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‐induced damage thresholds (LIDT) is critical for mid‐infrared (mid‐IR) solid‐state laser applications. High lattice thermal conductivity (κL) is crucial to increasing LIDT yet often neglected in the search for NLO crystals due to lack of accurate κL data. A machine learning (ML) approach to predict κL for over 6000 chalcogenides is hereby proposed. Combining ML‐generated κL data and first‐principles calculation, a high‐throughput screening route is initiated, and ten new potential mid‐IR NLO chalcogenides with optimal bandgap, NLO coefficients, and thermal conductivity are discovered, in which Li2SiS3 and AlZnGaS4 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 κL. The four‐coordinated motifs in which central cations show the bond valence sum of +2 to +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. This work provides not only an efficient strategy but also interpretable research directions in the search for NLO crystals with high thermal conductivity. A high‐throughput screening pipeline combining first‐principle calculation and machine learning is proposed, using lattice thermal conductivity predicted by machine learning for the first time as primary screening criteria to discover mid‐IR nonlinear optical (NLO) chalcogenides. Structural chemistry knowledge to balance thermal conducting and NLO‐related properties is extracted during the procedure. For application, two potential mid‐IR NLO materials with promising performances are identified.