Rampi Ramprasad

Research interests

Publications

• Physically informed artificial neural networks for atomistic modeling of materials

  RePEc: Research Papers in Economics ·

• Physically informed artificial neural networks for atomistic modeling of materials

  RePEc: Research Papers in Economics ·

• Design of functional and sustainable polymers assisted by artificial intelligence

  Nature Reviews Materials · 2024 · 10.1038/s41578-024-00708-8

• Design of functional and sustainable polymers assisted by artificial intelligence

  Nature Reviews Materials · 2024 · 10.1038/s41578-024-00708-8

• polyBERT: a chemical language model to enable fully machine-driven ultrafast polymer informatics

  Nature Communications · 2023 · 10.1038/s41467-023-39868-6

• polyBERT: a chemical language model to enable fully machine-driven ultrafast polymer informatics

  Nature Communications · 2023 · 10.1038/s41467-023-39868-6

• Machine-learning predictions of polymer properties with Polymer Genome

  Journal of Applied Physics · 2020 · 10.1063/5.0023759

• Machine-learning predictions of polymer properties with Polymer Genome

  Journal of Applied Physics · 2020 · 10.1063/5.0023759

• Flexible Temperature‐Invariant Polymer Dielectrics with Large Bandgap

  Advanced Materials · 2020 · 10.1002/adma.202000499

• Emerging materials intelligence ecosystems propelled by machine learning

  Nature Reviews Materials · 2020 · 10.1038/s41578-020-00255-y

• Polymer design using genetic algorithm and machine learning

  Computational Materials Science · 2020 · 10.1016/j.commatsci.2020.110067

• Flexible Temperature‐Invariant Polymer Dielectrics with Large Bandgap

  Advanced Materials · 2020 · 10.1002/adma.202000499

• Emerging materials intelligence ecosystems propelled by machine learning

  Nature Reviews Materials · 2020 · 10.1038/s41578-020-00255-y

• Polymer design using genetic algorithm and machine learning

  Computational Materials Science · 2020 · 10.1016/j.commatsci.2020.110067

• Solving the electronic structure problem with machine learning

  npj Computational Materials · 2019 · 10.1038/s41524-019-0162-7

• Solving the electronic structure problem with machine learning

  npj Computational Materials · 2019 · 10.1038/s41524-019-0162-7

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

  Journal of Chemical Information and Modeling · 2019 · 10.1021/acs.jcim.9b00656

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

  Journal of Chemical Information and Modeling · 2019 · 10.1021/acs.jcim.9b00656

• Polymer Genome: A Data-Powered Polymer Informatics Platform for Property Predictions

  The Journal of Physical Chemistry C · 2018 · 10.1021/acs.jpcc.8b02913

• Polymer Genome: A Data-Powered Polymer Informatics Platform for Property Predictions

  The Journal of Physical Chemistry C · 2018 · 10.1021/acs.jpcc.8b02913

• A universal strategy for the creation of machine learning-based atomistic force fields

  npj Computational Materials · 2017 · 10.1038/s41524-017-0042-y

• Machine learning in materials informatics: recent applications and prospects

  npj Computational Materials · 2017 · https://doi.org/10.1038/s41524-017-0056-5

• A universal strategy for the creation of machine learning-based atomistic force fields

  npj Computational Materials · 2017 · 10.1038/s41524-017-0042-y

• Mesoporous MoO 3– x Material as an Efficient Electrocatalyst for Hydrogen Evolution Reactions

  Advanced Energy Materials · 2016 · 10.1002/aenm.201600528

• Advanced polymeric dielectrics for high energy density applications

  Progress in Materials Science · 2016 · 10.1016/j.pmatsci.2016.05.001

• Machine Learning Force Fields: Construction, Validation, and Outlook

  The Journal of Physical Chemistry C · 2016 · 10.1021/acs.jpcc.6b10908

• Advanced polymeric dielectrics for high energy density applications

  Progress in Materials Science · 2016 · 10.1016/j.pmatsci.2016.05.001

• Machine Learning Strategy for Accelerated Design of Polymer Dielectrics

  Scientific Reports · 2016 · 10.1038/srep20952

• Machine Learning in Materials Science

  Reviews in computational chemistry · 2016 · 10.1002/9781119148739.ch4

• Machine Learning Force Fields: Construction, Validation, and Outlook

  The Journal of Physical Chemistry C · 2016 · 10.1021/acs.jpcc.6b10908

• Machine Learning Strategy for Accelerated Design of Polymer Dielectrics

  Scientific Reports · 2016 · 10.1038/srep20952

• Mesoporous MoO 3– x Material as an Efficient Electrocatalyst for Hydrogen Evolution Reactions

  Advanced Energy Materials · 2016 · 10.1002/aenm.201600528

• Machine learning bandgaps of double perovskites

  Scientific Reports · 2016 · 10.1038/srep19375

• Machine Learning in Materials Science

  Reviews in computational chemistry · 2016 · 10.1002/9781119148739.ch4

• Machine learning bandgaps of double perovskites

  Scientific Reports · 2016 · 10.1038/srep19375

• Rational design of all organic polymer dielectrics

  Nature Communications · 2014 · 10.1038/ncomms5845

• Adaptive machine learning framework to accelerate ab initio molecular dynamics

  International Journal of Quantum Chemistry · 2014 · 10.1002/qua.24836

• Rational design of all organic polymer dielectrics

  Nature Communications · 2014 · 10.1038/ncomms5845

• Pathways towards ferroelectricity in hafnia

  Physical Review B · 2014 · 10.1103/physrevb.90.064111

• Pathways towards ferroelectricity in hafnia

  Physical Review B · 2014 · 10.1103/physrevb.90.064111

• Adaptive machine learning framework to accelerate ab initio molecular dynamics

  International Journal of Quantum Chemistry · 2014 · 10.1002/qua.24836

Current projects

No projects listed.