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Yuan T, Song X, Shi Y, Wei S, Han Y, Yang L, Zhang Y, Li X, Li Y, Shen L, Fan L. Perspectives on development of optoelectronic materials in artificial intelligence age. Chem Asian J 2024:e202301088. [PMID: 38317532 DOI: 10.1002/asia.202301088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Revised: 01/28/2024] [Accepted: 02/05/2024] [Indexed: 02/07/2024]
Abstract
Optoelectronic devices, such as light-emitting diodes, have been demonstrated as one of the most demanded forthcoming display and lighting technologies because of their low cost, low power consumption, high brightness, and high contrast. The improvement of device performance relies on advances in precisely designing novelty functional materials, including light-emitting materials, hosts, hole/electron transport materials, and yet which is a time-consuming, laborious and resource-intensive task. Recently, machine learning (ML) has shown great prospects to accelerate material discovery and property enhancement. This review will summarize the workflow of ML in optoelectronic materials discovery, including data collection, feature engineering, model selection, model evaluation and model application. We highlight multiple recent applications of machine-learned potentials in various optoelectronic functional materials, ranging from semiconductor quantum dots (QDs) or perovskite QDs, organic molecules to carbon-based nanomaterials. We furthermore discuss the current challenges to fully realize the potential of ML-assisted materials design for optoelectronics applications. It is anticipated that this review will provide critical insights to inspire new exciting discoveries on ML-guided of high-performance optoelectronic devices with a combined effort from different disciplines.
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Affiliation(s)
- Ting Yuan
- College of Chemistry, Key Laboratory of Theoretical & Computational Photochemistry of Ministry of Education, Beijing Normal University, Beijing, 100875, China
| | - Xianzhi Song
- College of Chemistry, Key Laboratory of Theoretical & Computational Photochemistry of Ministry of Education, Beijing Normal University, Beijing, 100875, China
| | - Yuxin Shi
- College of Chemistry, Key Laboratory of Theoretical & Computational Photochemistry of Ministry of Education, Beijing Normal University, Beijing, 100875, China
| | - Shuyan Wei
- College of Chemistry, Key Laboratory of Theoretical & Computational Photochemistry of Ministry of Education, Beijing Normal University, Beijing, 100875, China
| | - Yuyi Han
- College of Chemistry, Key Laboratory of Theoretical & Computational Photochemistry of Ministry of Education, Beijing Normal University, Beijing, 100875, China
| | - Linjuan Yang
- College of Chemistry, Key Laboratory of Theoretical & Computational Photochemistry of Ministry of Education, Beijing Normal University, Beijing, 100875, China
| | - Yang Zhang
- College of Chemistry, Key Laboratory of Theoretical & Computational Photochemistry of Ministry of Education, Beijing Normal University, Beijing, 100875, China
| | - Xiaohong Li
- College of Chemistry, Key Laboratory of Theoretical & Computational Photochemistry of Ministry of Education, Beijing Normal University, Beijing, 100875, China
| | - Yunchao Li
- College of Chemistry, Key Laboratory of Theoretical & Computational Photochemistry of Ministry of Education, Beijing Normal University, Beijing, 100875, China
| | - Lin Shen
- College of Chemistry, Key Laboratory of Theoretical & Computational Photochemistry of Ministry of Education, Beijing Normal University, Beijing, 100875, China
| | - Louzhen Fan
- College of Chemistry, Key Laboratory of Theoretical & Computational Photochemistry of Ministry of Education, Beijing Normal University, Beijing, 100875, China
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Yang C, Chong X, Hu M, Yu W, He J, Zhang Y, Feng J, Zhou Y, Wang LW. Accelerating the Discovery of Hybrid Perovskites with Targeted Band Gaps via Interpretable Machine Learning. ACS APPLIED MATERIALS & INTERFACES 2023; 15:40419-40427. [PMID: 37594363 DOI: 10.1021/acsami.3c06392] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/19/2023]
Abstract
The band gap of hybrid organic-inorganic perovskites (HOIPs) is a key factor affecting the light absorption characteristics and thus the performance of perovskite solar cells (PSCs). However, band gap engineering, using experimental trial and error and high-throughput density functional theory calculations, is blind and costly. Therefore, it is critical to statistically identify the multiple factors influencing band gaps and to rationally design perovskites with targeted band gaps. This study combined feature engineering, the gradient-boosted regression tree (GBRT) algorithm, and the genetic algorithm-based symbolic regression (GASR) algorithm to develop an interpretable machine learning (ML) strategy for predicting the band gap of HOIPs accurately and quantitatively interpreting the factors affecting the band gap. Seven best physical features were selected to construct a GBRT model with a root-mean-square error of less than 0.060 eV, and the most important feature is the electronegativity difference between the B-site and the X-site (χB-X). Further, a mathematical formula (Eg = χB-X2 + 0.881χB-X) was constructed with GASR for a quantitative interpretation of the band gap influence patterns. According to the ML model, the HOIP MA0.23FA0.02Cs0.75Pb0.59Sn0.41Br0.24I2.76 was obtained, with a suitable band gap of 1.39 eV. Our proposed interpretable ML strategy provides an effective approach for developing HOIP structures with targeted band gaps, which can also be applied to other material fields.
