Publications

A.K. Sundaram, M. Chakraborty, S.M.K. Devathi, B.P. Prusty and R. Batra, "Automated Extraction of Multicomponent Alloy Data Using Large Language Models for Sustainable Design." Advanced Science (2026): e75916.

M.K. Panampilly, R. Batra, and K.C.H. Kumar. "Data-driven prediction of martensite start temperature (Ms) of steels." Calphad 93 (2026): 102950.

P.K. Rana, A. Vyawahare, R. Batra, and S.K. Yadav, "Prediction of new Ti-N phases using machine learned interatomic potential." Computational Materials Science 266 (2026): 114532.

B. Varughese, T.D. Loeffler, S. Banik, A. Koneru, S. Manna, K. Balasubramanian, R. Batra, M.J. Cherukara, O. Yildiz, T. Peterka and B.G. Sumpter, "Physically interpretable interatomic potentials via symbolic regression and reinforcement learning." npj Computational Materials (2026).

V. Parambil, H. Goyal, U. Tripathi, and R. Batra, "TransChem: A hybrid transformer and cheminformatics based framework for enhanced polymer informatics." Materials Today Communications (2025): 113375.

SSS Gadhavajhala, VP Kannan, AJ Kale, R Batra, SR Mishra, and B Srinivasan. "Magnetic Frameworks and Non‐Magnetic Dopants: A Novel Strategy for Enhancing Thermoelectric Efficiency in Manganese Telluride–Mechanistic Insights into Charge Carrier Dynamics and Phonon Transport." Small 21, no. 25 (2025): 2411481.

Y.N. Talluri, S.K.R.S. Sankaranarayanan, H.C. Fry, and R. Batra, "Discovery of unconventional and nonintuitive self-assembling peptide materials using experiment-driven machine learning." Science Advances 11, no. 24 (2025): eadt9466.

K. Kumari, R. Thiyagarajan, A.J. Kale, R. Batra, S. Krishanmurty, K. Sethupathi, and M.S.R. Rao, "Probing phonon anharmonicity of multilayer T i 3 C 2 T x (T: OH, O, or F) MXene through temperature-and pressure-dependent Raman studies." Physical Review B 111. 11, 115411 (2025)

C. Kunneth, R. Batra, G. A. Rossetti, R. Ramprasad and A. Kersch, “Thermodynamics of phase stability and ferroelectricity from first-principles”, Ferroelectricity in Doped Hafnium Oxide, 415-457, Woodhead Publishing (2025)

V. Parambil, U. Tripathi, H. Goyal, and R. Batra, "Polymer Property Prediction Using Machine Learning." Materials Informatics III: Polymers, Solvents and Energetic Materials, pp. 119-147 (2025)

C. Wang, Y.J. Kim, A. Vriza, R. Batra, A. Baskaran, N. Shan, N. Li, P. Darancet, L. Ward, Y. Liu and M.K.Y. Chan, "Autonomous platform for solution processing of electronic polymers.", Nature Communications 16, 1498 (2025)

P. Vashishtha, H.G. Kattamuri, N. Thawari, M. Amirthalingam and R. Batra, "Reusability report: Deep learning-based analysis of images and spectroscopy data with AtomAI", Nature Machine Intelligence, 1-6 (2025)

K. Balasubramanian, S. Manna, S. Banik, S Srinivasan and R. Batra, ''Machine learning enabled discovery of superhard and ultrahard carbon polymorphs.'', Computational Materials Science 246, 113506 (2025)

V.G. Abhijitha, R. Batra, and B. R. K. Nanda, "Single Transition Metal Atom Catalyst for a High-Performance Li–S Battery with a Graphdiyne–Graphene Heterostructure Host: A DFT Investigation+ ML Predictions." ACS Catalysis 14, 8874 (2024).

J.M.Y. Carillo, V Parambil, T.K. Patra, Z. Chen, T.P. Russell, S.K.R.S. Sankaranarayana, B.G. Sumpter and R. Batra, "Accelerated Sequence Design of Star Block Copolymers: An Unbiased Exploration Strategy via Fusion of Molecular Dynamics Simulations and Machine Learning", The Journal of Physical Chemistry B 128, 17, 4220 (2024)

B. Varughese, S. Manna, T.D. Loeffler, R. Batra, M.J. Cherukara and S.K.R.S. Sankarnarayanan, "Active and Transfer Learning of High-Dimensional Neural Network Potentials for Transition Metals", ACS Appl. Mater. Interfaces 16, 16, 20681 (2024)

J. M. Y. Carrillo, Vijith P, T. K. Patra, Z. Chen, T. P. Russell, S.K.R.S Sankaranarayanan, B. G. Sumpter, R. Batra, ‘’Accelerated Design of Block Copolymers: An Unbiased Exploration Strategy via Fusion of Molecular Dynamics Simulations and Machine Learning”, arXiv.org (2023)

T. J. Park, K. Selcuk, H. T. Zhang, S. Manna, R. Batra, Q. Wang, H. Yu, N.A. Aadit, S.K.R.S. Sankaranarayanan, H. Zhou and K.Y. Camsari, "Efficient probabilistic computing with stochastic perovskite nickelates." Nano Letters 22.21, 8654 (2022).

