2021
1072021
A machine learning approach for prediction of rate constants
PL Houston, A Nandi, JM Bowman
The Journal of Physical Chemistry Letters 10 (17), 5250-5258, 2019
572019
q-AQUA: A many-body CCSD (T) water potential, including four-body interactions, demonstrates the quantum nature of water from clusters to the liquid phase
Q Yu, C Qu, PL Houston, R Conte, A Nandi, JM Bowman
The Journal of Physical Chemistry Letters 13 (22), 5068-5074, 2022
512022
Breaking the coupled cluster barrier for machine-learned potentials of large molecules: The case of 15-atom acetylacetone
C Qu, PL Houston, R Conte, A Nandi, JM Bowman
The Journal of Physical Chemistry Letters 12 (20), 4902-4909, 2021
462021
Using Gradients in Permutationally Invariant Polynomial Potential Fitting: A Demonstration for CH4 Using as Few as 100 Configurations
A Nandi, C Qu, JM Bowman
Journal of Chemical Theory and Computation 15 (5), 2826-2835, 2019
462019
Full and fragmented permutationally invariant polynomial potential energy surfaces for trans and cis N-methyl acetamide and isomerization saddle points
A Nandi, C Qu, JM Bowman
The Journal of Chemical Physics 151 (8), 2019
352019
δ-machine learned potential energy surfaces and force fields
JM Bowman, C Qu, R Conte, A Nandi, PL Houston, Q Yu
Journal of Chemical Theory and Computation 19 (1), 1-17, 2022
332022
Permutationally invariant polynomial regression for energies and gradients, using reverse differentiation, achieves orders of magnitude speed-up with high precision compared to …
PL Houston, C Qu, A Nandi, R Conte, Q Yu, JM Bowman
The Journal of Chemical Physics 156 (4), 2022
322022
Toward an accurate and inexpensive estimation of CCSD (T)/CBS binding energies of large water clusters
N Sahu, G Singh, A Nandi, SR Gadre
The Journal of Physical Chemistry A 120 (28), 5706-5714, 2016
322016
A Machine Learning Approach for Rate Constants. II. Clustering, Training, and Predictions for the O(3P) + HCl → OH + Cl Reaction
A Nandi, JM Bowman, P Houston
The Journal of Physical Chemistry A 124 (28), 5746-5755, 2020
302020
A CCSD (T)-based 4-body potential for water
A Nandi, C Qu, PL Houston, R Conte, Q Yu, JM Bowman
The Journal of Physical Chemistry Letters 12 (42), 10318-10324, 2021
292021
Breaking the bottleneck: Use of molecular tailoring approach for the estimation of binding energies at MP2/CBS limit for large water clusters
G Singh, A Nandi, SR Gadre
The Journal of Chemical Physics 144 (10), 2016
222016
The MD17 datasets from the perspective of datasets for gas-phase “small” molecule potentials
JM Bowman, C Qu, R Conte, A Nandi, PL Houston, Q Yu
The Journal of Chemical Physics 156 (24), 2022
212022
PESPIP: Software to fit complex molecular and many-body potential energy surfaces with permutationally invariant polynomials
PL Houston, C Qu, Q Yu, R Conte, A Nandi, JK Li, JM Bowman
The Journal of Chemical Physics 158 (4), 2023
172023
A Δ-machine learning approach for force fields, illustrated by a CCSD (T) 4-body correction to the MB-pol water potential
C Qu, Q Yu, R Conte, PL Houston, A Nandi, JM Bomwan
Digital Discovery 1 (5), 658-664, 2022
142022
Ring-polymer instanton tunneling splittings of tropolone and isotopomers using a δ-machine learned ccsd (t) potential: Theory and experiment shake hands
A Nandi, G Laude, SS Khire, ND Gurav, C Qu, R Conte, Q Yu, S Li, ...
Journal of the American Chemical Society 145 (17), 9655-9664, 2023
132023
MULTIMODE calculations of vibrational spectroscopy and 1d interconformer tunneling dynamics in Glycine using a full-dimensional potential energy surface
C Qu, PL Houston, R Conte, A Nandi, JM Bowman
The Journal of Physical Chemistry A 125 (24), 5346-5354, 2021
132021
Two-layer Gaussian-based MCTDH study of the S1← S vibronic absorption spectrum of formaldehyde using multiplicative neural network potentials
W Koch, M Bonfanti, P Eisenbrandt, A Nandi, B Fu, J Bowman, D Tannor, ...
The Journal of Chemical Physics 151 (6), 2019
132019
Quantum Calculations on a New CCSD(T) Machine-Learned Potential Energy Surface Reveal the Leaky Nature of Gas-Phase Trans and Gauche Ethanol …
A Nandi, R Conte, C Qu, PL Houston, Q Yu, JM Bowman
Journal of Chemical Theory and Computation 18 (9), 5527-5538, 2022
122022
Quasiclassical simulations based on cluster models reveal vibration-facilitated roaming in the isomerization of CO adsorbed on NaCl
A Nandi, P Zhang, J Chen, H Guo, JM Bowman
Nature Chemistry 13 (3), 249-254, 2021
112021
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