Key Papers

  • Machine-learning predictions of polymer properties with Polymer Genome

    H. D. Tran, C. Kim, L. Chen, A. Chandrasekaran, R. Batra, S. Venkatram, D. Kamal, J. P. Lightstone, R. Gurnani, P. Shetty, M. Ramprasad, J. Laws, M. Shelton, R. Ramprasad, J. Appl. Phys., 128, 171104 (2020).

  • Polymer Genome: A data-powered polymer informatics platform for property predictions

    C. Kim, A. Chandrasekaran, T. D. Huan, D. Das, R. Ramprasad, J. Phys. Chem. C 122, 31, 17575-17585 (2018).

  • A polymer dataset for accelerated property prediction and design

    T. D. Huan, A. Mannodi-Kanakkithodi, C. Kim, V. Sharma, G. Pilania, R. Ramprasad, Sci. Data, 3 160012 (2016).

  • Copolymer informatics with multitask deep neural networks

    C. Kuenneth, W. Schertzer, R. Ramprasad, Macromolecules, 54, 5957 (2021).

  • Polymer informatics with multi-task learning

    Christopher Kuenneth, Arunkumar Chitteth Rajan, Huan Tran, Lihua Chen, Chiho Kim, Rampi Ramprasad, Patterns, 2, 4, 100238 (2021).

  • Data-assisted Polymer Retrosynthesis Planning

    Chen Lihua, Joseph Kern, Jordan P. Lightstone and Rampi Ramprasad, Applied Physics Reviews 8, 15, July (2021).

Relevant Papers

  • A Deep Learning Solvent-Selection Paradigm Powered by a Massive Solvent/Nonsolvent Database for Polymers

    A. Chandrasekaran, C. Kim, S. Venkatram, R. Ramprasad, Macromolecules 2020

  • Refractive index prediction models for polymers using machine learning

    J. P. Lightstone, L. Chen, C. Kim, R. Batra, R. Ramprasad, J. Appl. Phys., 127, 21, 215105 (2020).

  • Frequency-dependent dielectric constant prediction of polymers using machine learning

    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, Npj Comput. Mater. 6, 61 (2020).

  • Polymer genome–based prediction of gas permeabilities in polymers

    G. Zhu, C. Kim, A. Chandrasekaran, J. D. Everett, R. Ramprasad, R. P. Lively, J. Polym. Eng., 20190329, ISSN (Online) 2191-0340 (2020).

  • A Critical Assessment of the Hildebrand and Hansen Solubility Parameters for Polymers

    S. Venkatram, C. Kim, A. Chandrasekaran, R. Ramprasad, J. Chem. Inf. Model., 59, 10, 4188-4194 (2019).

  • Impact of dataset uncertainties on machine learning model predictions: the example of polymer glass transition temperatures

    A. Jha, A. Chandrasekaran, C. Kim, R Ramprasad, Modelling Simul. Mater. Sci. Eng. 27, 024002 (2019).

  • Scoping the polymer genome: a roadmap for rational polymer dielectrics design and beyond

    A. Mannodi-Kanakkithodi, A. Chandrasekaran, C. Kim, T. D. Huan, G. Pilania, V. Botu, R. Ramprasad, Materials Today 21, 785-796 (2018).

  • Rational co-design of polymer dielectrics for energy storage

    A. Mannodi-Kanakkithodi, G. M. Treich, T. D. Huan, R. Ma, M. Tefferi, Y. Cao, G A. Sotzing, R. Ramprasad, Adv. Mater. 28, 6277 (2016).

  • Machine learning strategy for accelerated design of polymer dielectrics

    A. Mannodi-Kanakkithodi, G. Pilania, T. D. Huan, T. Lookman, R. Ramprasad, Sci. Rep. 6, 20952 (2016).

  • Accelerated materials property predictions and design using motif-based fingerprints

    T. D. Huan, A. Mannodi-Kanakkithodi, R. Ramprasad, Phys. Rev. B 92, 014106 (2015).