Record #242254 - GSI Repository

FIAMMA

Fully Integrating Atomistic Modeling with Machine Learning

Grant period	2022-01-01 - 2026-12-31
Funding body	European Union
Call number	ERC-2020-COG
Grant number	101001890
Identifier	G:(EU-Grant)101001890

Note: 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 “plug and play” framework by which ML can be seamlessly combined with physics-based modeling, substituting individual steps of a QM calculation, or making direct predictions across complex SM workflows. Full integration of the three approaches will greatly extend the reach of atomistic simulations, and allow an insightful critical comparison of the role of inductive and deductive paradigms in theory and modeling. The development of an open-source software that unifies QM, SM and ML shall facilitate early adoption and broaden impact. We will demonstrate the benefits of our integrated framework through two challenging and compelling platform problems: (i) investigating stabilities and properties of flexible drug-like molecules and assemblies, and (ii) discovering fundamental structure-activity relationships of porous aluminosilicates for clean chemical technologies. Critical knowledge gaps that will be filled include: (1) the description of long-range physics within the same conceptual framework that has been used for short-range interactions; (2) the symmetry-adapted representation of input and outputs of each step of a QM calculation; and (3) the rigorous characterization of SM ensembles to enable end-to-end predictions of equilibrium properties with uncertainty quantification.

Recent Publications

There are no publications

Record created 2021-10-10, last modified 2023-02-08

Similar records

Add to personal basket
Export as Author List with IDs BibTeX (UTF-8), EndNote XML, EndNote Text, RIS, MARC, Print MARC, MARCXML, DC,
Request correction
Submit fulltext

GSI Homepage \| GSI Library \| English Deutsch \| guest :: login GSI Repository
Search	Submit	Personalize Your alerts Your baskets Your searches	Help