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.
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)
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)
Active learning in materials science with emphasis on adaptive sampling using uncertainties for targeted design T. Lookman, P.V. Balachandran, D. Xue, and R. Yuan, npj Computational Materials 5, 21 (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)
Predictions of New ABO3 Perovskite Compounds by Combining Machine Learning and Density Functional Theory P.V. Balachandran, A.A. Emery, J.E. Gubernatis, T. Lookman, C. Wolverton and A. Zunger, Physical Review Materials 2, 043802 (2018)
Multi-objective Optimization for Materials Discovery via Adaptive Design A. Gopakumar, P.V. Balachandran, D. Xue, J.E. Gubernatis, and T. Lookman, Scientific Reports 8, 3738 (2018)
Accelerated Discovery of Large Electrostrains in BaTiO3-based Piezoelectrics using Active Learning R. Yuan, Z. Liu, P.V. Balachandran, D. Xue, Y. Zhou, X. Ding, J. Sun, D. Xue, and T. Lookman, Advanced Materials 30 1702884 (2018)
Predicting Displacements of Octahedral Cations in Ferroelectric Perovskite using Machine Learning P.V. Balachandran, T. Shearman, J. Theiler, T. Lookman, Acta Crystallographica Section B73, 962-967 (2017)
Three-dimensional imaging of vortex structure in a ferroelectric nanoparticle driven by an electric field D. Karpov, Z. Liu, T. dos Santos Rolo, R. Harder, P.V. Balachandran, D. Xue, T. Lookman and E. Fohtung, Nature Communications 8, 280 (2017)
Materials descriptors for morphotropic phase boundary curvature in lead-free piezoelectrics D. Xue, P.V. Balachandran, H. Wu, R. Yuan, Y. Zhou, X-D. Ding, J. Sun and T. Lookman, Applied Physics Letters 111, 032907 (2017)
Learning from data to design functional materials without inversion symmetry P.V. Balachandran, J. Young, T. Lookman and J.M. Rondinelli, Nature Communications 8, 14282 (2017)
Accelerated search for BaTiO3-based piezoelectrics with vertical morphotropic phase boundary using Bayesian learning D. Xue, P.V. Balachandran, R. Yuan, T. Hu, X. Qian, E.R. Dougherty, and T. Lookman, Proceedings of the National Academy of Sciences USA 113, 13301-13306 (2016)
Accelerated search for materials with targeted properties by adaptive design D. Xue, P.V. Balachandran, J. Hogden, J. Theiler, D. Xue, and T. Lookman, Nature Communications 7, 11241 (2016)
