Publications

Books

Machine Learning for Material Discovery Focusing on the fundamentals of machine learning, this book covers broad areas of data-driven modeling, ranging from simple regression to advanced machine learning and optimization methods for applications in materials modeling and discovery. The book explains complex mathematical concepts in a lucid manner to ensure that readers from different materials domains are able to use these techniques successfully. A unique feature of this book is its hands-on aspect—each method presented herein is accompanied by a code that implements the method in open-source platforms such as Python. This book is thus aimed at graduate students, researchers, and engineers to enable the use of data-driven methods for understanding and accelerating the discovery of novel materials. link

Articles

  • Our paper on 21 challenges in AI and ML in glass technology featured in American Ceramic Society’s CTT. Read the article here. A reprint of the article can be found in Glass machinery plants and accessories magazine here

  • Our paper on glassy h-BN selected as the cover page in Advanced Theory and Simulations. Read the paper here

You can also find my articles on Google Scholar profile.

All Since 2020
Citations 701 693
h-index 17 17
i10-index 19 19


Journal Articles

  1. Understanding the compositional control on electrical, mechanical, optical, and physical properties of inorganic glasses with interpretable machine learning
    Bhattoo, Ravinder; Bishnoi, Suresh; Zaki, Mohd; Krishnan, N. M. Anoop;
    Acta Materialia, 2023. doi: 10.1016/j.actamat.2022.118439

  2. Learning the dynamics of particle-based systems with Lagrangian graph neural networks
    Bhattoo, Ravinder; Ranu, Sayan; Krishnan, N. M. Anoop;
    Machine Learning: Science and Technology, 2023. doi: 10.1088/2632-2153/acb03e

  3. Interpreting the optical properties of oxide glasses with machine learning and Shapely additive explanations
    Zaki, Mohd; Venugopal, Vineeth; Bhattoo, Ravinder; Bishnoi, Suresh; Singh, Sourabh Kumar; Allu, Amarnath R.; Jayadeva; Krishnan, N. M. Anoop;
    Journal of the American Ceramic Society, 2022. doi: 10.1111/jace.18345

  4. Scalable Gaussian processes for predicting the optical, physical, thermal, and mechanical properties of inorganic glasses with large datasets
    Bishnoi, Suresh; Ravinder, R.; Grover, Hargun Singh; Kodamana, Hariprasad; Krishnan, N. M. Anoop
    Materials Advances, 2021. doi: 10.1039/D0MA00764A

  5. Artificial intelligence and machine learning in glass science and technology: 21 challenges for the 21st century
    Ravinder; Venugopal, Vineeth; Bishnoi, Suresh; Singh, Sourabh; Zaki, Mohd; Grover, Hargun Singh; Bauchy, Mathieu; Agarwal, Manish; Krishnan, N. M. Anoop
    International Journal of Applied Glass Science, 2021. doi: 10.1111/ijag.15881

  6. Irradiation-induced brittle-to-ductile transition in α-quartz
    Ravinder, R.; Kumar, Abhishek; Kumar, Rajesh; Vangla, Prashanth; Krishnan, N. M. Anoop
    Journal of the American Ceramic Society, 2020. doi: 10.1111/jace.16951

  7. Glass Transition and Crystallization in Hexagonal Boron Nitride: Crucial Role of Orientational Order
    Ravinder, R.; Garg, Prateet; Krishnan, N. M. Anoop
    Advanced Theory and Simulations, 2020. doi: 10.1002/adts.201900174

  8. Deep learning aided rational design of oxide glasses
    Ravinder, R.; Sridhara, Karthikeya H.; Bishnoi, Suresh; Grover, Hargun Singh; Bauchy, Mathieu; Jayadeva; Kodamana, Hariprasad; Krishnan, N. M. Anoop
    Materials Horizons, 2020. doi: 10.1039/D0MH00162G

  9. Cooling rate effects on the structure of 45S5 bioglass: Insights from experiments and simulations
    Bhaskar, Pratik; Kumar, Rajesh; Maurya, Yashasvi; Ravinder, R.; Allu, Amarnath R.; Das, Sumanta; Gosvami, Nitya Nand; Youngman, Randall E.; Bødker, Mikkel S.; Mascaraque, Nerea; Smedskjaer, Morten M.; Bauchy, Mathieu; Krishnan, N. M. Anoop
    Journal of Non-Crystalline Solids, 2020. doi: 10.1016/j.jnoncrysol.2020.119952

