Machine Learning in Materials Science

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Machine Learning in Materials Science

Research Focus Areas

Data science has become ubiquitous in science and engineering. There is burgeoning activity in the adoption of machine learning tools in physics, chemistry, chemical engineering, materials science, and related disciplines to elucidate and design complex processes (chemical/biological, engineered/natural) or material systems with wide ranging applications addressing grand challenges in energy, health, environment, and water. At Lehigh, data science and machine learning tools are being used for modeling of complex molecular and material systems of physical, chemical or biological origin to discover the underlying synthesis-structure-property relationships and leverage them in inverse design of new materials and molceules. 

Research Group Members


AI in Materials Conference (May 2019)

At this critical juncture of expanding activity of AI in the domain of molecular and materials science, the “practitioners” of ML in these fields, in both academia and industry, will benefit from close interaction with “developers” of modern ML tools (i.e. data scientists) who, in turn, can learn the problems and the needs of specific domains.To foster this relationship the materials science group at I-DISC, wiht the support of the NSF TRIPODS+X program, recently bought together foundational data scientists and domain practioners to give talks to a common audience at an interdisciplinary conference, "Foundational & Applied Data Science for Molecular and Materials Science & Engineering". The specific foacl area of the conference was the development and application of data science algorithms and tools to address problems in molecular and materials science and engineering (ie. any problem spanning the length scale of atoms to bulk materials).

(Page updated Jan 2020)