Research Interest

Machine learning aided structural design, material modeling, physics-informed machine learning, graph neural networks, dynamic fracture and crack propagation on ballistic impact, molecular dynamics, and peridynamics

Research/Experience

• Assistant Professor, Civil Engineering
  April 2024 – Present
  Indian Institute of Technology Indore, Indore
• Research Associate, Civil and Environmental Engineering
  April 2023 – March 2024
  University of Wisconsin, Madison
• Postdoctoral Scholar, Civil Engineering
  January 2023 – March 2023
  Indian Institute of Technology Delhi, Delhi

Education

• Ph.D., Civil Engineering
  July 2017 – January 2023
  Indian Institute of Technology Delhi, Delhi
• B.Tech., Civil Engineering
  July 2011 – June 2015
  Indian Institute of Technology Roorkee, Roorkee

Ph.D. Thesis

Title: Data-driven Modeling and Physics-informed Machine Learning for Glass Discovery

The research work explores the use of machine learning methods in modeling inorganic glass properties, revealing composition–property relationships through explainable ML, understanding mechanisms at atomic scale and up-scaling of material properties using MD and DFT simulation trajectory. The research work uses the graph neural network based interatomic potential for reproducing features at atomic scale for complex systems and use ML material models (peridynamics nonlocal operators) for reproducing material behavior at meso-scale with MD and DFT simulation trajectories

Research Achievements and Awards

PyGGi (Python for Glass Genomics) It is an indigenous industry-relevant software package that uses trained Machine Learning algorithms to predict/optimize composition-property relationships in inorganic glasses. It will make the tedious process of designing tailored glasses economical in terms of time, effort, and money. The software package is launched through FITT IITD and is available at www.pyggi.iitd.ac.in

• ICG-GOMD 2019 registration grant (2019)
• Prime Ministers Research Fellowship (PMRF)
• SITARE/SRISTI Gandhian Young Technological Innovation (GYTI) Awards/Appreciations (2020) for PyGGi
• SERB International Travel Support (2022)

Book(s) and Contributed Chapters

• Machine Learning for Materials Discovery: Numerical Recipes and Practical Applications (Draft)

  N. M. Anoop Krishnan, Hariprasad Kodamana and Ravinder Bhattoo

Software and Programming Languages

• Packages Developed:
  • Peridynamics: PeriDyn.jl, PDMaterialPoints.jl and PDBenchmark.jl
  • Molecular Dynamics: MDSimulator.jl and MDBase.jl
  • Others: GlassConversionPy, MLPipeline, MixModelsPytorch
  • Contribution to other packages: jax-md

  All packages can be accessed at the github repository.

• Operating Systems: Linux, MacOS, and Windows
• Softwares: LAMMPS, NAMD, Peridigm, R.I.N.G.S, Ovito, VMD, Abaqus and STAADPRO
• Languages: Julia, Python, C++, and Matlab

Publications

Journal Articles

Preprints

Conference Papers

Conference Talks, Workshops and Posters

Talks

Workshops

Posters