Publications de Sandip De | EPFL Graph Search

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 ...

Iop Publishing Ltd2024

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

An assessment of the structural resolution of various fingerprints commonly used in machine learning

Sandip De

Atomic environment fingerprints are widely used in computational materials science, from machine learning potentials to the quantification of similarities between atomic configurations. Many approaches to the construction of such fingerprints, also called ...

2021

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, 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 has spent a considerable effort in designing representations tha ...

2019

Applications of machine learning for studying local solvation environments

Michele Ceriotti, Sandip De

ACS2018

Machine learning for the structure–energy–property landscapes of molecular crystals

Félix Benedito Clément Musil, Michele Ceriotti, Sandip De

2018

Chemical shifts in molecular solids by machine learning

David Lyndon Emsley, Michele Ceriotti, Maxime Lagier, Félix Benedito Clément Musil, Albert Hofstetter, Sandip De, Federico Maria Paruzzo

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 ...

Nature Research2018

Large-Scale Computational Screening of Molecular Organic Semiconductors Using Crystal Structure Prediction

Michele Ceriotti, Sandip De

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

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