Machine learning potentials for tobermorite minerals
Sign Up to like & getrecommendations! Published in 2021 at "Computational Materials Science"
DOI: 10.1016/j.commatsci.2020.110173
Abstract: Abstract Molecular dynamics (MD) simulation is an important tool to understand the physical and chemical properties of cement hydrates at the atomic level. MD with the machine learning potential (MLP) is considered a promising approach… read more here.
Keywords: machine learning; potentials tobermorite; tobermorite minerals; mlp ... See more keywords
Routine Molecular Dynamics Simulations Including Nuclear Quantum Effects: From Force Fields to Machine Learning Potentials.
Sign Up to like & getrecommendations! Published in 2022 at "Journal of chemical theory and computation"
DOI: 10.1021/acs.jctc.2c01233
Abstract: We report the implementation of a multi-CPU and multi-GPU massively parallel platform dedicated to the explicit inclusion of nuclear quantum effects (NQEs) in the Tinker-HP molecular dynamics (MD) package. The platform, denoted Quantum-HP, exploits two… read more here.
Keywords: quantum; molecular dynamics; machine learning; learning potentials ... See more keywords
Physically informed artificial neural networks for atomistic modeling of materials
Sign Up to like & getrecommendations! Published in 2019 at "Nature Communications"
DOI: 10.1038/s41467-019-10343-5
Abstract: Large-scale atomistic computer simulations of materials heavily rely on interatomic potentials predicting the energy and Newtonian forces on atoms. Traditional interatomic potentials are based on physical intuition but contain few adjustable parameters and are usually… read more here.
Keywords: machine learning; physically informed; informed artificial; physics ... See more keywords
Application of machine learning potentials to predict grain boundary properties in fcc elemental metals
Sign Up to like & getrecommendations! Published in 2020 at "Physical Review Materials"
DOI: 10.1103/physrevmaterials.4.123607
Abstract: Accurate interatomic potentials are in high demand for large-scale atomistic simulations of materials that are prohibitively expensive by density functional theory (DFT) calculation. In this study, we apply machine learning potentials in a recently constructed… read more here.
Keywords: grain boundary; machine learning; elemental metals; grain ... See more keywords
Transferring COVID-19 Challenges into Learning Potentials: Online Workshops in Architectural Education
Sign Up to like & getrecommendations! Published in 2020 at "Sustainability"
DOI: 10.3390/su12177024
Abstract: The paper addresses the shift in architectural education regarding the need to develop new approaches in teaching methodology, improve curricula, and make advancements in new learning arenas and digital environments. The research is based on… read more here.
Keywords: covid challenges; online workshops; education; architectural education ... See more keywords