63 citations · 80 across the 9 of their papers we have counts for
4 papers · 1 filter
Hierarchical universal open structure dataset for atomistic modeling
Sofiia Chorna, Cesare Malosso, Jonathan Schmidt +1
Atomic-scale simulations have been transformed by machine-learning models that predict the properties of atomistic structures with the accuracy of quantum calculations, but at a sm…
Transferable machine learning of excited-state dynamics with extremal pooling
Cesare Malosso, Wei Bin How, Gonzalo Díaz Mirón +2
Photochemical processes govern phenomena ranging from solar energy conversion and atmospheric chemistry to vision and photosynthesis. Accurate simulation of these processes require…
Beyond the Virial Expansion: Microscopic Origins of Partial Molar Volumes in LiCl Solutions
Chun-Ting Lin, Diganta Dasgupta, Tinglu Yang +6
Although electrolyte density measurements have been reported for over a century, employing them to obtain accurate partial molar volume (PMV) profiles as a function of salt concent…
High-quality, high-information datasets for universal atomistic machine learning
Cesare Malosso, Filippo Bigi, Paolo Pegolo +7
The quality, consistency, and information content of training data is often what determines the practical value of machine-learning models for atomistic simulations. Yet, many wide…