  10. An adaptive, interacting, cluster-based model for predicting the transmission dynamics of COVID-19
    Ravinder, R.; Singh, Sourabh; Bishnoi, Suresh; Jan, Amreen; Sharma, Amit; Kodamana, Hariprasad; Krishnan, N. M. Anoop
    Heliyon, 2020. doi: 10.1016/j.heliyon.2020.e05722

  11. A Peridynamics-Based Micromechanical Modeling Approach for Random Heterogeneous Structural Materials
    Nayak, Sumeru; Ravinder, R.; Krishnan, N. M. Anoop; Das, Sumanta
    Materials, 2020. doi: 10.3390/ma13061298

  12. Redox Sensitive Self-Assembling Dipeptide for Sustained Intracellular Drug Delivery
    Dhawan, Sameer; Ghosh, Sukanya; Ravinder, R.; Bais, Sachendra S.; Basak, Soumen; Krishnan, N. M. Anoop; Agarwal, Manish; Banerjee, Manidipa; Haridas, V.
    Bioconjugate Chemistry, 2019. doi: 10.1021/acs.bioconjchem.9b00532

  13. Predicting Young’s modulus of oxide glasses with sparse datasets using machine learning
    Bishnoi, Suresh; Singh, Sourabh; Ravinder, R.; Bauchy, Mathieu; Gosvami, Nitya Nand; Kodamana, Hariprasad; Krishnan, N. M. Anoop
    Journal of Non-Crystalline Solids, 2019. doi: 10.1016/j.jnoncrysol.2019.119643

  14. Glass Fracture Upon Ballistic Impact: New Insights From Peridynamics Simulations
    Rivera, Jared; Berjikian, Jonathan; Ravinder, R.; Kodamana, Hariprasad; Das, Sumanta; Bhatnagar, Naresh; Bauchy, Mathieu; Krishnan, N. M. Anoop
    Frontiers in Materials, 2019. doi: 10.3389/fmats.2019.00239

  15. Evidence of a two-dimensional glass transition in graphene: Insights from molecular simulations
    Ravinder, R.; Kumar, Rajesh; Agarwal, Manish; Krishnan, N. M. Anoop
    Scientific Reports, 2019. doi: 10.1038/s41598-019-41231-z

  16. Density–stiffness scaling in minerals upon disordering: Irradiation vs. vitrification
    Krishnan, N. M. Anoop; Ravinder, R.; Kumar, Rajesh; Le Pape, Yann; Sant, Gaurav; Bauchy, Mathieu
    Acta Materialia, 2019. doi: 10.1016/j.actamat.2019.01.015

Preprints

  1. Lagrangian Neural Network with Differentiable Symmetries and Relational Inductive Bias
    Bhattoo, Ravinder; Ranu, Sayan; Krishnan, N. M. Anoop
    Preprint, 2021. url: http://arxiv.org/abs/2110.03266

Conference Papers

  1. Learning the Dynamics of Physical Systems with Hamiltonian Graph Neural Networks
    Bishnoi, Suresh; Bhattoo, Ravinder; Jayadeva, Jayadeva; Ranu, Sayan; Krishnan, N. M. Anoop
    ICLR 2023 Workshop on Physics for Machine Learning, 2023. url: https://openreview.net/forum?id=Ugl-B_at5n

  2. Enhancing the Inductive Biases of Graph Neural ODE for Modeling Physical Systems
    Bishnoi, Suresh; Bhattoo, Ravinder; Jayadeva, Jayadeva; Ranu, Sayan; Krishnan, N. M. Anoop
    The Eleventh International Conference on Learning Representations, 2023. url: https://openreview.net/forum?id=ATLEl_izD87

  3. Unravelling the Performance of Physics-informed Graph Neural Networks for Dynamical Systems
    Thangamuthu, Abishek; Kumar, Gunjan; Bishnoi, Suresh; Bhattoo, Ravinder; Krishnan, N. M. Anoop; Ranu, Sayan
    Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track, 2022. url: https://openreview.net/forum?id=tXEe-Ew_ikh

  4. Learning Articulated Rigid Body Dynamics with Lagrangian Graph Neural Network
    Bhattoo, Ravinder; Ranu, Sayan; Krishnan, N. M. Anoop
    Advances in Neural Information Processing Systems, 2022. url: https://openreview.net/forum?id=nOdfIbo3A-F

