Concept
Potentiel interatomique
Publications associées (164)
Thermal transport of glasses via machine learning driven simulations
Accessing the thermal transport properties of glasses is a major issue for the design of production strategies of glass industry, as well as for the plethora of applications and devices where glasses are employed. From the computational standpoint, the che ...
Lausanne2024Efficient and insightful descriptors for representing molecular and material space
Data-driven approaches have been applied to reduce the cost of accurate computational studies on materials, by using only a small number of expensive reference electronic structure calculations for a representative subset of the materials space, and using ...
EPFL2024Machine-learned interatomic potentials: Recent developments and prospective applications
High-throughput generation of large and consistent ab initio data combined with advanced machine-learning techniques are enabling the creation of interatomic potentials of near ab initio quality. This capability has the potential of dramatically impacting ...
Heidelberg2023Comparing transferability in neural network approaches and linear models for machine-learning interaction potentials
Atomic simulations using machine learning interatomic potential (MLIP) have gained a lot of popularity owing to their accuracy in comparison to conventional empirical potentials. However, the transferability of MLIP to systems outside the training set pose ...
AMER PHYSICAL SOC2023Universal machine learning for the response of atomistic systems to external fields
Machine learned interatomic interaction potentials have enabled efficient and accurate molecular simulations of closed systems. However, external fields, which can greatly change the chemical structure and/or reactivity, have been seldom included in curren ...
Berlin2023Factorized structure of the long-range two-electron integrals tensor and its application in quantum chemistry
We introduce two new approximation methods for the numerical evaluation of the long-range component of the range-separated Coulomb potential and the approximation of the resulting high dimensional Two-Electron Integrals tensor (TEI) with long-range interac ...
San Diego2023Modelling of metal alloys in realistic conditions with machine learning
Computer simulations based on statistical methods have emerged as a powerful tool for studying structure-property relationships at the atomistic level. However, to provide reliable insights into materials in realistic conditions, it is essential to accurat ...
EPFL2023Modeling and characterization of the nucleation and growth of carbon nanostructures in physical synthesis
Carbon nanostructures formed through physical synthesis come in a variety of sizes and shapes. With the end goal of rationalizing synthetic pathways of carbon nanostructures as a function of tunable parameters in the synthesis, we investigate how the initi ...
ELSEVIER2023A recipe for cracking the quantum scaling limit with machine learned electron densities
A long-standing goal of science is to accurately simulate large molecular systems using quantum mechanics. The poor scaling of current quantum chemistry algorithms on classical computers, however, imposes an effective limit of about a few dozen atoms on tr ...
IOP Publishing Ltd2023Neural Network Potentials for Age Hardening Aluminum Alloys
High-strength metal alloys achieve their performance via careful control of precipitates and solutes.The nucleation, growth, and kinetics of precipitation, and the resulting mechanical properties, are inherently atomic scale phenomena, particularly during ...
EPFL2022