Deepak Kamal | IIT Madras (original) (raw)

Address: Madras, Tamil Nadu, India

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Papers by Deepak Kamal

Research paper thumbnail of Design of novel polymer-metal interfaces using first principles-informed artificial intelligence techniques

Bulletin of the American Physical Society, Mar 16, 2021

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Research paper thumbnail of Deep Well Trapping of Hot Carriers in a Hexagonal Boron Nitride Coating of Polymer Dielectrics

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Research paper thumbnail of A charge density prediction model for organic molecules using Deep Neural Networks

Bulletin of the American Physical Society, 2019

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Research paper thumbnail of Dielectric Polymers Tolerant to Electric Field and Temperature Extremes: Integration of Phenomenology, Informatics, and Experimental Validation

ACS Applied Materials & Interfaces, 2021

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Research paper thumbnail of Tuning Surface States of Metal/Polymer Contacts Toward Highly Insulating Polymer-Based Dielectrics

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Research paper thumbnail of polyG2G: A Novel Machine Learning Algorithm Applied to the Generative Design of Polymer Dielectrics

Chemistry of Materials, 2021

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Research paper thumbnail of EZFF: Python library for multi-objective parameterization and uncertainty quantification of interatomic forcefields for molecular dynamics

SoftwareX, 2021

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Research paper thumbnail of Machine-learning predictions of polymer properties with Polymer Genome

Journal of Applied Physics, 2020

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Research paper thumbnail of Novel high voltage polymer insulators using computational and data-driven techniques

The Journal of Chemical Physics, 2021

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Research paper thumbnail of Computable Bulk and Interfacial Electronic Structure Features as Proxies for Dielectric Breakdown of Polymers

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Research paper thumbnail of Solving the electronic structure problem with machine learning

npj Computational Materials, 2019

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Research paper thumbnail of A charge density prediction model for hydrocarbons using deep neural networks

Machine Learning: Science and Technology, 2020

The electronic charge density distribution ρ(r) of a given material is among the most fundamental... more The electronic charge density distribution ρ(r) of a given material is among the most fundamental quantities in quantum simulations from which many large scale properties and observables can be calculated. Conventionally, ρ(r) is obtained using Kohn–Sham density functional theory (KS-DFT) based methods. But, the high computational cost of KS-DFT renders it intractable for systems involving thousands/millions of atoms. Thus, recently there has been efforts to bypass expensive KS equations, and directly predict ρ(r) using machine learning (ML) based methods. Here, we build upon one such scheme to create a robust and reliable ρ(r) prediction model for a diverse set of hydrocarbons, involving huge chemical and morphological complexity /(saturated, unsaturated molecules, cyclo-groups and amorphous and semi-crystalline polymers). We utilize a grid-based fingerprint to capture the atomic neighborhood around an arbitrary point in space, and map it to the reference ρ(r) obtained from standar...

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Research paper thumbnail of Design of novel polymer-metal interfaces using first principles-informed artificial intelligence techniques

Bulletin of the American Physical Society, Mar 16, 2021

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Deep Well Trapping of Hot Carriers in a Hexagonal Boron Nitride Coating of Polymer Dielectrics

Bookmarks Related papers MentionsView impact

Research paper thumbnail of A charge density prediction model for organic molecules using Deep Neural Networks

Bulletin of the American Physical Society, 2019

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Research paper thumbnail of Dielectric Polymers Tolerant to Electric Field and Temperature Extremes: Integration of Phenomenology, Informatics, and Experimental Validation

ACS Applied Materials & Interfaces, 2021

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Research paper thumbnail of Tuning Surface States of Metal/Polymer Contacts Toward Highly Insulating Polymer-Based Dielectrics

Bookmarks Related papers MentionsView impact

Research paper thumbnail of polyG2G: A Novel Machine Learning Algorithm Applied to the Generative Design of Polymer Dielectrics

Chemistry of Materials, 2021

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Research paper thumbnail of EZFF: Python library for multi-objective parameterization and uncertainty quantification of interatomic forcefields for molecular dynamics

SoftwareX, 2021

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Research paper thumbnail of Machine-learning predictions of polymer properties with Polymer Genome

Journal of Applied Physics, 2020

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Research paper thumbnail of Novel high voltage polymer insulators using computational and data-driven techniques

The Journal of Chemical Physics, 2021

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Research paper thumbnail of Computable Bulk and Interfacial Electronic Structure Features as Proxies for Dielectric Breakdown of Polymers

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Research paper thumbnail of Solving the electronic structure problem with machine learning

npj Computational Materials, 2019

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Research paper thumbnail of A charge density prediction model for hydrocarbons using deep neural networks

Machine Learning: Science and Technology, 2020

The electronic charge density distribution ρ(r) of a given material is among the most fundamental... more The electronic charge density distribution ρ(r) of a given material is among the most fundamental quantities in quantum simulations from which many large scale properties and observables can be calculated. Conventionally, ρ(r) is obtained using Kohn–Sham density functional theory (KS-DFT) based methods. But, the high computational cost of KS-DFT renders it intractable for systems involving thousands/millions of atoms. Thus, recently there has been efforts to bypass expensive KS equations, and directly predict ρ(r) using machine learning (ML) based methods. Here, we build upon one such scheme to create a robust and reliable ρ(r) prediction model for a diverse set of hydrocarbons, involving huge chemical and morphological complexity /(saturated, unsaturated molecules, cyclo-groups and amorphous and semi-crystalline polymers). We utilize a grid-based fingerprint to capture the atomic neighborhood around an arbitrary point in space, and map it to the reference ρ(r) obtained from standar...

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