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Third-generation photovoltaic materials, including metal halide perovskites (MHPs), colloidal quantum dots (QDs), copper zinc tin sulfide (CZTS), and organic semiconductors, among others, have become attractive in the past two decades. Unlike their first- and second-generation counterparts, these advanced materials boast properties beyond mere photovoltaic performance, such as mechanical flexibility, light weight, and cost-effectiveness. Meanwhile, these materials possess more intricate crystalline structures that aid in understanding and predicting their transport properties. In particular, the distinctive phonon dispersions in MHPs, the layered architecture in quasi-two-dimensional (2D) perovskites, the strong quantum confinement in QDs, and the complex crystal structures interspersed with abundant disorders in quaternary CZTS result in unique and sometimes anomalous thermal transport behaviors. Concurrently, the criticality of thermal management in applications such as photovoltaics, thermoelectrics, light emitting diodes, and photodetection devices has received increased recognition, considering that many of these third-generation photovoltaic materials are not good thermal conductors. Effective thermal management necessitates precise measurement, advanced modeling, and a profound understanding and interpretation of thermal transport properties in these novel materials. In this review, we provide a comprehensive summary of various techniques for measuring thermal transport properties of these materials and discuss the ultralow thermal conductivities of three-dimensional (3D) MHPs, superlattice-like thermal transport in 2D perovskites, and novel thermal transport characteristics inherent in QDs and CZTS. By collecting and comparing the literature-reported results, we offer a thorough discussion on the thermal transport phenomenon in these materials. The collective understanding from the literature in this area, as reviewed in this article, can provide guidance for improving thermal management across a wide spectrum of applications extending beyond photovoltaics.
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Machine-learning structural and electronic properties of metal halide perovskites using a hierarchical convolutional neural network
Abstract The development of statistical tools based on machine learning (ML) and deep networks is actively sought for materials design problems. While structure-property relationships can be accurately determined using quantum mechanical methods, these first-principles calculations are computationally demanding, limiting their use in screening a large set of candidate structures. Herein, we use convolutional neural networks to develop a predictive model for the electronic properties of metal halide perovskites (MHPs) that have a billions-range materials design space. We show that a well-designed hierarchical ML approach has a higher fidelity in predicting properties of the MHPs compared to straight-forward methods. In this architecture, each neural network element has a designated role in the estimation process from predicting complex features of the perovskites such as lattice constant and octahedral till angle to narrowing down possible ranges for the values of interest. Using the hierarchical ML scheme, the obtained root-mean-square errors for the lattice constants, octahedral angle and bandgap for the MHPs are 0.01 Å, 5°, and 0.02 eV, respectively. Our study underscores the importance of a careful network design and a hierarchical approach to alleviate issues associated with imbalanced dataset distributions, which is invariably common in materials datasets.
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- Award ID(s):
- 1809085
- PAR ID:
- 10418331
- Date Published:
- Journal Name:
- npj Computational Materials
- Volume:
- 6
- Issue:
- 1
- ISSN:
- 2057-3960
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.1038/s41524-020-0307-8
