| New tool in the box TY Zhang Journal of Materials Informatics 1 (1), 1, 2021 | 43* | 2021 |
| Generative deep learning as a tool for inverse design of high entropy refractory alloys A Debnath, AM Krajewski, H Sun, S Lin, M Ahn, W Li, S Priya, J Singh, ... Journal of Materials Informatics 1 (1), 3, 2021 | 26 | 2021 |
| Generative models for inverse design of inorganic solid materials L Chen, W Zhang, Z Nie, S Li, F Pan J. Mater. Inform 1 (4), 2021 | 24 | 2021 |
| Boosting for concept design of casting aluminum alloys driven by combining computational thermodynamics and machine learning techniques W Yi, G Liu, J Gao, L Zhang Journal of Materials Informatics 1 (2), 2021 | 23 | 2021 |
| Processparameter optimizationof metal additiveman-ufacturing: a review and outlook HY Chia, J Wu, X Wang, W Yan | 18 | 2022 |
| Development of robust surfaces for harsh service environments from the perspective of phase formation and transformation M Lou, K Xu, L Chen, C Hong, Y Yuan, Y Du, K Chang J. Mater. Inf. 1, 5, 2021 | 18 | 2021 |
| Machine learning-guided design and development of metallic structural materials J Yu, S Xi, S Pan, Y Wang, Q Peng, R Shi, C Wang, X Liu J. Mater. Info 1 (9), 2021 | 16 | 2021 |
| Domain knowledge-guided interpretive machine learning: formula discovery for the oxidation behavior of ferritic-martensitic steels in supercritical water B Cao, S Yang, A Sun, Z Dong, TY Zhang Journal of Materials Informatics 2 (2), 4, 2022 | 14 | 2022 |
| High-entropy alloy catalysts: high-throughput and machine learning-driven design L Chen, Z Chen, X Yao, B Su, W Chen, X Pang, KS Kim, CV Singh, Y Zou J. Mater. Inform 2 (19), 10.20517, 2022 | 11 | 2022 |
| Accelerated development of hard high-entropy alloys with data-driven high-throughput experiments Y Liu, J Wang, B Xiao, J Shu J. Mater. Inform 2 (3), 2022 | 10 | 2022 |
| Big data-assisted digital twins for the smart design and manufacturing of advanced materials: from atoms to products WY Wang, J Yin, Z Chai, X Chen, W Zhao, J Lu, F Sun, Q Jia, X Gao, ... J Mater Inf 2 (1), 1-27, 2022 | 9 | 2022 |
| A review on high-throughput development of high-entropy alloys by combinatorial methods S Mooraj, W Chen Journal of Materials Informatics 3 (1), https://doi.org/10.20517/jmi.2022.41, 2023 | 8 | 2023 |
| A critical review of the machine learning guided design of metallic glasses for superior glass-forming ability Z Zhou, Y Shang, Y Yang Journal of Materials Informatics 2 (1), 2, 2022 | 8 | 2022 |
| Additive manufacturing as a tool for high-throughput experimentation W Xiong Journal of Materials Informatics 2 (3), 2022 | 7 | 2022 |
| Structure-property modeling scheme based on optimized microstructural information by two-point statistics and principal component analysis X Hu, J Zhao, Y Chen, Y Wang, J Li, Q Wu, Z Wang, J Wang J. Mater. Inform. 2 (2), 5, 2022 | 7 | 2022 |
| Mapping pareto fronts for efficient multi-objective materials discovery AKY Low, E Vissol-Gaudin, YF Lim, K Hippalgaonkar Journal of Materials Informatics 3 (2), 11, 2023 | 6 | 2023 |
| Recent progress in the data-driven discovery of novel photovoltaic materials T Lu, M Li, W Lu, TY Zhang Journal of Materials Informatics 2 (2), 7, 2022 | 6 | 2022 |
| New trends in additive manufacturing of high-entropy alloys and alloy design by machine learning: From single-phase to multiphase systems Y Zhou, Z Zhang, D Wang, W Xiao, J Ju, S Liu, B Xiao, M Yan, T Yang J. Mater. Inform 2, 18, 2022 | 6 | 2022 |
| A mini review of machine learning in inorganic phosphors L Jiang, X Jiang, G Lv, Y Su J. Mater. Inf 2, 14, 2022 | 6 | 2022 |
| Composition and risk assessment of perioperative patient safety incidents reported by anesthesiologists from 2009 to 2019: a single‐center retrospective cohort study X Zhang, S Ma, X Sun, Y Zhang, W Chen, Q Chang, H Pan, X Zhang, ... BMC anesthesiology 21, 1-8, 2021 | 5 | 2021 |
