Hierarchical incremental learning deciphers molecular arrangements in multi-component materials.

ABSTRACT: Identifying meaningful patterns of atomic and molecular arrangements from molecular simulations is crucial for revealing microscopic mechanisms in materials. Unraveling these patterns is challenging for the multi-component systems frequently encountered in advanced materials, energy and environmental applications. This limits the understanding of the microscopic mechanisms that ultimately govern the performance of devices based on these systems. Here, we propose a hierarchical incremental learning research protocol named HiDiscover to systematically expedite the mechanistic exploration in multi-component materials. As illustrations, we study Li-ion transport and gas adsorption in nanoporous framework materials, as well as molecular packing in organic active layers for photovoltaics. The Hi

SUBMITTER: Zhang H 

PROVIDER: S-EPMC12546701 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature