1 citations · 1 across the 2 of their papers we have counts for
2 papers
physics.comp-ph2024
Expanding Density-Correlation Machine Learning Representations for Anisotropic Coarse-Grained Particles
Arthur Y. Lin, Kevin K. Huguenin-Dumittan, Yong-Cheol Cho +2
Physics-based, atom-centered machine learning (ML) representations have been instrumental to the effective integration of ML within the atomistic simulation community. Many of thes…
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…