Publications

VG 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, Vijith P, T.K. Patra, Z. Chen, T.P. Russell, S.K.R.S. Sankaranarayana, B.G. Sumpter, 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, SKRS 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, et al. "Efficient probabilistic computing with stochastic perovskite nickelates." Nano Letters 22.21, 8654 (2022).

R. Batra et al. “Machine learning overcomes human bias in the discovery of self-assembling peptides.” Nature Chemistry 1-9, (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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, R. Ramprasad,“A comprehensive computational study of adatom diffusion on the aluminum (100) surface”, Computational Materials Science, 158, 15 (2019).

C. Kunneth, R. Batra, G. A. Rossetti, R. Ramprasad, A. Kersch, “Thermodynamics of phase stability and ferroelectricity in HfO2 from first-principles”, Ferroelectricity in Doped Hafnium Oxide, 1st edition, U. Schroeder, C. S. Hwang, and H. Funakubo, Eds. Elsevier Ltd, 245–291 (2019). (ISBN: 9780081024300)

R. Batra, T. D. Huan, C. Kim, J. Chapman, L. Chen, A. Chandrasekaran, 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, 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. Purja Pun, R. Batra, R. Ramprasad, 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, R. Ramprasad, “Electrochemical stability window of polymeric electrolytes”, Chemistry of Materials, 31, 4598 (2019).

L. Chen, H. Tran, R. Batra, C. Kim, 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, 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, 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, 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., 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, 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, C. Kim, “Machine learning and materials informatics: Recent applications and prospects”, npj Computational Materials, 3, 54 (2017).

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

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