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Affiliation(s)
- Chao Yang
- Faculty of Materials Science and Engineering, Kunming University of Science and Technology, Kunming 650093, China
| | - Xiaoyu Chong
- Faculty of Materials Science and Engineering, Kunming University of Science and Technology, Kunming 650093, China
| | - Mingyu Hu
- Department of Physics, Hong Kong Baptist University, Kowloon, Hong Kong 999077, China
| | - Wei Yu
- Faculty of Materials Science and Engineering, Kunming University of Science and Technology, Kunming 650093, China
| | - Jingjin He
- Faculty of Materials Science and Engineering, Kunming University of Science and Technology, Kunming 650093, China
| | - Yalan Zhang
- Department of Physics, Hong Kong Baptist University, Kowloon, Hong Kong 999077, China
| | - Jing Feng
- Faculty of Materials Science and Engineering, Kunming University of Science and Technology, Kunming 650093, China
| | - Yuanyuan Zhou
- Department of Physics, Hong Kong Baptist University, Kowloon, Hong Kong 999077, China
| | - Lin-Wang Wang
- Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, P. R. China
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Wu L, Dong C, Chen C, Zhao L, Lu P, Yang K. Interface Engineering at Sc 2C MXene and Germanium Iodine Perovskite Interface: First-Principles Insights. J Phys Chem Lett 2022; 13:11801-11810. [PMID: 36519799 DOI: 10.1021/acs.jpclett.2c03478] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
In recent years, Ge-based halide perovskite has gained increasing attention due to its potential in the development of lead-free perovskite solar cells. Here, through first-principles calculations, we explored the possibilities to enhance the optoelectronic properties of Ge-based perovskites via interfacial engineering between germanium iodine perovskite and 2D scandium-carbide MXene with various termination groups including F, O, and OH. We first evaluated the relative stability of the material interfaces and found that MAI-terminated interfaces are energetically more favorable than the GeI2-terminated interfaces. The MAI/F interface exhibits a type-II band alignment that can promote the photogenerated electron-hole separation. Moreover, the work function of the heterostructures can be tuned from 2.60 to 4.45 eV via using various termination groups. Additionally, 2D Sc2C MXene can also significantly enhance the light absorption. These results indicate that the 2D MXene serves as one promising candidate for optimizing the properties of perovskite solar cells via interface engineering.
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Affiliation(s)
- Liyuan Wu
- CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, 100049, China
| | - Chao Dong
- State Key Laboratory of Information Photonics and Optical Communications, Beijing University of Posts and Telecommunications, Beijing, 100876, China
| | - Changcheng Chen
- School of Science, Xi'an University of Architecture and Technology, Xi'an710055, ShaanxiChina
| | - Lina Zhao
- CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, 100049, China
| | - Pengfei Lu
- State Key Laboratory of Information Photonics and Optical Communications, Beijing University of Posts and Telecommunications, Beijing, 100876, China
| | - Kesong Yang
- Department of NanoEngineering and Program of Chemical Engineering, University of California San Diego, 9500 Gilman Drive, Mail Code 0448, La Jolla, California92093-0448, USA
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Park JG, Park SW, Hong KH. High-throughput screening of perovskite inspired bismuth halide materials: toward lead-free photovoltaic cells and light-emitting diodes. NANOTECHNOLOGY 2022; 33:485706. [PMID: 35952474 DOI: 10.1088/1361-6528/ac88db] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Accepted: 08/10/2022] [Indexed: 06/15/2023]
Abstract
Toxicity is the main bottleneck for the commercialization of Pb halide perovskites. Bi has been considered a promising metal cation to replace Pb because of its comparable electronic structures with Pb and better stability. Although experimental and theoretical studies have proposed various Bi-based halides, the present achievements in photovoltaic cells and other photoelectronic device fields do not compete with Pb analogs. Thermodynamic stability, bandgap control, and enhancement of carrier transport are fundamental challenges in the context of intrinsic material properties for developing highly efficient Bi-based devices. This study evaluates the potential of Bi-based halide compounds with good stability and electronic properties through high-throughput density functional theory calculations. Lattice structures and compositions are selected based on previous reports and an open material database. Then, we expanded our dataset to cover all possible compositional variations of A- and X-sites and alloying to B-sites. We examined over six-hundred candidates and found ten new candidates that have not been reported previously. Rb3SbBiI9exhibits the best-expected efficiency for high-efficiency solar cells among selected compounds, and other compounds can be used as visible-light-generation sources. Analysis of the screening procedure revealed that vacancy-ordered (A3B2X9)-type Bi-halides exhibit significantly favorable characteristics when compared with those of double perovskites and rudorffite-like structures for Bi-based photoelectronic devices.
