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The Journal of Chemical Physics|July 17, 2026
Machine learning of electronic structure and atomistic properties from the external potentialJigyasa Nigam, Tess Smidt, Geneviève DussonChemical Science|June 24, 2021
Multi-scale approach for the prediction of atomic scale propertiesAndrea Grisafi, Jigyasa Nigam, Michele CeriottiThe Journal of Chemical Physics|October 2, 2020
Recursive evaluation and iterative contraction of N-body equivariant featuresJigyasa Nigam, Sergey Pozdnyakov, Michele CeriottiThe Journal of Chemical Physics|January 9, 2022
Equivariant representations for molecular Hamiltonians and N-center atomic-scale propertiesJigyasa Nigam, Michael J Willatt, Michele CeriottiThe Journal of Chemical Physics|June 1, 2022
Unified theory of atom-centered representations and message-passing machine-learning schemesJigyasa Nigam, Sergey Pozdnyakov, Guillaume Fraux, et al.The Journal of Chemical Physics|September 16, 2021
Optimal radial basis for density-based atomic representationsAlexander Goscinski, Félix Musil, Sergey Pozdnyakov, et al.ACS Central Science|April 1, 2024
Electronic Excited States from Physically Constrained Machine LearningEdoardo Cignoni, Divya Suman, Jigyasa Nigam, et al.The Journal of Chemical Physics|August 20, 2024
Expanding density-correlation machine learning representations for anisotropic coarse-grained particlesArthur Lin, Kevin K Huguenin-Dumittan, Yong-Cheol Cho, et al.Journal of Chemical Theory and Computation|June 25, 2025
Exploring the Design Space of Machine Learning Models for Quantum Chemistry with a Fully Differentiable FrameworkDivya Suman, Jigyasa Nigam, Sandra Saade, et al.The Journal of Chemical Physics|July 17, 2020
Predicting molecular dipole moments by combining atomic partial charges and atomic dipolesMax Veit, David M Wilkins, Yang Yang, et al.Pageof 2