Welcome to Machine learning applications in materials science Project
Welcome to Machine learning applications in materials science Project
Machine learning is recently considered as a powerful tool for deciphering complex physics of materials science. We are working on exploring effective charge in electromigration effect, Sn-based solder design, ductile-to-brittle transition temperature (DBTT) in steel under irradiation, etc, by using this method.
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Related Papers:
Y.-C. Liu*, D. Morgan, T. Yamamoto and G. R. Odette, "Characterizing the flux effect on the irradiation embrittlement of reactor pressure vessel steels using machine learning", Acta Materialia, 256, 119144 (2023) (#IF = 9.4, Rank: 3/78 = 3.8% in METALLURGY & METALLURGICAL ENGINEERING -- SCIE). <Journal Pub>
M.-H. Tsai, T.-Y. Lin, T.-S. Su, G.-M. Chen, Y.-C. Liu, and Y.-Z. Chen, "Regulating Zn deposition via Zincophilic 2D-Cu2Te as the Current Collector to Suppress Dendrite Formation toward High Performance Aqueous Zinc-Ion Batteries", Batteries & Supercaps, e202300107 (2023) (#IF = 6.043, Rank: 9/30 = 30 % in ELECTROCHEMISTRY). <Journal Pub>
T. Banda, Y.-C. Liu*, A. A. Farid, and C.-S. Lim, "A machine learning model for flank wear prediction in face milling of Inconel 718", International Journal of Advanced Manufacturing Technology, 126, 935-945 (2023) (#IF = 3.563, Rank: 28/65 = 43 % in AUTOMATION & CONTROL SYSTEMS). <Journal Pub>
Y.-C. Liu, H. Wu, T. Mayeshiba, B. Afflerbach, R. Jacobs, J. Perry, J. George, J. Cordell, J. Xia, H. Yuan, A. Lorenson, H. Wu, M. Parker, F. Doshi, A. Politowicz, L. Xiao, D. Morgan, P. Wells, N. Almirall, T. Yamamoto and G. R. Odette, “Machine Learning Predictions of Irradiation Embrittlement in Reactor Pressure Vessel Steels”, npj Computational Materials, 8, 85 (2022) (#IF = 12.256, Rank:33/345 = 9.6% in MATERIALS SCIENCE, MULTIDISCIPLINARY). <Journal Pub>
Y.-C. Liu, T.-Y. Liu, T.-H. Huang, K.-C. Chiu, S.-k. Lin, "Exploring dielectric constant and dissipation factor of LTCC using machine learning", Materials, 14(19), 5784 (2021) (Special Issue: Simulation and Reliability Assessment of Advanced Packaging) (IF = 3.748, Rank: 18/79 = 23% in METALLURGY & METALLURGICAL ENGINEERING). <Journal Pub>
Y.-C. Liu, B. Afflerbach, R. Jacobs, S.-K. Lin and D. Morgan, “Exploring effective charge in electromigration using machine learning”, MRS Communications, 1-9 (2019) (Special Issue Research Letter: Artificial Intelligence [by invitation only]) (IF = 1.997, Rank: 197/314 = 63% in MATERIALS SCIENCE, MULTIDISCIPLINARY -- SCIE). <Journal Pub> <Selected as the Worth Reading Literature by Citrine Informatics>
紀喆允、郭力維、劉禹辰、吳明修、胡永毅、許文東, "數值模擬與機器學習技術應用於材料開發",《工業材料雜誌》434期 (2023). <Journal Pub>