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arXiv 2510.00513cond-mat.mtrl-sci
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基于晶体结构的电荷缺陷形成能机器学习预测

Machine Learning Prediction of Charged Defect Formation Energies from Crystal Structures

Institute for Materials Research, Tohoku University(东北大学物质材料研究中心);Organization for Advanced Studies, Tohoku University(东北大学先进研究组织)

Shin Kiyohara, Chisa Shibui, Soungmin Bae, Yu Kumagai

AI总结:

本文提出了一种基于晶体结构的机器学习方法,用于预测电荷状态下缺陷形成能,并通过实验验证了其在氧空位预测中的有效性,同时识别出潜在的光伏材料。

摘要:

材料信息学的最新进展扩大了可合成材料的数量。然而,基于缺陷性质筛选有前景的候选材料(如半导体)仍然具有挑战性。这主要是由于缺乏一个通用框架,用于从结构数据中预测多种电荷状态下缺陷形成能。在本信件中,我们提出了一种协议,即数据标准化、费米能级对齐以及扰动宿主态的处理,并通过使用单一模型准确预测氧空位在三种电荷状态下的形成能来验证该协议。我们还介绍了一种联合机器学习模型,该模型整合了缺陷形成能和能带边缘预测用于虚拟筛选。使用这一框架,我们识别出89种可掺杂的氧化物,包括BaGaSbO,一种潜在的双极光伏材料。我们的协议预计将成为机器学习研究点缺陷形成能研究的标准方法。

英文摘要:

Recent advances in materials informatics have expanded the number of synthesizable materials. However, screening promising candidates, such as semiconductors, based on defect properties remains challenging. This is primarily due to the lack of a general framework for predicting defect formation energies in multiple charge states from structural data. In this Letter, we present a protocol, namely data normalization, Fermi level alignment, and treatment of perturbed host states, and validate it by accurately predicting oxygen vacancy formation energies in three charge states using a single model. We also introduce a joint machine-learning model that integrates defect formation energies and band-edge predictions for virtual screening. Using this framework, we identify 89 hole-dopable oxides, including BaGaSbO, a potential ambipolar photovoltaic material. Our protocol is expected to become a standard approach for machine-learning studies on point defect formation energies.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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