R. Batra, T.D. Loeffler, H. Chan,S. Srinivasan, H. Cui, I.V. Korendovych, V. Nanda, L.C. Palmer, L.A. Solomon, H.C. Fry and S.K.R.S. Sankaranarayan “Machine learning overcomes human bias in the discovery of self-assembling peptides.” Nature Chemistry 14.12, 1427 (2022)

S. Srinivasan, R. Batra, D. Luo, T. Loeffler, S. Manna, H. Chan, L. Yang, W. Yang, J. Wen, P. Darancet and S. K.R.S. Sankaranarayanan "Machine learning the metastable phase diagram of covalently bonded carbon." Nature Communications 13.1, 1 (2022).

A. Koneru, R. Batra, S. Manna, T. D. Loeffler, H. Chan, M. Sternberg, A. Avarca, H. Singh, M. J. Cherukara and S. K. R. S. Sankaranarayanan, "Multi-reward reinforcement learning based bond-order potential to study strain-assisted phase transitions in phosphorene." The Journal of Physical Chemistry Letters 13.7, 1886 (2022).

S. Manna, T. D. Loeffler, R. Batra, S. Banik, H. Chan, B. Varughese, K. Sasikumar, M. Sternberg, T. Peterka, M. J. Cherukara, S. K. Gray, B. G. Sumpter and S. K. R. S. Sankaranarayanan, "Learning in continuous action space for developing high dimensional potential energy models." Nature communications 13.1, 1 (2022).

S. Banik, T. D. Loeffler, R. Batra, H. Singh, M. J. Cherukara and S. K. R. S. Sankaranarayanan, "Learning with Delayed Rewards—A Case Study on Inverse Defect Design in 2D Materials." ACS Applied Materials & Interfaces 13.30, 36455 (2021).

S. Srinivasan, R. Batra, H. Chan, G. Kamath, M. J. Cherukara and S. K. R. S. Sankaranarayanan, “Artificial Intelligence-Guided De Novo Molecular Design Targeting COVID-19”, ACS Omega, 6, 19, 12557 (2021).

R. Batra, “Accurate machine learning in materials science facilitated by using diverse data sources”, Nature, 589, 524 (2021).

C. Kim, R. Batra, L. Chen, H. Tran and R. Ramprasad, “Polymer design using genetic algorithm and machine learning”, Computational Materials Science, 186, 110067 (2021).

L. Chen, G. Pilania, R. Batra, T. D. Huan, C. Kim, C. Kuenneth and R. Ramprasad, “Polymer informatics: Current status and critical next steps”, Materials Science and Engineering: R: Reports, 100595, 144, (2021).

R. Batra, L. Song and R. Ramprasad, “Emerging materials intelligence ecosystems propelled by machine learning”, Nature Review Materials, 6.8, 655 (2021).

R. Batra, H. Dai, T. D. Huan, L. Chen, C. Kim, W. R. Gutekunst, L. Song and R. Ramprasad, “Polymers for extreme conditions designed using syntax-directed variational autoencoders”, Chemistry of Materials, 32 (24), 10489 (2020).

R. Batra, C. Chen, T. G. Evans, K. S. Walton and R. Ramprasad, “Prediction of water stability of metal–organic frameworks using machine learning”, Nature Machine Intelligence, 2, 704, (2020).

R. Batra, H. Chan, G. Kamath, R. Ramprasad, M.J. Cherukara and S Sankaranarayanan, “Screening of therapeutic agents for COVID-19 using machine learning and ensemble docking studies”, Journal of Physical Chemistry Letters, 11.17, 7058 (2020).

R. Batra and S. Sankaranarayanan, “Machine learning for multi-fidelity scale bridging and dynamical simulations of materials”, Journal of Physics: Materials, 3.3, 031002 (2020).

D. Kamal, A. Chandrasekaran, R. Batra and R. Ramprasad, “A charge density prediction model for hydrocarbons using deep neural networks”, Machine Learning: Science and Technology, 1, 2, 025003 (2020).

J. Chapman, R. Batra and R. Ramprasad, “Machine learning models for the prediction of energy, forces, and stresses for platinum”, Computational Materials Science, 174, 109483 (2020).

R. Batra, T. D. Huan, B. Johnson, B. Zoellner, P. Maggard, J. L. Jones, G. A. Rossetti and R. Ramprasad, “Search for ferroelectric binary oxides: Chemical and structural space exploration guided by group theory, computations and experiments”, Chemistry of Materials, 32, 9 (2020).

