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

14 citations · 38 across the 8 of their papers we have counts for

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cs.LG2026

How unconstrained machine-learning models learn physical symmetries

Michelangelo Domina, Joseph William Abbott, Paolo Pegolo +2

The requirement of generating predictions that exactly fulfill the fundamental symmetry of the corresponding physical quantities has profoundly shaped the development of machine-le…