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Affiliation(s)
- Jong-Goo Park
- Department of Materials Science and Engineering, Hanbat National University, 125 Dongseo-daero, Yuseong-Gu, Daejeon, 34158, Republic of Korea
| | - Sang Woo Park
- Department of Materials Science and Engineering, Hanbat National University, 125 Dongseo-daero, Yuseong-Gu, Daejeon, 34158, Republic of Korea
| | - Ki-Ha Hong
- Department of Materials Science and Engineering, Hanbat National University, 125 Dongseo-daero, Yuseong-Gu, Daejeon, 34158, Republic of Korea
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Luo S, Xing B, Faizan M, Xie J, Zhou K, Zhao R, Li T, Wang X, Fu Y, He X, Lv J, Zhang L. Inorganic Crystal Structure Prototype Database Based on Unsupervised Learning of Local Atomic Environments. J Phys Chem A 2022; 126:4300-4312. [PMID: 35732014 DOI: 10.1021/acs.jpca.2c03416] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Recognition of structure prototypes from tremendous known inorganic crystal structures has been an important subject beneficial for materials science research and new materials design. The existing databases of inorganic crystal structure prototypes were mostly constructed by classifying materials in terms of the crystallographic space group information. Herein, we employed a distinct strategy to construct the inorganic crystal structure prototype database, relying on the classification of materials in terms of local atomic environments (LAEs) accompanied by unsupervised machine learning method. Specifically, we adopted a hierarchical clustering approach onto all experimentally known inorganic crystal structure data to identify structure prototypes. The criterion for hierarchical clustering is the LAE represented by the state-of-the-art structure fingerprints of the improved bond-orientational order parameters and the smooth overlap of atomic positions. This allows us to build up a LAE-based Inorganic Crystal Structure Prototype Database (LAE-ICSPD) containing 15,613 structure prototypes with defined stoichiometries. In addition, we have developed a Structure Prototype Generator Infrastructure (SPGI) package, which is a useful toolkit for structure prototype generation. Our developed SPGI toolkit and LAE-ICSPD are beneficial for investigating inorganic materials in a global way as well as accelerating the materials discovery process in the data-driven mode.
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Affiliation(s)
- Shulin Luo
- State Key Laboratory of Integrated Optoelectronics, Key Laboratory of Automobile Materials of MOE, School of Materials Science and Engineering, and Jilin Provincial International Cooperation Key Laboratory of High-Efficiency Clean Energy Materials, Jilin University, Changchun 130012, China
| | - Bangyu Xing
- State Key Laboratory of Integrated Optoelectronics, Key Laboratory of Automobile Materials of MOE, School of Materials Science and Engineering, and Jilin Provincial International Cooperation Key Laboratory of High-Efficiency Clean Energy Materials, Jilin University, Changchun 130012, China
| | - Muhammad Faizan
- State Key Laboratory of Integrated Optoelectronics, Key Laboratory of Automobile Materials of MOE, School of Materials Science and Engineering, and Jilin Provincial International Cooperation Key Laboratory of High-Efficiency Clean Energy Materials, Jilin University, Changchun 130012, China
| | - Jiahao Xie
- State Key Laboratory of Integrated Optoelectronics, Key Laboratory of Automobile Materials of MOE, School of Materials Science and Engineering, and Jilin Provincial International Cooperation Key Laboratory of High-Efficiency Clean Energy Materials, Jilin University, Changchun 130012, China
| | - Kun Zhou
- State Key Laboratory of Integrated Optoelectronics, Key Laboratory of Automobile Materials of MOE, School of Materials Science and Engineering, and Jilin Provincial International Cooperation Key Laboratory of High-Efficiency Clean Energy Materials, Jilin University, Changchun 130012, China
| | - Ruoting Zhao
- State Key Laboratory of Integrated Optoelectronics, Key Laboratory of Automobile Materials of MOE, School of Materials Science and Engineering, and Jilin Provincial International Cooperation Key Laboratory of High-Efficiency Clean Energy Materials, Jilin University, Changchun 130012, China
| | - Tianshu Li
- State Key Laboratory of Integrated Optoelectronics, Key Laboratory of Automobile Materials of MOE, School of Materials Science and Engineering, and Jilin Provincial International Cooperation Key Laboratory of High-Efficiency Clean Energy Materials, Jilin University, Changchun 130012, China
| | - Xinjiang Wang
