期刊文献+
共找到3篇文章
< 1 >
每页显示 20 50 100
Identifying Pb-free perovskites for solar cells by machine learning 被引量:10
1
作者 Jino Im Seongwon Lee +3 位作者 Tae-Wook Ko Hyun Woo Kim YunKyong Hyon hyunju chang 《npj Computational Materials》 SCIE EI CSCD 2019年第1期828-835,共8页
Recent advances in computing power have enabled the generation of large datasets for materials,enabling data-driven approaches to problem-solving in materials science,including materials discovery.Machine learning is ... Recent advances in computing power have enabled the generation of large datasets for materials,enabling data-driven approaches to problem-solving in materials science,including materials discovery.Machine learning is a primary tool for manipulating such large datasets,predicting unknown material properties and uncovering relationships between structure and property.Among state-of-the-art machine learning algorithms,gradient-boosted regression trees(GBRT)are known to provide highly accurate predictions,as well as interpretable analysis based on the importance of features.Here,in a search for lead-free perovskites for use in solar cells,we applied the GBRT algorithm to a dataset of electronic structures for candidate halide double perovskites to predict heat of formation and bandgap.Statistical analysis of the selected features identifies design guidelines for the discovery of new lead-free perovskites. 展开更多
关键词 LEARNING PEROVSKITE COMPUTING
原文传递
A public database of thermoelectric materials and system-identified material representation for data-driven discovery 被引量:1
2
作者 Gyoung S.Na hyunju chang 《npj Computational Materials》 SCIE EI CSCD 2022年第1期2064-2074,共11页
Thermoelectric materials have received much attention as energy harvesting devices and power generators.However,discovering novel high-performance thermoelectric materials is challenging due to the structural diversit... Thermoelectric materials have received much attention as energy harvesting devices and power generators.However,discovering novel high-performance thermoelectric materials is challenging due to the structural diversity and complexity of the thermoelectric materials containing alloys and dopants.For the efficient data-driven discovery of novel thermoelectric materials,we constructed a public dataset that contains experimentally synthesized thermoelectric materials and their experimental thermoelectric properties.For the collected dataset,we were able to construct prediction models that achieved R^(2)-scores greater than 0.9 in the regression problems to predict the experimentally measured thermoelectric properties from the chemical compositions of the materials.Furthermore,we devised a material descriptor for the chemical compositions of the materials to improve the extrapolation capabilities of machine learning methods.Based on transfer learning with the proposed material descriptor,we significantly improved the R^(2)-score from 0.13 to 0.71 in predicting experimental ZTs of the materials from completely unexplored material groups. 展开更多
关键词 properties ALLOYS SYSTEM
原文传递
Predicting thermoelectric properties from chemical formula with explicitly identifying dopant effects
3
作者 Gyoung S.Na Seunghun Jang hyunju chang 《npj Computational Materials》 SCIE EI CSCD 2021年第1期964-974,共11页
Dopants play an important role in synthesizing materials to improve target materials properties or stabilize the materials.In particular,the dopants are essential to improve thermoelectic performances of the materials... Dopants play an important role in synthesizing materials to improve target materials properties or stabilize the materials.In particular,the dopants are essential to improve thermoelectic performances of the materials.However,existing machine learning methods cannot accurately predict the materials properties of doped materials due to severely nonlinear relations with their materials properties.Here,we propose a unified architecture of neural networks,called DopNet,to accurately predict the materials properties of the doped materials.DopNet identifies the effects of the dopants by explicitly and independently embedding the host materials and the dopants.In our evaluations,DopNet outperformed existing machine learning methods in predicting experimentally measured thermoelectric properties,and the error of DopNet in predicting a figure of merit(ZT)was 0.06 in mean absolute error.In particular,DopNet was significantly effective in an extrapolation problem that predicts ZTs of unknown materials,which is a key task to discover novel thermoelectric materials. 展开更多
关键词 MATERIALS PROPERTIES explicitly
原文传递
上一页 1 下一页 到第
使用帮助 返回顶部