Chemical reaction networks and opportunities for machine learning
Chemical reaction networks (CRNs), defined by sets of species and possible reactions
between them, are widely used to interrogate chemical systems. To capture increasingly …
between them, are widely used to interrogate chemical systems. To capture increasingly …
Open-source machine learning in computational chemistry
A Hagg, KN Kirschner - Journal of Chemical Information and …, 2023 - ACS Publications
The field of computational chemistry has seen a significant increase in the integration of
machine learning concepts and algorithms. In this Perspective, we surveyed 179 open …
machine learning concepts and algorithms. In this Perspective, we surveyed 179 open …
GPUMD: A package for constructing accurate machine-learned potentials and performing highly efficient atomistic simulations
We present our latest advancements of machine-learned potentials (MLPs) based on the
neuroevolution potential (NEP) framework introduced in Fan et al.[Phys. Rev. B 104, 104309 …
neuroevolution potential (NEP) framework introduced in Fan et al.[Phys. Rev. B 104, 104309 …
Accelerating the prediction of inorganic surfaces with machine learning interatomic potentials
K Noordhoek, C Bartel - Nanoscale, 2024 - pubs.rsc.org
The surface properties of solid-state materials often dictate their functionality, especially for
applications where nanoscale effects become important. The relevant surface (s) and their …
applications where nanoscale effects become important. The relevant surface (s) and their …
From electrons to phase diagrams with machine learning potentials using pyiron based automated workflows
S Menon, Y Lysogorskiy, ALM Knoll… - npj Computational …, 2024 - nature.com
We present a comprehensive and user-friendly framework built upon the pyiron integrated
development environment (IDE), enabling researchers to perform the entire Machine …
development environment (IDE), enabling researchers to perform the entire Machine …
[HTML][HTML] Molecular dynamics simulations of heat transport using machine-learned potentials: A mini-review and tutorial on GPUMD with neuroevolution potentials
Molecular dynamics (MD) simulations play an important role in understanding and
engineering heat transport properties of complex materials. An essential requirement for …
engineering heat transport properties of complex materials. An essential requirement for …
Engineering inorganic interfaces using molecular nanolayers
G Ramanath, C Rowe, G Sharma… - Applied Physics …, 2023 - pubs.aip.org
Advances in interface science over the last 20 years have demonstrated the use of
molecular nanolayers (MNLs) at inorganic interfaces to access emergent phenomena and …
molecular nanolayers (MNLs) at inorganic interfaces to access emergent phenomena and …
Enhancing the Quality and Reliability of Machine Learning Interatomic Potentials through Better Reporting Practices
Recent developments in machine learning interatomic potentials (MLIPs) have empowered
even nonexperts in machine learning to train MLIPs for accelerating materials simulations …
even nonexperts in machine learning to train MLIPs for accelerating materials simulations …
Injecting domain knowledge from empirical interatomic potentials to neural networks for predicting material properties
For decades, atomistic modeling has played a crucial role in predicting the behavior of
materials in numerous fields ranging from nanotechnology to drug discovery. The most …
materials in numerous fields ranging from nanotechnology to drug discovery. The most …
OpenQDC: Open Quantum Data Commons
Machine Learning Interatomic Potentials (MLIPs) are a highly promising alternative to force-
fields for molecular dynamics (MD) simulations, offering precise and rapid energy and force …
fields for molecular dynamics (MD) simulations, offering precise and rapid energy and force …