iamrp.net-v4uh-1790651230-f215517044 1790651230 Institute for Advanced Materials Research Press info@iamrp.net Institute for Advanced Materials Research Press Journal of Computational and Data-Driven Materials Engineering J. Comput. Data-Driven Mater. Eng. 3149-9368 2022 1 1 Lucas Meyer Anna Schmid Stefan Braun 2022 10.68159/f215517044 https://iamrp.net/pub/journal/2/article/f215517044 Rao Z, Tung PY, Xie R, Wei Y, Zhang H, Ferrari A, et al. Machine learning-enabled high-entropy alloy discovery. Science. 2022;378(6615):78-85. Wen C, Zhang Y, Wang C, Xue D, Bai Y, Antonov S, et al. Machine learning assisted design of high entropy alloys with desired property. Acta Mater. 2019;170:109-17. Rohr B, Stein HS, Guevarra D, Wang Y, Haber JA, Aykol M, et al. Benchmarking the acceleration of materials discovery by sequential learning. Chem Sci. 2020;11(10):2696-706. McCloskey M, Cohen NJ. Catastrophic interference in connectionist networks: The sequential learning problem. In: Psychol Learn Motiv. 1989;24:109-65. Kirkpatrick J, Pascanu R, Rabinowitz N, Veness J, Desjardins G, Rusu AA, et al. Overcoming catastrophic forgetting in neural networks. Proc Natl Acad Sci U S A. 2017;114(13):3521-6. De Lange M, Aljundi R, Masana M, Parisot S, Jia X, Leonardis A, et al. A continual learning survey: Defying forgetting in classification tasks. IEEE Trans Pattern Anal Mach Intell. 2022;44(7):3366-85. Hadsell R, Rao D, Rusu AA, Pascanu R. Embracing change: Continual learning in deep neural networks. Trends Cogn Sci. 2020;24(12):1028-40. Bartók AP, De S, Poelking C, Bernstein N, Kermode JR, Csányi G, et al. Machine learning unifies the modeling of materials and molecules. Sci Adv. 2017;3(12):e1701816. Zuo Y, Chen C, Li X, Deng Z, Chen Y, Behler J, et al. Performance and cost assessment of machine learning interatomic potentials. J Phys Chem A. 2020;124(4):731-45. Batzner S, Musaelian A, Sun L, Geiger M, Mailoa JP, Kornbluth M, et al. E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials. Nat Commun. 2022;13(1):2453. Eckhoff M, Reiher M. Lifelong machine learning potentials. J Chem Theory Comput. 2023;19(12):3509-25. Chen S, Cheng Y, Gao H. Machine learning for high-entropy alloys. In: Artificial intelligence for materials science. Cham: Springer International Publishing; 2021. p. 21-58. Chen L, Chen Z, Yao X, Su B, Chen W, Pang X, et al. High-entropy alloy catalysts: High-throughput and machine learning-driven design. J Mater Inform. 2022;2(4):N-A. Li Y, Holmedal B, Liu B, Li H, Zhuang L, Zhang J, et al. Towards high-throughput microstructure simulation in compositionally complex alloys via machine learning. Calphad. 2021;72:102231. Murty BS, Yeh JW, Ranganathan S, Bhattacharjee PP. High-entropy alloys. Amsterdam: Elsevier; 2019. Müller MN, Brix C, Bak S, Liu C, Johnson TT. The third international verification of neural networks competition (VNN-COMP 2022): Summary and results. arXiv preprint arXiv:2212.10376. 2022 Dec 20. Mirzadeh SI, Chaudhry A, Yin D, Hu H, Pascanu R, Gorur D, et al. Wide neural networks forget less catastrophically. In: International conference on machine learning; 2022. p. 15699-717. Huang W, Martin P, Zhuang HL. Machine-learning phase prediction of high-entropy alloys. Acta Mater. 2019;169:225-36. Zhang Y, Wen C, Wang C, Antonov S, Xue D, Bai Y, et al. Phase prediction in high entropy alloys with a rational selection of materials descriptors and machine learning models. Acta Mater. 2020;185:528-39. Wu Q, Wang Z, Hu X, Zheng T, Yang Z, He F, et al. Uncovering the eutectics design by machine learning in the Al-Co-Cr-Fe-Ni high entropy system. Acta Mater. 2020;182:278-86. Liu Y, Parisot S, Slabaugh G, Jia X, Leonardis A, Tuytelaars T. More classifiers, less forgetting: A generic multi-classifier paradigm for incremental learning. In: European conference on computer vision. Cham: Springer International Publishing; 2020. p. 699-716. Aljundi R, Rohrbach M, Tuytelaars T. Selfless sequential learning. arXiv preprint arXiv:1806.05421. 2018 Jun 14. Giles SA, Sengupta D, Broderick SR, Rajan K. Machine-learning-based intelligent framework for discovering refractory high-entropy alloys with improved high-temperature yield strength. npj Comput Mater. 2022;8(1):235. Kaufmann K, Vecchio KS. Searching for high entropy alloys: A machine learning approach. Acta Mater. 2020;198:178-222.