S. Venkatram, R. Batra, L. Chen, C. Kim, M. Shelton and R. Ramprasad, “Predicting crystallization tendency of polymers using multi-fidelity information fusion and machine learning”, Journal of Physical Chemistry B, 124, 28, 6046 (2020).

J. P. Lightstone, L. Chen, C. Kim, R. Batra and R. Ramprasad, “Refractive index prediction models for polymers using machine learning”, Journal of Applied Physics, 127, 21, 215105 (2020).

R. Batra, A. Patra, A. Chandrasekaran, C. Kim, T. D. Huan and R. Ramprasad, “A multi-fidelity information-fusion approach to machine learn and predict polymer bandgap”, Computational Materials Science, 172, 109286 (2020).

L. Chen, C. Kim, R. Batra, J. P. Lightstone, C. Wu, Z. Li, A. A. Deshmukh, Y. Wang, H. D. Tran, P. Vashishta, G. A. Sotzing, Y. Cao and R. Ramprasad, “Frequency-dependent dielectric constant prediction of polymers using machine learning”, npj Computational Materials, 6, 61 (2020).

A. Chandrasekaran, D. Kamal, R. Batra, C. Kim, L. Chen and R. Ramprasad, “Solving the electronic structure problem with machine learning”, npj Computational Materials, 5, 22 (2019).

T. D. Huan, R. Batra, J. Chapman, C. Kim, A. Chandrasekaran and R. Ramprasad, “Iterative-learning strategy for the development of application-specific atomistic force fields”, Journal of Physical Chemistry C, 123, 24 (2019).

J. Chapman, R. Batra, B. P. Uberuaga, G. Pilania and R. Ramprasad, “A comprehensive computational study of adatom diffusion on the aluminum (100) surface”, Computational Materials Science, 158, 15 (2019).

R. Batra, T. D. Huan, C. Kim, J. Chapman, L. Chen, A. Chandrasekaran and R. Ramprasad, “General atomic neighborhood fingerprint for machine learning based methods”, Journal of Physical Chemistry C, 123, 15859 (2019).

R. Batra, G. Pilania, B. P. Uberuaga and R. Ramprasad, “Multi-fidelity information fusion with machine learning: A case study of dopant formation energies in hafnia”, ACS Applied Materials and Interfaces, 11, 24906 (2019).

G.P.P. Pun, R. Batra, R. Ramprasad and Y. Mishin “Physically-informed artificial neural networks for atomistic modeling of materials”, Nature Communications, 10, 2339 (2019).

L. Chen, S. Venkatram, C. Kim, R. Batra, A. Chandrasekaran and R. Ramprasad, “Electrochemical stability window of polymeric electrolytes”, Chemistry of Materials, 31, 4598 (2019).

L. Chen, H. Tran, R. Batra, C. Kim and R. Ramprasad, “Machine learning models for the lattice thermal conductivity prediction of inorganic materials”, Computational Materials Science, 170, 109155 (2019).

S. J. Heo, R. Batra, R. Ramprasad and P. Singh, “Crystal morphology and phase transformation of LiAlO2: Combined experimental and first-principles studies”, Journal of Physical Chemistry C, 122, 50 (2018).

L. Chen, R. Batra, R. Ranganathan, G. Sotzing, Y. Cao and R. Ramprasad, “Electronic structure of polymer dielectrics: The role of chemical and morphological complexity”, Chemistry of Materials, 30, 21 (2018).

R. Batra, T. D. Huan, G. Rossetti and R. Ramprasad, “Dopants promoting ferroelectricity in hafnia: Insights from a comprehensive chemical space exploration”, Chemistry of Materials, 29, 9102 (2017).

R. Batra, T. D. Huan, J. L. Jones, G. Rossetti, Jr. and R. Ramprasad, “Factors favoring ferroelectricity in hafnia: A first-principles computational study”, Journal of Physical Chemistry C, 121, 4139 (2017).

T. D. Huan, R. Batra, J. Chapman, S. Krishnan, L.Chen and R. Ramprasad, “A universal strategy for the creation of machine learning-based atomistic force fields”, npj Computational Materials, 3, 27 (2017).

R. Ramprasad, R. Batra, G. Pilania, A. Mannodi-Kanakkithodi and C. Kim, “Machine learning and materials informatics: Recent applications and prospects”, npj Computational Materials, 3, 54 (2017).

V. Botu, R. Batra, J. Chapman and R. Ramprasad, “Machine learning force fields: Construction, validation, and outlook”, Journal of Physical Chemistry C, 121 (1), 511 (2017).

R. Batra, T. D. Huan and R. Ramprasad, “Stabilization of metastable phases in hafnia owing to surface energy effects”, Applied Physics Letters, 108, 172902 (2016).