PERFORMANCE PREDICTION OF HALIDE DOUBLE PEROVSKITE MATERIALS BASED ON MACHINE LEARNING

Zhang Qixin; Xu Zhangyang; Feng Ping; Tu Jielei

doi:10.19912/j.0254-0096.tynxb.2023-1794

PDF(5616 KB)

Acta Energiae Solaris Sinica ›› 2024, Vol. 45 ›› Issue (4) : 107-115. DOI: 10.19912/j.0254-0096.tynxb.2023-1794

PERFORMANCE PREDICTION OF HALIDE DOUBLE PEROVSKITE MATERIALS BASED ON MACHINE LEARNING

Zhang Qixin, Xu Zhangyang, Feng Ping, Tu Jielei

Author information +

History +

Abstract

Taking halide double perovskite materials as the research object, the machine learning method is used to predict the band gap and relative stability of halide double perovskite materials with high speed and high accuracy. Four distinct algorithms, namely Bayesian ridge regression, gradient boosting regression, support vector regression, and XGBoost, are employed to construct predictive models. The results show that gradient boosting regression can provide the highest performance prediction for relative stability （R²=0.9161, MAE=0.2061）, XGBoost can provide the highest performance prediction for band gap （R²=0.9899, MAE=0.0542）, and after using the SHAP method to explain the model, the new samples after element substitution are screened, and finally 18 halide double perovskites with ideal light absorption range and exceptional stability are obtained. These outcomes indicate that compared with traditional methods, data-driven machine learning can effectively accelerate functiona material discovery and improve design efficiency .

Key words

halide double perovskite / machine learning / feature engineering / material discovery / solar cells

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

Zhang Qixin, Xu Zhangyang, Feng Ping, Tu Jielei. PERFORMANCE PREDICTION OF HALIDE DOUBLE PEROVSKITE MATERIALS BASED ON MACHINE LEARNING[J]. Acta Energiae Solaris Sinica. 2024, 45(4): 107-115 https://doi.org/10.19912/j.0254-0096.tynxb.2023-1794

