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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