Darwin4Matter: A Platform Integrating Machine Learning and Quantum Chemistry for New Materials Design

Abstract

Machine learning (ML) has emerged to play a more and more important role in material science. Here, we develop a platform named Darwin4Matter that integrates machine learning and quantum chemistry for new materials discovery. The framework consists of six steps: quantum chemistry prediction, Δ-machine learning correction, molecular augmentation, machine learning prediction, molecular production, and verification. Using this platform, we start from a very small dataset and design three new functional molecules with high refractive indexes in the visible spectrum, which serves as the capping layer of organic light-emitting diode devices for improving light extraction efficiency. The superior performance over currently used materials has been verified experimentally, exhibiting significant commercial value in the field of advanced display materials.

Keywords

Data-driven materials design / Machine learning / Quantum chemistry / Organic light-emitting diode

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RIGHTS & PERMISSIONS

Jilin University, The Editorial Department of Chemical Research in Chinese Universities and Springer-Verlag GmbH