most citedExploring the design space of machine-learning models for quantum chemistry with a fully differentiable framework

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physics.chem-ph2025★ 14 cited

Exploring the design space of machine-learning models for quantum chemistry with a fully differentiable framework

Divya Suman, Jigyasa Nigam, Sandra Saade +5

Traditional atomistic machine learning (ML) models serve as surrogates for quantum mechanical (QM) properties, predicting quantities such as dipole moments and polarizabilities, di…

physics.chem-ph2023★ 1 cited

Electronic excited states from physically-constrained machine learning

Edoardo Cignoni, Divya Suman, Jigyasa Nigam +3

Data-driven techniques are increasingly used to replace electronic-structure calculations of matter. In this context, a relevant question is whether machine learning (ML) should be…