Traditional materials discovery is in ‘trial-and-error’ mode, leading to the issues of low-efficiency, high-cost, and unsustainability in materials design. Meanwhile, numerous experimental and computational trials a...Traditional materials discovery is in ‘trial-and-error’ mode, leading to the issues of low-efficiency, high-cost, and unsustainability in materials design. Meanwhile, numerous experimental and computational trials accumulate enormous quantities of data with multi-dimensionality and complexity, which might bury critical ‘structure–properties’ rules yet unfortunately not well explored. Machine learning(ML), as a burgeoning approach in materials science, may dig out the hidden structure–properties relationship from materials bigdata, therefore, has recently garnered much attention in materials science. In this review, we try to shortly summarize recent research progress in this field, following the ML paradigm:(i) data acquisition →(ii) feature engineering →(iii) algorithm →(iv) ML model →(v) model evaluation →(vi) application. In section of application, we summarize recent work by following the ‘material science tetrahedron’:(i) structure and composition →(ii) property →(iii) synthesis →(iv) characterization, in order to reveal the quantitative structure–property relationship and provide inverse design countermeasures. In addition, the concurrent challenges encompassing data quality and quantity, model interpretability and generalizability, have also been discussed. This review intends to provide a preliminary overview of ML from basic algorithms to applications.展开更多
The figure of merit is of crucial importance in materials design to search for candidates with optimal functionality.In the field of photovoltaics,the bandgap(E_g)is a well-recognized figure of merit for screening sol...The figure of merit is of crucial importance in materials design to search for candidates with optimal functionality.In the field of photovoltaics,the bandgap(E_g)is a well-recognized figure of merit for screening solar cell absorbers subject to the Shockley-Queisser limit.In this paper,the bandgap as the figure of merit is challenged since an ideal solar cell absorber requires multiple criteria such as stability,optical absorption,and carrier lifetime.Multiple criteria make the quantitative description of material candidates difficult and computationally time-consuming.Taking halide perovskites as an example,we combine thermodynamic stability(ΔHd)and Eginto a unified figure of merit and use Bayesian optimization(BO)to accelerate materials screening.We have found that,in comparison to an exhaustive search via multiple parameters,BO based on the unified figure of merit can screen optimal candidates(E_g,PBEbetween 0.6–1.2 eV,ΔHd>-29 meV per atom)more efficiently.Therefore,the proposed method opens a viable route for the search of optimal solar cell absorbers from a large amount of material candidates with less computational cost.展开更多
基金Project support by the National Natural Science Foundation of China(Grant Nos.11674237 and 51602211)the National Key Research and Development Program of China(Grant No.2016YFB0700700)+1 种基金the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD),ChinaChina Post-doctoral Foundation(Grant No.7131705619).
文摘Traditional materials discovery is in ‘trial-and-error’ mode, leading to the issues of low-efficiency, high-cost, and unsustainability in materials design. Meanwhile, numerous experimental and computational trials accumulate enormous quantities of data with multi-dimensionality and complexity, which might bury critical ‘structure–properties’ rules yet unfortunately not well explored. Machine learning(ML), as a burgeoning approach in materials science, may dig out the hidden structure–properties relationship from materials bigdata, therefore, has recently garnered much attention in materials science. In this review, we try to shortly summarize recent research progress in this field, following the ML paradigm:(i) data acquisition →(ii) feature engineering →(iii) algorithm →(iv) ML model →(v) model evaluation →(vi) application. In section of application, we summarize recent work by following the ‘material science tetrahedron’:(i) structure and composition →(ii) property →(iii) synthesis →(iv) characterization, in order to reveal the quantitative structure–property relationship and provide inverse design countermeasures. In addition, the concurrent challenges encompassing data quality and quantity, model interpretability and generalizability, have also been discussed. This review intends to provide a preliminary overview of ML from basic algorithms to applications.
基金Yin WJ acknowledges funding support from the National Key Research and Development Program of China(2016YFB0700700)the National Natural Science Foundation of China(11974257,11674237 and 51602211)+1 种基金the Natural Science Foundation of Jiangsu Province of China(BK20160299)the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD).The theoretical work was carried out at the National Supercomputer Center in Tianjin and the calculations were performed on TianHe-l(A).
文摘The figure of merit is of crucial importance in materials design to search for candidates with optimal functionality.In the field of photovoltaics,the bandgap(E_g)is a well-recognized figure of merit for screening solar cell absorbers subject to the Shockley-Queisser limit.In this paper,the bandgap as the figure of merit is challenged since an ideal solar cell absorber requires multiple criteria such as stability,optical absorption,and carrier lifetime.Multiple criteria make the quantitative description of material candidates difficult and computationally time-consuming.Taking halide perovskites as an example,we combine thermodynamic stability(ΔHd)and Eginto a unified figure of merit and use Bayesian optimization(BO)to accelerate materials screening.We have found that,in comparison to an exhaustive search via multiple parameters,BO based on the unified figure of merit can screen optimal candidates(E_g,PBEbetween 0.6–1.2 eV,ΔHd>-29 meV per atom)more efficiently.Therefore,the proposed method opens a viable route for the search of optimal solar cell absorbers from a large amount of material candidates with less computational cost.
