@article{jpcm_editorial_2026,title={Editorial for machine learning for understanding the physics of chemical processes},volume={38},issn={0953-8984},url={https://doi.org/10.1088/1361-648X/ae3415},doi={10.1088/1361-648X/ae3415},number={2},urldate={2026-01-20},journal={Journal of Physics: Condensed Matter},author={Nigam, Jigyasa and Veit, Max},year={2026},note={Publisher: IOP Publishing},pages={020201},reps={true}}
2025
Equivariant Compression of Quantum Operator Representations
Jigyasa Nigam, Utku Sirin, Tess Smidt, and Stratos Idreos
@article{nigam2025equivariantcompress,title={Equivariant Compression of Quantum Operator Representations},author={Nigam, Jigyasa and Sirin, Utku and Smidt, Tess and Idreos, Stratos},year={2025},journal={NeurIPS ML4PS},note={NeurIPS 2025, ML4PS Workshop},ham={true},url={https://ml4physicalsciences.github.io/2025/files/NeurIPS_ML4PS_2025_232.pdf}}
Scalable emulation of protein equilibrium ensembles with generative deep learning
S Lewis, T Hempel, J Jimenez-Luna, M Gastegger, Yu Xie, A Foong, V Satorras, and others
@article{bioemu,title={Scalable emulation of protein equilibrium ensembles with generative deep learning},url={https://www-science-org.libproxy.mit.edu/doi/10.1126/science.adv9817},author={Lewis, S and Hempel, T and Jimenez-Luna, J and Gastegger, M and Xie, Yu and Foong, A and Satorras, V and others},journal={Science},year={2025},publisher={American Association for the Advancement of Science},}
AniSOAP: Machine Learning Representations for Coarse-grained and Non-spherical Systems
Arthur Yan Lin, Lucas Ortengren, Seonwoo Hwang, Yong-Cheol Cho, Jigyasa Nigam, and Rose K Cersonsky
@article{lin2025anisoap,title={AniSOAP: Machine Learning Representations for Coarse-grained and Non-spherical Systems},author={Lin, Arthur Yan and Ortengren, Lucas and Hwang, Seonwoo and Cho, Yong-Cheol and Nigam, Jigyasa and Cersonsky, Rose K},journal={Journal of Open Source Software},volume={10},number={111},pages={7954},year={2025},url={https://joss.theoj.org/papers/10.21105/joss.07954.pdf},reps={true}}
Exploring the design space of machine-learning models for quantum chemistry with a fully differentiable framework
Divya Suman, Jigyasa Nigam, Sandra Saade, Paolo Pegolo, Hanna Tuerk, Xing Zhang, Garnet Kin Chan, and Michele Ceriotti
@article{suman2025exploring,title={Exploring the design space of machine-learning models for quantum chemistry with a fully differentiable framework},url={https://pubs.acs.org/doi/10.1021/acs.jctc.5c00522},author={Suman, Divya and Nigam, Jigyasa and Saade, Sandra and Pegolo, Paolo and Tuerk, Hanna and Zhang, Xing and Chan, Garnet Kin and Ceriotti, Michele},journal={Journal of Chemical Theory and Computation},volume={21},number={13},pages={6505-6516},year={2025},ham={true}}
2024
Integrating symmetry and physical constraints into atomic-scale machine learning
@article{lin2024expanding,title={Expanding density-correlation machine learning representations for anisotropic coarse-grained particles},author={Lin, Arthur and Huguenin-Dumittan, Kevin K and Cho, Yong-Cheol and Nigam, Jigyasa and Cersonsky, Rose K},journal={The Journal of Chemical Physics},volume={161},number={7},year={2024},publisher={AIP Publishing},url={https://pubs.aip.org/aip/jcp/article-abstract/161/7/074112/3308992/Expanding-density-correlation-machine-learning},reps={true}}
Electronic Excited States from Physically Constrained Machine Learning
Edoardo Cignoni, Divya Suman, Jigyasa Nigam, Lorenzo Cupellini, Benedetta Mennucci, and Michele Ceriotti
@article{cignoni2024electronic,title={Electronic Excited States from Physically Constrained Machine Learning},author={Cignoni, Edoardo and Suman, Divya and Nigam, Jigyasa and Cupellini, Lorenzo and Mennucci, Benedetta and Ceriotti, Michele},journal={ACS Central Science},volume={10},number={3},pages={637--648},year={2024},publisher={ACS Publications},url={https://pubs.acs.org/doi/full/10.1021/acscentsci.3c01480},ham={true}}
Completeness of atomic structure representations
Jigyasa Nigam, Sergey N Pozdnyakov, Kevin K Huguenin-Dumittan, and Michele Ceriotti
@article{nigam2024completeness,title={Completeness of atomic structure representations},author={Nigam, Jigyasa and Pozdnyakov, Sergey N and Huguenin-Dumittan, Kevin K and Ceriotti, Michele},journal={APL Machine Learning},volume={2},number={1},year={2024},publisher={AIP Publishing},url={https://pubs.aip.org/aip/aml/article/2/1/016110/3262493/Completeness-of-atomic-structure-representations},reps={true}}
2022
Equivariant representations for molecular Hamiltonians and N-center atomic-scale properties
Jigyasa Nigam, Michael J Willatt, and Michele Ceriotti
@article{nigam2022equivariant,title={Equivariant representations for molecular Hamiltonians and N-center atomic-scale properties},author={Nigam, Jigyasa and Willatt, Michael J and Ceriotti, Michele},journal={The Journal of Chemical Physics},volume={156},number={1},pages={014115},year={2022},publisher={AIP Publishing LLC},url={https://pubs.aip.org/aip/jcp/article-abstract/156/1/014115/2839817/Equivariant-representations-for-molecular},ham={true}}
