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Molecular dynamics modelling of radiation defects in ferromagnetic alpha-iron

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Thermal transport of glasses via machine learning driven simulations

Federico Grasselli

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 ...

Lausanne2024

Universal machine learning for the response of atomistic systems to external fields

Yaolong Zhang

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 ...

Berlin2023

The OpenMolcas Web: A Community-Driven Approach to Advancing Computational Chemistry

Francesco Aquilante

The developments of the open-source OpenMolcas chemistry software environment since spring 2020 are described, with a focus on novel functionalities accessible in the stable branch of the package or via interfaces with other packages. These developments sp ...

Washington2023

Advancing Computational Chemistry with Stochastic and Artificial Intelligence Approaches

Justin Villard

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 ...

EPFL2023

Transferable machine-learning models of complex materials: the case of GaAs

Giulio Imbalzano

Molecular simulations allow to investigate the behaviour of materials at the atomistic level, shedding light on phenomena that cannot be directly observed in experiments. Accurate results can be obtained with ab initio methods, while simulations of large-s ...

EPFL2021

Machine learning encompasses tools and algorithms that are now becoming popular in almost all scientific and technological fields. This is true for molecular dynamics as well, where machine learning offers promises of extracting valuable information from t ...

AMER CHEMICAL SOC2020

Machine Learning Force Fields and Coarse-Grained Variables in Molecular Dynamics: Application to Materials and Biological Systems

Michele Ceriotti, Fabio Pietrucci

2020

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The k(off) values of ligands unbinding to proteins are key parameters for drug discovery. Their predictions based on molecular simulation may under- or overestimate experiment in a system- and/or technique-dependent way. Here we use an established method-i ...

AMER CHEMICAL SOC2020

Probabilistic and deterministic full field approaches to simulate recrystallization in ODS steels

Roland Logé, Flore Villaret

Mechanical and functional properties of Oxide Dispersion Strengthened (ODS) ferritic/martensitic steels are strongly related to their microstructures. Thus, numerical modeling of microstructure evolution during ODS forming is of prime importance. In this w ...

ELSEVIER2020

The role of atomistic simulations in probing hydrogen effects on plasticity and embrittlement in metals

William Curtin, Ali Tehranchi

Atomistic simulations are a powerful complement to experimental probes for understanding the nanoscale processes associated with the effects of hydrogen (H) on plasticity and fracture that are the underlying causes of hydrogen embrittlement (HE). Current e ...

PERGAMON-ELSEVIER SCIENCE LTD2019