I am a postdoc at MIT, funded by the School of Engineering’s Postdoc Fellowship for Excellence in Engineering! In August 2024 I finished my PhD at the Lab of Computational Science and Modeling at EPFL, which I called home since the summer of 2020. Before embarking on this adventure, I was working on getting my Masters at the Indian Institute of Space Science and Technology, where I started given my fascination with the stars and mysteries of the giant worlds beyond our world, but ended up falling in love with the dynamics of objects on the opposite end of the length scale. My current research interests include machine learning (ML) enabled simulations at the atomic scale, incorporating symmetries (equivariance) in these frameworks and unifying them with electronic structure.
@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},}
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}}
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},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},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},reps={true}}
Equivariant representations for molecular Hamiltonians and N-center atomic-scale properties
@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},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},reps={true}}
Multi-scale approach for the prediction of atomic scale properties
@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},}
Recursive evaluation and iterative contraction of N-body equivariant features
@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},}
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, 2020