Conference Talks

  1. Lagrangian and Hamiltonian Graph Neural Networks for Robust Molecular Simulations
    Ravinder Bhattoo*, N. M. Anoop Krishnan
    XXX International Materials Research Congress (IMRC2022) and International Conference on Advanced Materials (ICAM2021), August, 2022
    Cancun, Mexico

  2. Lagrangian and Hamiltonian Graph Neural Networks for Robust Molecular Simulations
    Ravinder Bhattoo*, N. M. Anoop Krishnan
    XXX International Materials Research Congress (IMRC2022) and International Conference on Advanced Materials (ICAM2021), August, 2022
    Cancun, Mexico

  3. Understanding the Compositional Control on Electrical, Mechanical, Optical, And Physical Properties of Inorganic Glasses with Interpretable Machine Learning
    Ravinder Bhattoo*, N. M. Anoop Krishnan
    XXX International Materials Research Congress (IMRC2022) and International Conference on Advanced Materials (ICAM2021), August, 2022
    Cancun, Mexico

  4. PeriDyn: A Peridynamics Package Written in Julia Programming Language
    Ravinder Bhattoo*, N. M. Anoop Krishnan
    11th European Solid Mechanics Conference, July, 2022
    NUI, Galway, Ireland

  5. Learning Quantum-accuracy Interatomic Potential for Silica Using Lagrangian Graph Neural Networks
    Ravinder Bhattoo*, N. M. Anoop Krishnan
    2022 Glass and Optical Materials Division Annual Meeting, May, 2022
    Hyatt Regency Baltimore, Baltimore, MD, United States

  6. Learning interaction laws in atomistic system using Lagrangian Graph Neural Networks
    Ravinder Bhattoo*, N. M. Anoop Krishnan
    2022 Glass and Optical Materials Division Annual Meeting, May, 2022
    Hyatt Regency Baltimore, Baltimore, MD, United States

  7. Decoding the Genome of Inorganic Glasses using Interpretable Machine Learning
    Ravinder Bhattoo*, N. M. Anoop Krishnan
    14th Pacific Rim Conference on Ceramic and Glass Technology and GOMD 2021 Division Meeting, December, 2021
    Vancouver, British Columbia, Canada (Virtual)

  8. Molecular Dynamics Simulation Using Graph Neural Networks
    Ravinder Bhattoo*, N. M. Anoop Krishnan
    MRS Fall Meeting 2021, December, 2021
    Boston, Massachusetts, USA (Virtual)

  9. Understanding the Composition-property Relationship of Glasses Using Interpretable Machine Learning
    Ravinder Bhattoo*, Suresh Bishnoi, M. Zaki, N. M. Anoop Krishnan
    Materials Science and Technology (MS&T) 2021, October, 2021
    Columbus, Ohio, USA (Virtual)

  10. Machine learning to predict the elastic properties of glasses
    Sourabh Singh, Suresh Bishnoi, R. Ravinder*, Hariprasad Kodamana, N. M. Anoop Krishnan
    Material Science and Technology (MS&T) 2019, October, 2019
    Oregon Convocation Center, Portland, USA

Workshops

  1. Introduction to Machine Learning Tools
    Artificial Intelligence Concepts and Multidisciplinary Applications in Modern Biology, September, 2019
    International Center for Genetic Engineering and Biotechnology, New Delhi, India

  2. Introduction to Machine Learning
    Machine Learning For Engineering Applications (TEQIP Course), June, 2019
    Indian Institute of Technology Delhi, New Delhi, India

  3. Molecular dynamics workshop 2
    Advanced Simulation Methods: DFT, MD and Beyond, March, 2019
    Indian Institute of Technology Delhi, New Delhi, India

Posters

  1. Designing Functional Glasses using Machine Learning
    R. Ravinder*, Suresh Bishnoi, Sourabh Kumar Singh, Hargun Singh, Hariprasad Kodamana, N M Anoop Krishnan
    IIT Delhi Industry Day 2019, September, 2019
    Indian Institute of Technology Delhi, New Delhi, India

  2. Two-dimensional glass transition in graphene: Insights from molecular simulations
    R. Ravinder*, Rajesh Kumar, Manish Agarwal, N. M. Anoop Krishnan
    Advanced Simulation Methods: DFT, MD and Beyond, March, 2019
    Indian Institute of Technology Delhi, New Delhi, India

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