- State Key Laboratory of Superhard Materials, College of Physics, Jilin University, Changchun 130012, China
| | - Yuhao Fu
- State Key Laboratory of Superhard Materials, College of Physics, Jilin University, Changchun 130012, China.,International Center of Computational Method and Software, Jilin University, Changchun 130012, China
| | - Xin He
- State Key Laboratory of Integrated Optoelectronics, Key Laboratory of Automobile Materials of MOE, School of Materials Science and Engineering, and Jilin Provincial International Cooperation Key Laboratory of High-Efficiency Clean Energy Materials, Jilin University, Changchun 130012, China
| | - Jian Lv
- State Key Laboratory of Superhard Materials, College of Physics, Jilin University, Changchun 130012, China.,International Center of Computational Method and Software, Jilin University, Changchun 130012, China
| | - Lijun Zhang
- State Key Laboratory of Integrated Optoelectronics, Key Laboratory of Automobile Materials of MOE, School of Materials Science and Engineering, and Jilin Provincial International Cooperation Key Laboratory of High-Efficiency Clean Energy Materials, Jilin University, Changchun 130012, China.,International Center of Computational Method and Software, Jilin University, Changchun 130012, China
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Wang Z, Han Y, Lin X, Cai J, Wu S, Li J. An Ensemble Learning Platform for the Large-Scale Exploration of New Double Perovskites. ACS APPLIED MATERIALS & INTERFACES 2022; 14:717-725. [PMID: 34967594 DOI: 10.1021/acsami.1c18477] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Lead-free double perovskites are regarded as stable and green optoelectronic alternatives to single perovskites, but may exhibit indirect band gaps and high effective masses, thus limiting their maximum photovoltaic efficiency. Considering that the trial-and-error experimental and computational approaches cannot quickly identify ideal candidates, we propose an ensemble learning workflow to screen all suitable double perovskites from the periodic table, with a high predictive accuracy of 92% and a computed speed that is ∼108 faster than ab initio calculations. From ∼23 314 unexplored double perovskites, we successfully identify six candidates that exhibit suitable band gaps (1.0-2.0 eV), where two have direct band gaps and low effective masses. They all show good thermal stabilities that are hopefully able to be synthesized. The proposed ML workflow immensely shortens the screening cycle for double perovskites, which will greatly promote the development and application of photovoltaic devices.
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Affiliation(s)
- Zhilong Wang
- National Key Laboratory of Science and Technology on Micro/Nano Fabrication, Shanghai Jiao Tong University, Shanghai 200240, China
- Key Laboratory of Thin Film and Microfabrication Technology, Ministry of Education, Department of Micro/Nano-electronics, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Yanqiang Han
- National Key Laboratory of Science and Technology on Micro/Nano Fabrication, Shanghai Jiao Tong University, Shanghai 200240, China
- Key Laboratory of Thin Film and Microfabrication Technology, Ministry of Education, Department of Micro/Nano-electronics, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Xirong Lin
- National Key Laboratory of Science and Technology on Micro/Nano Fabrication, Shanghai Jiao Tong University, Shanghai 200240, China
- Key Laboratory of Thin Film and Microfabrication Technology, Ministry of Education, Department of Micro/Nano-electronics, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Junfei Cai
- National Key Laboratory of Science and Technology on Micro/Nano Fabrication, Shanghai Jiao Tong University, Shanghai 200240, China
- Key Laboratory of Thin Film and Microfabrication Technology, Ministry of Education, Department of Micro/Nano-electronics, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Sicheng Wu
- National Key Laboratory of Science and Technology on Micro/Nano Fabrication, Shanghai Jiao Tong University, Shanghai 200240, China
- Key Laboratory of Thin Film and Microfabrication Technology, Ministry of Education, Department of Micro/Nano-electronics, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Jinjin Li
- National Key Laboratory of Science and Technology on Micro/Nano Fabrication, Shanghai Jiao Tong University, Shanghai 200240, China
- Key Laboratory of Thin Film and Microfabrication Technology, Ministry of Education, Department of Micro/Nano-electronics, Shanghai Jiao Tong University, Shanghai 200240, China
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