A Machine Learning Study on High Thermal Conductivity Assisted to Discover Chalcogenides with Balanced Infrared Nonlinear Optical Performance
At a Glance
Section titled âAt a Glanceâ| Metadata | Details |
|---|---|
| Publication Date | 2023-11-06 |
| Journal | Advanced Materials |
| Authors | Qingchen Wu, Lei Kang, Zheshuai Lin |
| Institutions | University of Chinese Academy of Sciences, Technical Institute of Physics and Chemistry |
| Citations | 34 |
Abstract
Section titled âAbstractâAbstract Exploration of novel nonlinear optical (NLO) chalcogenides with high laserâinduced damage thresholds (LIDT) is critical for midâinfrared (midâIR) solidâstate laser applications. High lattice thermal conductivity ( Îș L ) is crucial to increasing LIDT yet often neglected in the search for NLO crystals due to lack of accurate Îș L data. A machine learning (ML) approach to predict Îș L for over 6000 chalcogenides is hereby proposed. Combining MLâgenerated Îș L data and firstâprinciples calculation, a highâthroughput screening route is initiated, and ten new potential midâIR NLO chalcogenides with optimal bandgap, NLO coefficients, and thermal conductivity are discovered, in which Li 2 SiS 3 and AlZnGaS 4 are highlighted. Bigâdata 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 Îș L . The fourâcoordinated 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 diamondâlike defectâchalcopyrite 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.