6 papers
Transferable machine learning of excited-state dynamics with extremal pooling
Cesare Malosso, Wei Bin How, Gonzalo DÃaz 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…
Comparing the latent features of universal machine-learning interatomic potentials
Sofiia Chorna, Davide Tisi, Cesare Malosso +3
The past few years have seen the development of ``universal'' machine-learning interatomic potentials (uMLIPs) capable of approximating the ground-state potential energy surface ac…
High-quality, high-information datasets for universal atomistic machine learning
Cesare Malosso, Filippo Bigi, Paolo Pegolo +5
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…
The Photochemical Birth of the Hydrated Electron in Liquid Water
Gonzalo DÃaz Mirón, Cesare Malosso, Solana Di Pino +4
The photophysics and photochemistry associated with irradiating UV light in liquid water is central to numerous physical, chemical and biological processes. One of the key events i…
Dynamical Heterogeneity in Supercooled Water and its Spectroscopic Fingerprints
Cesare Malosso, Edward Danquah Donkor, Stefano Baroni +1
A growing body of theoretical and experimental evidence strongly supports the existence of a second liquid-liquid critical point (LLCP) in deeply supercooled water leading to the c…