A dual-cutoff machine-learned potential for condensed organic systems obtained via uncertainty-guided active learning
Year: 2024
Type: article
Abstract: We train a machine-learned potential using a dual-cutoff architecture to capture different interaction length scales, obtaining good accuracy in densities and heat capacity, among other, compared to experiment and first-principles simulations.
DOI: 10.1039/d4cp01980f
Institutions: Materials Design (France), BP (United Kingdom), BP (United States)
Language: English
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: green
Award: G1338859120
OpenAlex ID: W4401385147