References

[1] WANG J Y, XU P C, JI X B, et al.Feature selection in machine learning for perovskite materials design and discovery[J]. Materials, 2023, 16(8): 3134.
[2] LIU Z Y, YANG H J, WANG J Y, et al.Synthesis of lead-free Cs₂AgBiX₆ (X=Cl, Br, I) double perovskite nanoplatelets and their application in CO₂ photocatalytic reduction[J]. Nano letters, 2021, 21(4): 1620-1627.
[3] FORTKAMP F P, NAKASHIMA A T D, DOS SANTOS V M A, et al. Computationally-efficient optimization of the remanence angles of permanent magnet circuits for magnetic refrigeration[J]. Journal of magnetism and magnetic materials, 2023, 569: 170429.
[4] IVANOV N, ZHURAVLEV S, ZANEGIN S, et al.Calculation, design, and winding preliminary tests of 90 kW HTS machine for small-scale demonstrator of generating system for future aircraft with hybrid propulsion system[J]. IEEE transactions on applied superconductivity, 2023, 33(2): 5200105.
[5] WEI J, CHU X, SUN X Y, et al.Machine learning in materials science[J]. InfoMat, 2019, 1(3): 338-358.
[6] LU S H, ZHOU Q H, GUO Y L, et al.On-the-fly interpretable machine learning for rapid discovery of two-dimensional ferromagnets with high curie temperature[J]. Chem, 2022, 8(3): 769-783.
[7] WAN X Y, ZHANG Y H, LU S H, et al.Machine learning accelerated search for new double perovskite oxide photocatalysis[J]. Acta physica sinica, 2022, 71(17): 177101.
[8] NISHIMURA S, LE S D, MIYAZATO I, et al.High-throughput screening and literature data-driven machine learning-assisted investigation of multi-component La₂O₃-based catalysts for the oxidative coupling of methane[J]. Catalysis science & technology, 2022, 12(9): 2766-2774.
[9] MORELOCK R J, BARE Z J L, MUSGRAVE C B. Bond-valence parameterization for the accurate description of DFT energetics[J]. Journal of chemical theory and computation, 2022, 18(5): 3257-3267.
[10] GUO Z M, LIN B.Machine learning stability and band gap of lead-free halide double perovskite materials for perovskite solar cells[J]. Solar energy, 2021, 228: 689-699.
[11] KIM Y, KIM E, ANTONO E, et al.Machine-learned metrics for predicting the likelihood of success in materials discovery[J]. NPJ computational materials, 2020, 6: 131.
[12] 王妮妮, 马萍, 张宏立, 等. 基于多尺度深度卷积网络特征融合的滚动轴承故障诊断[J]. 太阳能学报, 2022, 43(4): 351-358.
WANG N N, MA P, ZHANG H L,et al.Fault diagnosis of rolling bearing based on feature fusion of multi- scale deep convolutional network[J]. Acta energiae solaris sinica, 2022, 43(4): 351-358.
[13] KONG Q Z, HE C, LIAO L J, et al.Hyperparameter optimization for interfacial bond strength prediction between fiber-reinforced polymer and concrete[J]. Structures, 2023, 51: 573-601.
[14] MARAPELLI B, CARIE A, ISLAM S M N. Software effort estimation with use case points using ensemble machine learning models[C]//2021 International Conference on Electrical, Computer and Energy Technologies(ICECET).Cape Town, South Africa, 2021: 333-338.
[15] ZHA L L, MA K, LI G Q, et al.An improved extreme learning machine with self-recurrent hidden layer[J]. Advanced engineering informatics, 2022, 54: 101736.
[16] MIENYE I D, SUN Y X.A survey of ensemble learning: concepts, algorithms, applications, and prospects[J]. IEEE ACCESS, 2022, 10: 99129-99149.
[17] ZHENG W D, CHENG H, LIU Y R, et. al. Machine learning for imbalanced datasets: application in prediction of 3d-5d double perovskite structures[J]. Computational materials science, 2022, 209: 111394.
[18] ZHI C Y, WANG S, SUN S J, et al.Machine-learning-assisted screening of interface passivation materials for perovskite solar cells[J]. ACS energy letters, 2023, 8(3): 1424-1433.
[19] HAO J G, HO T K.Machine learning made easy: a review of scikit-learn package in python programming language[J]. Journal of educational and behavioral statistics, 2019, 44(3): 348-361.
[20] SUCHOPAROVA G, NERUDA R.Genens: an AutoML system for ensemble optimization based on developmental genetic programming[C]//2020 IEEE Symposium Series on Computational Intelligence (SSCI). Canberra, ACT, Australia, 2020: 631-638.
[21] LU S H, ZHOU Q H, OUYANG Y X, et al.Accelerated discovery of stable lead-free hybrid organic-inorganic perovskites via machine learning[J]. Nature communications, 2018, 9: 3405.
[22] ZHANG L, LI N.Material analysis and big data monitoring of sports training equipment based on machine learning algorithm[J]. Neural computing and applications, 2022, 34(4): 2749-2763.
[23] PAN Z, ZHOU Y G, ZHANG L.Photoelectrochemical properties, machine learning, and symbolic regression for molecularly engineered halide perovskite materials in water[J]. ACS applied materials & interfaces, 2022, 14(7): 9933-9943.
[24] 胡扬, 张胜利, 周文瀚, 等. 基于机器学习探索钙钛矿材料及其应用[J]. 硅酸盐学报, 2023, 51(2): 452-468.
HU Y, ZHANG S L, ZHOU W H, et al.Studies on perovskite material and its applications via machine learning[J]. Journal of the Chinese Ceramic Society, 2023, 51(2): 452-468.
[25] CHOUBISA H, TODOROVIĆ P, PINA J M, et al.Interpretable discovery of semiconductors with machine learning[J]. NPJ computational materials, 2023, 9: 117.
[26] HUANG Y R, LI S Y, ZHANG L.Accelerated multisolvent prediction for aqueous stable halide perovskite materials[J]. ACS applied materials & interfaces, 2023, 15(41): 48771-48784.
[27] HU W G, ZHANG L.High-throughput calculation and machine learning of two-dimensional halide perovskite materials: formation energy and band gap[J]. Materials today communications, 2023, 35: 105841.
[28] OBADA D O, OKAFOR E, ABOLADE S A, et al.Explainable machine learning for predicting the band gaps of ABX₃ perovskites[J]. Materials science in semiconductor processing, 2023, 161: 107427.
[29] KIM S, JEONG Y, HAN D W, et.al. Machine learning-assisted defect analysis and optimization for P-I-N-structured perovskite solar cells[J]. Journal of electronic materials, 2023, 52(9): 5861-5871.
[30] LI Z Z, XU Q C, SUN Q D, et.al. Thermodynamic stability landscape of halide double perovskites via high-throughput computing and machine learning[J]. Advanced functional materials, 2019, 29(9): 1807280.