Publication
Comparison of permutationally invariant polynomials, neural networks, and Gaussian approximation potentials in representing water interactions through many-body expansions
Related publications (52)
Efficient 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 ...
EPFL2024Thermal 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 ...
Lausanne2024Roughness Evolution Induced by Third-Body Wear
Jean-François Molinari, Antonio Joaquin Garcia Suarez, Tobias Brink
Surface roughness is a key factor when it comes to friction and wear, as well as to other physical properties. These phenomena are controlled by mechanisms acting at small scales, in which the topography of apparently flat surfaces is revealed. Roughness i ...
New York2024Comparing 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 SOC2023Advancing Computational Chemistry with Stochastic and Artificial Intelligence Approaches
Computational chemistry aims to simulate reactions and molecular properties at the atomic scale, advancing the design of novel compounds and materials with economic, environmental, and societal implications. However, the field relies on approximate quantum ...
EPFL2023Universal 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 ...
Berlin2023Learning Physics-Consistent Particle Interactions
Olga Fink, David Simon Kammer, Zhichao Han
Interacting particle systems play a key role in science and engineering. Access to the governing particle interaction law is fundamental for a complete understanding of such systems. However, the inherent system complexity keeps the particle interaction hi ...
Oxford University Press2023Predicting Ion Diffusion from the Shape of Potential Energy Landscapes
Berend Smit, Senja Dominique Barthel, Amber Kashan Mace
We present an efficient method to compute diffusion coefficients of multiparticle systems with strong interactions directly from the geometry and topology of the potential energy field of the migrating particles. The approach is tested on Li-ion diffusion ...
Washington2023Young children can use their subjective straight-ahead to remap visuo-motor alterations
Jenifer Cléa Miehlbradt, Davide Esposito
Young children and adults process spatial information differently: the former use their bodies as primary reference, while adults seem capable of using abstract frames. The transition is estimated to occur between the 6th and the 12th year of age. The mech ...
NATURE PORTFOLIO2023Modeling non-covalent interactions in condensed phase
The modeling of non-covalent interactions, solvation effects, and chemical reactions in complex molecular environment is a challenging task. Current state-of-the-art approaches often rely on static computations using implicit solvent models and harmonic ap ...
EPFL2022