虽然新材料的计算发现可以简化实验合成前的筛选过程，但由于材料组分和结构的潜在组合潜力巨大，这一筛选过程仍然困难而漫长，系统地探索材料组成和结构空间也具有挑战性。
fig. 1 performance ()of prediction of various test datasets using the ce feature models with different center atom definitions.
如果目标材料的数据有限，则难度更大，从其他材料已知的大数据集中迁移习晶体结构已成为材料设计中的重要策略之一。
fig. 2 formation energies predicted by the ml with ce features (dnn-ce) and dft using various datasets.
上海大学材料基因组工程研究所刘毅教授和冯凌燕教授团队提出了一种基于大规模尖晶石氧化物计算数据集的热力学稳定钙钛矿氧化物深度迁移习方法。
fig. 3 heat map of formation energies of 5329 abo3 perovskite oxide structures predicted by the transferred learning model in this work, containing 73 constitution elements at the a and b sites, respectively, sorted by the atom number.
利用计算出的5329尖晶石氧化物结构的形成能，建立了具有“中心环境（CE）”特征的深度神经元网络（DNN）源域模型，然后通过习对855个钙钛矿氧化物结构的小数据集对DNN模型参数进行微调，得到了目标域中钙钛矿氧化物中具有良好迁移率的迁移习模型。
fig. 4 heat map of tolerance factor of 5329 perovskite oxide structures calculated in this work, containing 73 constitution elements at the a and b sites, sorted by the atom number.
迁移习模型**对钙钛矿结构形成能的平均绝对误差（MAE）仅为0106 EV 原子，优于 0132 ev/atom。基于迁移习模型，作者快速估计了包含73种元素的5329个潜在钙钛矿结构的形成能。
fig. 5 tolerance factor (t) vs. octahedral factor (μscatter plot ofperovskite oxide structures, where the colormap corresponds tothe transfer learning predicted formation energy of perovskitestructure.
结合**的形成能和公差因子（0.）的包含。7 < t ≤ 1.1） 和八面体因子 （045 < 0.7）他们鉴定了1314种潜在的热力学稳定的钙钛矿氧化物。在1314种潜在的钙钛矿氧化物中，144种已经通过实验合成证实，10种已经通过其他计算确定，301种已经记录在材料项目数据库中，其余859种氧化物尚未在文献中报道。
fig. 6 statistical distribution of the formation energy of perovskite structures predicted by machine learning and the screening process for stable perovskite structures.
本研究结合了基于结构信息的机器习表征和迁移习方法，利用丰富的已知结构数据以较低的额外计算成本创建新结构，为昂贵的高通量计算筛选材料设计提供了一种新的有效加速策略。
fig. 7 crystal structures and constituent elements of spinel oxides and perovskite oxides studied in this work.
*新型钙钛矿氧化物为可再生能源和电子材料应用的实验合成和探索提供了丰富的候选材料。 相关**最近发表在NPJ Computational Materials上106 (2023)。在手机上阅读原文，请点击本文底部左下角的“阅读原文”，输入后也可以**全文pdf文件。
fig. 8 general schematic diagram of dnn-ce models and the workflow of transfer learning method in this work.
editorial summary
transfer learning across crystal structures: “center-environment” feature accelerates materials predicting
discovering new materials through computational methods has simplified the screening process before experimental synthesis. however, systematically exploring the material space remains challenging due to the vast potential combinations of material compositions and structures. in cases where data on the target material is limited, cross-crystal structure transfer learning from large-scale known datasets of other materials has become an important strategy in materials design.
this study proposes a deep transfer learning approach based on a large-scale dataset of spinel oxide compounds to predict the thermodynamically stable perovskite oxides. prof. liu and prof. feng’s team at materials genome institute of shanghai university utilized the formation energy of 5,329 spinel oxide structures to develop a deep neural network (dnn) source domain model with “center-environment” (ce) features. the ce-dnn model was then fine-tuned using a small dataset of 855 perovskite oxide structures to achieve a transferable learning model with good performance in the perovskite oxide target domain.
