Srikanth Patala, Ph.D.
Senior Materials Design Engineer
Sc.D. Materials Science and Engineering, Massachusetts Institute of Technology, 2011.
M.Sc. Materials Science and Engineering, Massachusetts Institute of Technology, 2008.
B.Tech. Metallurgical and Materials Engineering, Indian Institute of Technology Madras, 2005.
Background and Specialization
Srikanth Patala specializes in computational materials science, with expertise in utilizing machine learning tools for materials design. Srikanth works in QuesTek’s Modeling and Software Development team. At QuesTek, Srikanth is responsible for developing physics-based models to enable ICME-based materials design, implementing software solutions for integrating ICME models in predictive materials design, and coordinating materials design projects for government and commercial entities.
Before joining QuesTek, Patala managed a research group, at North Carolina State University, and secured close to $1.5 million to develop computational tools at the intersection of machine learning and materials design. He has published close to 30 journal articles and has given more than 25 invited talks at various conferences and universities. He has served on numerous panels for the Department of Defense and the National Science Foundation, both reviewing proposals and in workshops defining the landscape of the role of AI/ML in materials science.
Honors, Awards and Patents
- TMS MPMD Young Professional Development Leader Award
- Air Force Office of Scientific Research Young Investigator Award
- National Science Foundation CAREER Award, Faculty Early Career Development Program
- Outstanding PhD Thesis Research Award, Department of Materials Science and Engineering, Massachusetts Institute of Technology
- James Clerk Maxwell Young Writers Prize, Philosophical Magazine & Letters
Selected Publications and Presentations
- Patala, Srikanth, Jeremy K. Mason, and Christopher A. Schuh. "Improved representations of misorientation information for grain boundary science and engineering." Progress in Materials Science8 (2012): 1383-1425.
- Patala, S. (2019). Understanding grain boundaries–The role of crystallography, structural descriptors and machine learning. Computational Materials Science, 162, 281-294.
- Banadaki, A. D., Tschopp, M. A., & Patala, S. (2018). An efficient Monte Carlo algorithm for determining the minimum energy structures of metallic grain boundaries. Computational Materials Science, 155, 466-475.
- Banadaki, A. D., & Patala, S. (2017). A three-dimensional polyhedral unit model for grain boundary structure in fcc metals. npj Computational Materials, 3(1), 1-13.
- Patala, S., & Schuh, C. A. (2011). A continuous and one-to-one coloring scheme for misorientations. Acta materialia, 59(2), 554-562.