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.


  • Research Excellence Award, University of Virginia 2020
  • DARPA Young Faculty Award 2020
  • Rising Star, Computational Materials Science (Top 20) 2019

Research Interests

  • Density Functional Theory
  • Materials Informatics
  • Machine Learning/Active Learning/Adaptive Learning
  • Magnetic Materials
  • Ferroic Materials
  • Electronic Materials

In the News

Selected Publications

  • Accelerated HKUST-1 Thin-Film Property Optimization Using Active Learning L. Huelsenbeck, S. Jung, R. Herrera del Valle, P.V. Balachandran, and G. Giri, ACS Applied Materials and Interfaces 13, 61827–61837 (2021)
  • A Bayesian Approach to the Eagar-Tsai Model for Melt Pool Geometry Prediction with Implications in Additive Manufacturing of Metals B.J. Whalen, J. Ma, and P.V. Balachandran, Integrating Materials and Manufacturing Innovation 10, 597-609 (2021)
  • Machine-Learning-Enabled Prediction of Adiabatic Temperature Change in Lead-Free BaTiO3-Based Electrocaloric Ceramics M. Su, R. Grimes, S. Garg, D. Xue, and P.V. Balachandran, ACS Applied Materials and Interfaces 13, 53475-53484 (2021)
  • Density functional theory study of chemical pressure in multicaloric MTX compounds T.Q. Hartnett, V. Sharma, R. Barua, and P.V. Balachandran, Applied Physics Letters 118, 212408 (2021)
  • Machine learning for materials design and discovery (Editorial) R. Vasudevan, G. Pilania, and P.V. Balachandran, Journal of Applied Physics, 129, 070401 (2021)
  • Rare-earth-free ferrimagnetic Mn4N sub-20 nm thin films as potential high-temperature spintronic material W. Zhou, C.T. Ma, T.Q. Hartnett, P.V. Balachandran, and S.J. Poon, AIP Advances 11, 015334 (2021)
  • Data-driven assessment of chemical vapor deposition grown MoS2 monolayer thin films A. Costine, P. Delsa, P. Reinke, and P.V. Balachandran, Journal of Applied Physics 128, 235303 (2020)
  • Magnetic Damping in Epitaxial Iron Alloyed with Vanadium and Aluminum D. A. Smith, A. Rai, Y. Lim, T. Q. Hartnett, A. Sapkota, A. Srivastava, C. Mewes, Z. Jiang, M. Clavel, M. K. Hudait, D. D. Viehland, J. J. Heremans, P. V. Balachandran, T. Mewes, and S. Emori, Physical Review Applied 14, 034042 (2020)
  • Adaptive machine learning for efficient materials design P.V. Balachandran, MRS Bulletin 45, 579-586 (2020)
  • 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) ABS Ghanshyam 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)

Courses Taught

  • MSE 4592/6592 (Special Topics: Introduction to Materials Informatics) Spring 2020
  • MAE 3420 (Computational Methods in Mechanical & Aerospace Engineering) Fall 2019
  • MSE 4592/6592 (Special Topics: Introduction to Materials Informatics) Spring 2019
  • MSE 2090 (Introduction to Materials Science) Fall 2018