• Assistant Professor, Mechanical Engineering
Dr. Aravind Krishnamoorthy

Educational Background

  • Ph.D., Materials Science and Engineering, Massachusetts Institute of Technology – 2016
  • B.Tech., Metallurgical Engineering and Materials Science, Indian Institute of Technology, Madras – 2010

Research Interests

    • Atomistic Simulations and Multiscale Modeling
    • Computational Synthesis of Materials and Structures
    • Machine Learning for Mechanical Materials
    • Predictive Design and Manufacturing of Materials, Surfaces, and Interfaces
    • Ultrafast and Far-from-Equilibrium Phenomena

Selected Publications

  • P. Rajak, A. Krishnamoorthy, A. Mishra, R. K. Kalia, A. Nakano and P. Vashishta, “Autonomous reinforcement learning agent for chemical vapor deposition synthesis of quantum materials”, npj Computational Materials 7, 108: 1-9 (2021)
  • A. Krishnamoorthy, K. Nomura, N. Baradwaj, K. Shimamura, P. Rajak, A. Mishra, S. Fukushima, F. Shimojo, R. K. Kalia, A. Nakano, and P. Vashishta, “Dielectric constant of liquid water using neural network ab initio molecular dynamics”, Physical Review Letters 126, 216403:1-7 (2021)
  • I. Tung, A. Krishnamoorthy, S. Sadasivam, H. Zhou, Q. Zhang, K. L. Seyler, G. Clark, E. M. Mannebach, C. Nyby, F. Ernst, D. Zhu, J. M. Glownia, M. E. Kozina, S. Song, S. Nelson, H. Kumazoe, F. Shimojo, R. K. Kalia, P. Vashishta, P. Darancet, T. F. Heinz, A. Nakano, X. Xu, A. M. Lindenberg, and H. Wen, “Anisotropic structural dynamics of monolayer crystals revealed by femtosecond surface x-ray scattering”, Nature Photonics 13, 425-430 (2019)
  • T. Linker, K. Nomura, A. Aditya, S. Fukshima, R. K. Kalia, A. Krishnamoorthy, A. Nakano, P. Rajak, K. Shimmura, F. Shimojo, and P. Vashishta, “Exploring far-from-equilibrium ultrafast polarization control in ferroelectric oxides with excited-state neural network quantum molecular dynamics”, Science Advances 8, eabk2625: 1-7 (2022)
  • A. Krishnamoorthy, A. Mishra, D. Kamal, S. Hong, K. Nomura, S. C. Tiwari, A. Nakano, R. K. Kalia, R. Ramprasad and P. Vashishta, “EZFF: Python library for multi-objective parameterization and uncertainty quantification of interatomic forcefields for molecular dynamics”, SoftwareX 13, 100663:1-9 (2021)