Person

Sandip De

This person is no longer with EPFL

Related publications (11)

Surface segregation in high-entropy alloys from alchemical machine learning

Michele Ceriotti, Guillaume André Jean Fraux, Arslan Mazitov, Sandip De, Nataliya Lopanitsyna

High-entropy alloys (HEAs), containing several metallic elements in near-equimolar proportions, have long been of interest for their unique mechanical properties. More recently, they have emerged as a promising platform for the development of novel heterog ...

Bristol2024

Modeling high-entropy transition metal alloys with alchemical compression

Michele Ceriotti, Guillaume André Jean Fraux, Sandip De, Nataliya Lopanitsyna

Alloys composed of several elements in roughly equimolar composition, often referred to as high-entropy alloys, have long been of interest for their thermodynamics and peculiar mechanical properties, and more recently for their potential application in cat ...

AMER PHYSICAL SOC2023

Machine Learning-Guided Approach for Studying Solvation Environments

Sandip De, Yasemin Basdogan

Molecular-level understanding and characterization of solvation environments are often needed across chemistry, biology, and engineering. Toward practical modeling of local solvation effects of any solute in any solvent, we report a static and all-quantum ...

AMER CHEMICAL SOC2020

Chemical machine learning with kernels: The impact of loss functions

Volkan Cevher, Quoc Tran Dinh, Sandip De, Junhong Lin

Machine learning promises to accelerate materials discovery by allowing computational efficient property predictions from a small number of reference calculations. As a result, the literature has spent a considerable effort in designing representations tha ...

2019

Chemical machine learning with kernels: The key impact of loss functions

Volkan Cevher, Sandip De, Junhong Lin, Van Quang Nguyen

Machine learning promises to accelerate materials discovery by allowing computational efficient property predictions from a small number of reference calculations. As a result, the literature spent a considerable effort in designing representations that ca ...

2018

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Due to their strong dependence on local atonic environments, NMR chemical shifts are among the most powerful tools for strucutre elucidation of powdered solids or amorphous materials. Unfortunately, using them for structure determination depends on the abi ...

2018

Predictive computational methods have the potential to significantly accelerate the discovery of new materials with targeted properties by guiding the choice of candidate materials for synthesis. Recently, a planar pyrrole-based azaphenacene molecule (pyri ...

2018

Mapping and classifying molecules from a high-throughput structural database

Michele Ceriotti, Félix Benedito Clément Musil, Sandip De, Teresa Ingram, Carsten Baldauf

High-throughput computational materials design promises to greatly accelerate the process of discovering new materials and compounds, and of optimizing their properties. The large databases of structures and properties that result from computational search ...

Biomed Central Ltd2017

Impact of oxygen plasma on nitrided and annealed atomic layer deposited SiO2/high-k/metal gate for high-voltage input and output fin-shaped field effect transistor devices

Sandip De

In this study, the authors investigate the impact of radical oxygen plasma on nitrided and annealed atomic layer deposited (ALD) SiO2 as a thick gate oxide (1.65-3 V) with a high-k/metal gate transistor. Time-dependent-dielectric-breakdown voltage, seconda ...

A V S Amer Inst Physics2017

Machine learning unifies the modeling of materials and molecules

Michele Ceriotti, Sandip De

Determining the stability ofmolecules and condensed phases is the cornerstone of atomisticmodeling, underpinning our understanding of chemical andmaterials properties and transformations. We show that amachine-learningmodel, based on a local description of ...

Amer Assoc Advancement Science2017

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