the mean absolute error (mae) of the perovskite structure formation energy predicted by the transfer learning model is 0.106 ev/atom, which is better than the mae of 0.132 ev/atom of the model trained solely using small perovskite data. based on the transfer learning model, the formation energy of 5,329 potential perovskite structures containing 73 different elements was further predicted. combining the predicted formation energies with structural factor criteria, including tolerance factor (0.7 < t ≤ 1.1) and octahedral factor (0.45 < 0.7), a total of 1,314 potentially thermodynamically stable perovskite oxides were predicted.
among 1,314 predicted potential perovskite oxides, 144 were experimentally synthesized, 10 were predicted by other computational works, and 301 are documented in the materials project database, while the remaining 859 oxides h**e not been reported in literatures. the combination of structural information features and transfer learning methods in this study enables the low-cost prediction of new structures using existing big data, providing an effective acceleration strategy for expensive high-throughput computational material design. the predicted stable novel perovskite oxides offer a rich platform for exploring novel perovskite experimental synthesis, renewable energy and electronic materials applications.this article was recently published in npj computational materials
原文摘要及其翻译
跨晶体结构的中心环境深度转移机器学习：从尖晶石氧化物到钙钛矿氧化物 习 s
yihang li, ruijie zhu, yuanqing wang, lingyan feng & yi liu
abstractin data-driven materials design where the target materials h**e limited data, the transfer machine learning from large known source materials, becomes a demanding strategy especially across different crystal structures. in this work, we proposed a deep transfer learning approach to predict thermodynamically stable perovskite oxides based on a large computational dataset of spinel oxides. the deep neural network (dnn) source domain model with “center-environment” (ce) features was first developed using the formation energy of 5329 spinel oxide structures and then was fine-tuned by learning a small dataset of 855 perovskite oxide structures, leading to a transfer learning model with good transferability in the target domain of perovskite oxides. based on the transferred model, we further predicted the formation energy of potential 5329 perovskite structures with combination of 73 elements. combining the criteria of formation energy and structure factors including tolerance factor (0.7 < t ≤ 1.1) and octahedron factor (0.45 < 0.7), we predicted 1314 thermodynamically stable perovskite oxides, among which 144 oxides were reported to be synthesized experimentally, 10 oxides were predicted computationally by other literatures, 301 oxides were recorded in the materials project database, and 859 oxides h**e been first reported. combing with the structure-informed features the transfer machine learning approach in this work takes the advantage of existing data to predict new structures at a lower cost, providing an effective acceleration strategy for the expensive high-throughput computational screening in materials design. the predicted stable novel perovskite oxides serve as a rich platform for exploring potential renewable energy and electronic materials applications.
总结：
在数据驱动的材料设计中，当目标材料的数据有限时，基于大量已知源材料数据，特别是跨越不同晶体结构的转移机习已成为具有实际需求和应用场景的研究策略。 本文基于已知的大规模尖晶石氧化物计算数据和新添加的少量钙钛矿氧化物计算数据，以及新的热力学稳定的新型钙钛矿氧化物跨晶体结构，提出了一种深度迁移习方法。
首先，利用计算得到的5329个尖晶石氧化物结构的形成能，建立了基于包含结构信息的“中心环境”（CE）特征的深度神经网络（DNN）源域模型，然后通过习学习855个钙钛矿氧化物结构的小数据集对DNN模型参数进行微调，得到具有良好迁移率的钙钛矿氧化物在目标域的迁移习模型。 基于CE-DNN迁移习模型，进一步估计了包含73种元素组合的5329个钙钛矿结构的形成能。 结合形成能**和结构因子（包括公差因子（0.）。7 < t ≤ 1.1） 和八面体因子 （045 < 0.7）），共鉴定出1314种具有潜在热力学稳定性的钙钛矿氧化物，其中144种氧化物已被实验合成，10种氧化物已被记录在其他计算文献中，301种氧化物被记录在材料项目数据库中，另外859种氧化物是本文首次报道。
基于包含结构信息的特征工程，本研究的迁移机习方法利用丰富的可用数据，以较低的额外计算成本为昂贵的新晶体结构特性的高通量计算筛选提供了有效的加速策略。 本研究的新型钙钛矿氧化物为探索可再生能源和电子材料应用提供了丰富的候选材料和改进平台。