ERC Consolidator Grant · 2020
Fully Integrating Atomistic Modeling with Machine Learning
Computer simulations of molecules and materials are undergoing a profound transformation. Machine learning (ML) has become essential to extend the reach and increase the predictive power of atomic-scale modeling. The potential of ML in association with quantum mechanical (QM) and statistical mechanical (SM) methods has been shown, but the link has been rather superficial, due to the complex, interdisciplinary effort needed to combine the three approaches. Without full convergence, ML-powered simulations cannot address modern modeling challenges, which involve complex materials in realistic conditions, and require increasingly predictive accuracy. The objective of this project is to create a…
From the public funding record at EU CORDIS. Describes the funded project, not the reviews below.
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