Publications | Jigyasa Nigam

2026

Machine learning electronic structure and atomistic properties from the external potential

Jigyasa Nigam, Tess Smidt, and Geneviève Dusson

arXiv.org, Feb 2026

@article{nigam_machine_2026,
  title = {Machine learning electronic structure and atomistic properties from the external potential},
  url = {https://arxiv.org/abs/2602.15345v1},
  urldate = {2026-02-18},
  journal = {arXiv.org},
  author = {Nigam, Jigyasa and Smidt, Tess and Dusson, Geneviève},
  month = feb,
  year = {2026},
  ham = {true},
  reps = {true},
}

Editorial for machine learning for understanding the physics of chemical processes

Jigyasa Nigam, and Max Veit

Journal of Physics: Condensed Matter, Feb 2026

Publisher: IOP Publishing

Bib

@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

NeurIPS ML4PS, Feb 2025

NeurIPS 2025, ML4PS Workshop

Bib

@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

Science, Feb 2025

Bib

@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

Journal of Open Source Software, Feb 2025

Bib

@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

Journal of Chemical Theory and Computation, Feb 2025

Bib

@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

Jigyasa Nigam

Feb 2024

Bib

@article{thesis2024jn,
  title = {Integrating symmetry and physical constraints into atomic-scale machine learning},
  url = {https://infoscience.epfl.ch/handle/20.500.14299/240680},
  doi = {10.5075/epfl-thesis-10848},
  author = {Nigam, Jigyasa},
  year = {2024},
  keywords = {atom-centered density correlations (ACDC) | atomistic machine learning | descriptors | N-center representations | equivariance , completeness , physical constraints | electronic Hamiltonians},
}

Expanding density-correlation machine learning representations for anisotropic coarse-grained particles

Arthur Lin, Kevin K Huguenin-Dumittan, Yong-Cheol Cho, Jigyasa Nigam, and Rose K Cersonsky

The Journal of Chemical Physics, Feb 2024

Bib

@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

ACS Central Science, Feb 2024

Bib

@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

APL Machine Learning, Feb 2024

Bib

@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

The Journal of Chemical Physics, Feb 2022

Bib

@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

The Journal of Chemical Physics, Feb 2022

Bib

@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

Electron. Struct., Jun 2022

Bib

@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

J. Chem. Phys., Jun 2021

Bib

@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

Chemical Science, Jun 2021

Bib

@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

The Journal of Chemical Physics, Jun 2020

Bib

@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},
}