A Machine Learning Study on High Thermal Conductivity Assisted to Discover Chalcogenides with Balanced Infrared Nonlinear Optical Performance.

Exploration of novel nonlinear optical (NLO) chalcogenides with high laser damage thresholds (LIDT) are critical for mid-infrared (mid-IR) solid-state laser applications. High lattice thermal conductivity (κ L ) is crucial to increasing LIDT yet often neglected in the search for NLO crystals due to lack of accurate κ L data. We hereby propose a machine learning (ML) approach to predict κ L for over 6000 chalcogenides. Combining ML-generated κ L data and first-principles calculation, a high-throughput screening route is initiated, and 10 new potential mid-IR NLO chalcogenides with optimal band gap, NLO coefficients and thermal conductivity are discovered, in which Li 2 SiS 3 and AlZnGaS 4 are highlighted. Big-data analysis on structural chemistry proves that the chalcogenides having dense and simple lattice structures with low anisotropy, light atoms and strong covalent bonds are likely to possess higher κ L . The four-coordinated motifs in which central cations show the bond valence sum of +2∼ +3 and are from IIIA, IVA, VA and IIB groups, such as those in diamond-like defect-chalcopyrite chalcogenides, are preferred to fulfill the desired structural chemistry conditions for balanced NLO and thermal properties. Our work provides not only an efficient strategy but also interpretable research directions in the search for NLO crystals with high thermal conductivity. This article is protected by copyright. All rights reserved.