Towards a fast machine-learning-assisted prediction of the mechanoelectric response in organic crystals

Abstract

Organic semiconductors can improve the performance of wearable electronics, e-skins, and pressure sensors by exploiting their mechanoelectric response. However, identifying new materials for these applications is challenging due to the lack of fast and reliable computational protocols, whose major limitation is the computational burden required to evaluate the relevant figures of merit from first principles. To overcome this challenge, we present a new protocol that combines molecular dynamics, density functional theory, machine learning, and kinetic Monte Carlo simulations. The fast machine learning model enables the evaluation of millions of specific electronic interactions between molecules and their thermal fluctuations, which play a key role in modulating charge transport. We use this protocol to study the dependence of charge mobility on mechanical deformations for C10-DNBDT-NW. Several analyses are performed to rationalise and predict the impact of strain on the material in a reduced amount of time. The predictions are consistent with experimental measurements, indicating its potential for screening the mechanoelectric response to identify materials with the desired properties. This new protocol presents an effective approach to predict the performance of organic semiconductors under external mechanical strain, which could lead to the discovery of new materials for advanced technologies.