Unified theory of atom-centered representations and message-passing machine-learning schemes
Jigyasa Nigam, Sergey Pozdnyakov, Guillaume Fraux, and Michele Ceriotti
@article{nigam2022unified,title={Unified theory of atom-centered representations and message-passing machine-learning schemes},author={Nigam, Jigyasa and Pozdnyakov, Sergey and Fraux, Guillaume and Ceriotti, Michele},journal={The Journal of Chemical Physics},volume={156},number={20},pages={204115},year={2022},publisher={AIP Publishing LLC},url={https://pubs.aip.org/aip/jcp/article/156/20/204115/2841327/Unified-theory-of-atom-centered-representations},reps={true}}
Roadmap on Machine Learning in Electronic Structure
H J Kulik, T Hammerschmidt, J Schmidt, S Botti, M A L Marques, M Boley, M Scheffler, M Todorović, P Rinke, C Oses, A Smolyanyuk, S Curtarolo, A Tkatchenko, A P Bartók, S Manzhos, M Ihara, T Carrington, J Behler, O Isayev, M Veit, A Grisafi, J Nigam, M Ceriotti, K T Schütt, J Westermayr, M Gastegger, R J Maurer, B Kalita, K Burke, R Nagai, R Akashi, O Sugino, J Hermann, F Noé, S Pilati, C Draxl, M Kuban, S Rigamonti, M Scheidgen, M Esters, D Hicks, C Toher, P V Balachandran, I Tamblyn, S Whitelam, C Bellinger, and L M Ghiringhelli
@article{kuli+22es,title={Roadmap on {{Machine}} Learning in Electronic Structure},author={Kulik, H J and Hammerschmidt, T and Schmidt, J and Botti, S and Marques, M A L and Boley, M and Scheffler, M and Todorovi{\'c}, M and Rinke, P and Oses, C and Smolyanyuk, A and Curtarolo, S and Tkatchenko, A and Bart{\'o}k, A P and Manzhos, S and Ihara, M and Carrington, T and Behler, J and Isayev, O and Veit, M and Grisafi, A and Nigam, J and Ceriotti, M and Sch{\"u}tt, K T and Westermayr, J and Gastegger, M and Maurer, R J and Kalita, B and Burke, K and Nagai, R and Akashi, R and Sugino, O and Hermann, J and No{\'e}, F and Pilati, S and Draxl, C and Kuban, M and Rigamonti, S and Scheidgen, M and Esters, M and Hicks, D and Toher, C and Balachandran, P V and Tamblyn, I and Whitelam, S and Bellinger, C and Ghiringhelli, L M},year={2022},month=jun,journal={Electron. Struct.},volume={4},number={2},pages={023004},issn={2516-1075},doi={10.1088/2516-1075/ac572f},urldate={2022-08-23},url={https://iopscience.iop.org/article/10.1088/2516-1075/ac572f}}
2021
Optimal Radial Basis for Density-Based Atomic Representations
Alexander Goscinski, Félix Musil, Sergey Pozdnyakov, Jigyasa Nigam, and Michele Ceriotti
@article{gosc+21jcp,title={Optimal Radial Basis for Density-Based Atomic Representations},author={Goscinski, Alexander and Musil, F{\'e}lix and Pozdnyakov, Sergey and Nigam, Jigyasa and Ceriotti, Michele},year={2021},journal={J. Chem. Phys.},volume={155},number={10},pages={104106},doi={10.1063/5.0057229},url={https://pubs.aip.org/aip/jcp/article-abstract/155/10/104106/1013253/Optimal-radial-basis-for-density-based-atomic},}
Multi-scale approach for the prediction of atomic scale properties
Andrea Grisafi, Jigyasa Nigam, and Michele Ceriotti
@article{grisafi2021multi,title={Multi-scale approach for the prediction of atomic scale properties},author={Grisafi, Andrea and Nigam, Jigyasa and Ceriotti, Michele},journal={Chemical Science},volume={12},number={6},pages={2078--2090},year={2021},publisher={Royal Society of Chemistry},reps={true},url={https://pubs.rsc.org/en/content/articlelanding/2021/sc/d0sc04934d},}
2020
Recursive evaluation and iterative contraction of N-body equivariant features
Jigyasa Nigam, Sergey Pozdnyakov, and Michele Ceriotti
@article{nigam2020recursive,title={Recursive evaluation and iterative contraction of N-body equivariant features},author={Nigam, Jigyasa and Pozdnyakov, Sergey and Ceriotti, Michele},journal={The Journal of Chemical Physics},volume={153},number={12},pages={121101},year={2020},publisher={AIP Publishing LLC},reps={true},url={https://pubs.aip.org/aip/jcp/article/153/12/121101/1023428/Recursive-evaluation-and-iterative-contraction-of},}
Exploring the design space of machine-learning models for quantum chemistry with a fully differentiable frameworkJournal of Chemical Theory and Computation, 2025
Expanding density-correlation machine learning representations for anisotropic coarse-grained particlesThe Journal of Chemical Physics, 2024
Electronic Excited States from Physically Constrained Machine LearningACS Central Science, 2024
Equivariant representations for molecular Hamiltonians and N-center atomic-scale propertiesThe Journal of Chemical Physics, 2022
Unified theory of atom-centered representations and message-passing machine-learning schemesThe Journal of Chemical Physics, 2022
Recursive evaluation and iterative contraction of N-body equivariant featuresThe Journal of Chemical Physics, Jun 2020