S
Sandip De
Researcher at École Polytechnique Fédérale de Lausanne
Publications - 35
Citations - 2473
Sandip De is an academic researcher from École Polytechnique Fédérale de Lausanne. The author has contributed to research in topics: Energy landscape & Potential energy surface. The author has an hindex of 17, co-authored 33 publications receiving 1894 citations. Previous affiliations of Sandip De include IBM & Saha Institute of Nuclear Physics.
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Machine learning unifies the modeling of materials and molecules
Albert P. Bartók,Sandip De,Carl Poelking,Noam Bernstein,James R. Kermode,Gábor Csányi,Michele Ceriotti +6 more
TL;DR: A machine-learning model, based on a local description of chemical environments and Bayesian statistical learning, provides a unified framework to predict atomic-scale properties and captures the quantum mechanical effects governing the complex surface reconstructions of silicon.
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Comparing molecules and solids across structural and alchemical space.
TL;DR: In this article, a regularized entropy match (REMatch) approach was proposed to describe the similarity of both molecular and bulk periodic structures, introducing powerful metrics that enable the navigation of alchemical and structural complexities within a unified framework.
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Energy landscape of fullerene materials: a comparison of boron to boron nitride and carbon.
TL;DR: Using the minima hopping global geometry optimization method on the density functional potential energy surface, this article showed that the energy landscape of boron clusters is glass-like and that the structures of these structures have many structures which are lower in energy than the cages.
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Machine learning for the structure–energy–property landscapes of molecular crystals
TL;DR: Polymorphism is common in molecular crystals, whose energy landscapes usually contain many structures with similar stability, but very different physical–chemical properties, and machine-learning techniques can accelerate the evaluation of energy and properties by side-stepping accurate but demanding electronic-structure calculations.
Journal Article
Energy landscape of fullerene materials: A comparion of boron to boron nitride and carbon
TL;DR: This work uses the minima hopping global geometry optimization method on the density functional potential energy surface to show that the energy landscape of boron clusters is glasslike, and presents a methodology which can make predictions on the feasibility of the synthesis of new nanostructures.