PSG College of Technology, Coimbatore, India, B.E. Metallurgical Engineering, 2007Iowa State University, Ames, IA, USA, Ph.D. Materials Science & Engineering, 2011Drexel University, Philadelphia, PA, USA, Postdoctoral researcher, 2014Los Alamos National Laboratory, Los Alamos, NM, USA, Postdoctoral researcher, 2017
My interests are in materials informatics, density functional theory, machine learning, bayesian inference, and optimal design methods applied to accelerate the search and discovery of novel 2D materials, metallic alloys, ferroic and electronic materials.
A team of University of Virginia researchers has received a $3.4 million grant from Defense Advanced Research Projects Agency with the goal to shrink computing memory bits to a nanoscale.
Researchers recently demonstrated how an informatics-based adaptive design strategy, tightly coupled to experiments, can accelerate the discovery of new materials with targeted properties.
Compositional Dependence of Linear and Nonlinear Optical Response in Crystalline Hafnium Zirconium Oxide Thin Films J.F. Ihlefeld, T.S. Luk, S.W. Smith, S.S. Fields, S.T. Jaszewski, D.M. Hirt, W.T. Riffe, S.T. Bender, C. Constantin, M.V. Ayyasamy et al Journal of Applied Physics 128 034101 (2020)
Chemical gradients in human enamel crystallites K.A. DeRocher, P.J.M. Smeets, B.H. Goodge, M.J. Zachman, P.V. Balachandran, L. Stegbauer, M.J. Cohen, L.M. Gordon, J.M. Rondinelli, L.F. Kourkoutis, and D. Joester, Nature 583,66-71 (2020)
Density functional theory and machine learning guided search for RE2Si2O7 with targeted coefficient of thermal expansion M.V. Ayyasamy, J.A. Deijkers, H.N.G. Wadley, and P.V. Balachandran, Journal of American Ceramic Society 103, 4489-4497 (2020)
Data-driven design of B20 alloys with targeted magnetic properties guided by machine learning and density functional theory (Journal of Materials Research Early Career Scholars) P.V. Balachandran, Journal of Materials Research 35, 890-897 (2020)
Conductivity-like Gilbert Damping due to Intraband Scattering in Epitaxial Iron (Editor's Suggestion) B. Khodadadi et al, Physical Review Letters 124, 157201 (2020)
Data-Based Methods for Materials Design and Discovery: Basic Ideas and General Methods (Book) ABSGhanshyam Pilania, Prasanna V. Balachandran, James E. Gubernatis and Turab Lookman
Prediction of new iodine-containing apatites using machine learning and density functional theory T.Q. Hartnett, M.V. Ayyasamy, and P.V. Balachandran, MRS Communications 9, 882-890 (2019)
Machine learning guided design of single-molecule magnets for magnetocaloric applications (Feature Article; AIP Scilight) L. Holleis, B.S. Shivaram, and P.V. Balachandran, Applied Physics Letters 114, 222404 (2019)
Future Frontiers in Corrosion Science and Engineering, Part III: The Next “Leap Ahead” in Corrosion Control May Be Enabled by Data Analytics and Artificial Intelligence J.R. Scully and P.V. Balachandran, Corrosion 75, 1395-1397 (2019)
Machine learning guided design of functional materials with targeted properties (Special Issue: Rising Stars in Computational Materials Science) P.V. Balachandran, Computational Materials Science 164, 82-90 (2019)
Experimental search for high-temperature ferroelectric perovskites guided by two-step machine learning P.V. Balachandran, B. Kowalski, A. Sehirlioglu, and T. Lookman, Nature Communications 9, 1668 (2018)
