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

## TL;DR

A new protocol called HiDiscover helps uncover atomic and molecular arrangements in complex materials, improving understanding of their microscopic mechanisms.

## Contribution

The novel contribution is the hierarchical incremental learning protocol HiDiscover for systematic mechanistic exploration in multi-component materials.

## Key findings

- HiDiscover enables detailed differentiation and extraction of ionic and molecular arrangements in complex systems.
- The protocol reveals quantitative microscopic features not easily discernible through conventional simulations.
- It improves reliability of mechanistic descriptions across three different material classes and processes.

## 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 HiDiscover protocol enables the detailed differentiation and facile extraction of ionic and molecular arrangements, and reveals quantitative microscopic features that are difficult to discern through conventional molecular simulations, thereby informing materials design. Our approach is seen to improve the reliability of mechanistic descriptions for three different processes in three different classes of materials.

Understanding molecular arrangements in mechanistic studies of complex materials is critical but challenging. Here, the authors develop a hierarchical incremental learning protocol to uncover microscopic patterns, improving mechanistic insights obtained