Two-dimensional(2D)hybrid organic-inorganic perovskites(HOIPs)have strong potential for optoelectronic applications due to their polarized photon absorption and emission properties.These macroscopic behaviors are intr...Two-dimensional(2D)hybrid organic-inorganic perovskites(HOIPs)have strong potential for optoelectronic applications due to their polarized photon absorption and emission properties.These macroscopic behaviors are intrinsically linked to microscopic symmetry breaking,particularly the emergence of momentum-dependent,non–centrosymmetric spin splitting in frontier electronic bands.To efficiently identify such spin-related phenomena,we combine first-principles calculations and deep learning models to explore the correlation between in-plane bond distortions and spin-orbit splitting.Our model achieves 100%accuracy in qualitatively identifying systems with observable spin splitting,and over 80%quantitative accuracy in predicting its magnitude and location,confirming that in-plane structural distortions are key descriptors of spin splitting.The trained model can be readily extended to real 2D HOIP systems and is expected to benefit experimentalists by enabling rapid screening and discovery of functional materials,especially in caseswhere ab initio calculations are not feasible due to computational cost.展开更多
基金supported by the National Key Research and Development Program of China(Grant No.2022YFB4200500)the National Natural Science Foundation of China(Grant Nos.12474057 and 52494934)+1 种基金the Fundamental Research Funds for the Central Universities for this workR.Z.also thanks DHC-AI Co.,Ltd.for providing financial support and computational resources.R.S.declares no external funding for this work.
文摘Two-dimensional(2D)hybrid organic-inorganic perovskites(HOIPs)have strong potential for optoelectronic applications due to their polarized photon absorption and emission properties.These macroscopic behaviors are intrinsically linked to microscopic symmetry breaking,particularly the emergence of momentum-dependent,non–centrosymmetric spin splitting in frontier electronic bands.To efficiently identify such spin-related phenomena,we combine first-principles calculations and deep learning models to explore the correlation between in-plane bond distortions and spin-orbit splitting.Our model achieves 100%accuracy in qualitatively identifying systems with observable spin splitting,and over 80%quantitative accuracy in predicting its magnitude and location,confirming that in-plane structural distortions are key descriptors of spin splitting.The trained model can be readily extended to real 2D HOIP systems and is expected to benefit experimentalists by enabling rapid screening and discovery of functional materials,especially in caseswhere ab initio calculations are not feasible due to computational cost.
