Deepak Kamal | IIT Madras (original) (raw)
Address: Madras, Tamil Nadu, India
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Papers by Deepak Kamal
Bulletin of the American Physical Society, Mar 16, 2021
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Bulletin of the American Physical Society, 2019
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ACS Applied Materials & Interfaces, 2021
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Chemistry of Materials, 2021
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SoftwareX, 2021
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Journal of Applied Physics, 2020
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The Journal of Chemical Physics, 2021
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npj Computational Materials, 2019
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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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Bulletin of the American Physical Society, Mar 16, 2021
Bookmarks Related papers MentionsView impact
Bookmarks Related papers MentionsView impact
Bulletin of the American Physical Society, 2019
Bookmarks Related papers MentionsView impact
ACS Applied Materials & Interfaces, 2021
Bookmarks Related papers MentionsView impact
Bookmarks Related papers MentionsView impact
Chemistry of Materials, 2021
Bookmarks Related papers MentionsView impact
SoftwareX, 2021
Bookmarks Related papers MentionsView impact
Journal of Applied Physics, 2020
Bookmarks Related papers MentionsView impact
The Journal of Chemical Physics, 2021
Bookmarks Related papers MentionsView impact
Bookmarks Related papers MentionsView impact
npj Computational Materials, 2019
Bookmarks Related papers MentionsView impact
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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