Soofi, Yasaman J., Gu, Yijia, Liu, Jinling (2023) An adaptive Physics-based feature engineering approach for Machine Learning-assisted alloy discovery. Computational Materials Science, 226. 112248 doi:10.1016/j.commatsci.2023.112248

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Reference Type	Journal (article/letter/editorial)
Title	An adaptive Physics-based feature engineering approach for Machine Learning-assisted alloy discovery
Journal	Computational Materials Science
Authors	Soofi, Yasaman J.		Author
	Gu, Yijia		Author
	Liu, Jinling		Author
Year	2023 (June)	Volume	226
Publisher	Elsevier BV
DOI	doi:10.1016/j.commatsci.2023.112248Search in ResearchGate
	Generate Citation Formats
Mindat Ref. ID	15837570	Long-form Identifier	mindat:1:5:15837570:2
GUID	0
Full Reference	Soofi, Yasaman J., Gu, Yijia, Liu, Jinling (2023) An adaptive Physics-based feature engineering approach for Machine Learning-assisted alloy discovery. Computational Materials Science, 226. 112248 doi:10.1016/j.commatsci.2023.112248
Plain Text	Soofi, Yasaman J., Gu, Yijia, Liu, Jinling (2023) An adaptive Physics-based feature engineering approach for Machine Learning-assisted alloy discovery. Computational Materials Science, 226. 112248 doi:10.1016/j.commatsci.2023.112248
In	(2023) Computational Materials Science Vol. 226. Elsevier BV

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	*Tahmasebi, Pejman (2026) Ensemble-guided machine learning for feature-constrained porous media. Computational Materials Science, 261. doi:10.1016/j.commatsci.2025.114305*
	*Liu, Jian, Qian, Quan (2023) Reinforcement learning-based knowledge graph reasoning for aluminum alloy applications. Computational Materials Science, 221. 112075 doi:10.1016/j.commatsci.2023.112075*
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	Xu, Han; Yu, Xuexian; Liu, Jun; Gao, Xiang (2025) Scaling law-informed machine learning for predicting thermal and electrical properties of polymers: A physics-based approach. Computational Materials Science, 253. doi:10.1016/j.commatsci.2025.113887
	Firouznia, Mohammadhossein, Bryngelson, Spencer H., Saintillan, David (2023) A spectral boundary integral method for simulating electrohydrodynamic flows in viscous drops. Journal of Computational Physics, 489. 112248 doi:10.1016/j.jcp.2023.112248
	Verma, Apoorv; Jami, Junaid; Bhattacharya, Amrita (2026) Accelerating magnetic materials discovery using interaction matrix-based machine learning descriptors. Computational Materials Science, 262. doi:10.1016/j.commatsci.2025.114395
	Yan, Yonggang, Lu, Dan, Wang, Kun (2021) Accelerated discovery of single-phase refractory high entropy alloys assisted by machine learning. Computational Materials Science, 199. 110723pp. doi:10.1016/j.commatsci.2021.110723
	Lian, Zhengheng, Ma, Yingying, Li, Minjie, Lu, Wencong, Zhou, Wenyan (2024) Discovery Precision: An effective metric for evaluating performance of machine learning model for explorative materials discovery. Computational Materials Science, 233. 112738 doi:10.1016/j.commatsci.2023.112738
	Mitra, Rahul, Bajpai, Anurag, Biswas, Krishanu (2023) ADASYN-assisted machine learning for phase prediction of high entropy carbides. Computational Materials Science, 223. 112142 doi:10.1016/j.commatsci.2023.112142

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Soofi, Yasaman J., Gu, Yijia, Liu, Jinling (2023) An adaptive Physics-based feature engineering approach for Machine Learning-assisted alloy discovery. Computational Materials Science, 226. 112248 doi:10.1016/j.commatsci.2023.112248

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