MLatom 3: A Platform for Machine Learning-Enhanced Computational Chemistry Simulations and Workflows
PO Dral, F Ge, YF Hou, P Zheng, Y Chen… - Journal of Chemical …, 2024 - ACS Publications
Machine learning (ML) is increasingly becoming a common tool in computational chemistry.
At the same time, the rapid development of ML methods requires a flexible software …
At the same time, the rapid development of ML methods requires a flexible software …
Transferable machine learning interatomic potential for bond dissociation energy prediction of drug-like molecules
We present a transferable MACE interatomic potential that is applicable to open-and closed-
shell drug-like molecules containing hydrogen, carbon, and oxygen atoms. Including an …
shell drug-like molecules containing hydrogen, carbon, and oxygen atoms. Including an …
From electrons to phase diagrams with machine learning potentials using pyiron based automated workflows
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 …
Guest editorial: Special Topic on software for atomistic machine learning
Welcome to the Journal of Chemical Physics' Special Topic on Software for Atomistic
Machine Learning. For some years now, search engines have been dominating our online …
Machine Learning. For some years now, search engines have been dominating our online …
ML-Accelerated Automatic Process Exploration Reveals Facile O-Induced Pd Step-Edge Restructuring on Catalytic Time Scales
We combine automatic process exploration with an iteratively trained machine-learning
interatomic potential to systematically identify elementary processes occurring during the …
interatomic potential to systematically identify elementary processes occurring during the …
Collaboration on Machine-Learned Potentials with IPSuite: A Modular Framework for Learning-on-the-Fly
The field of machine learning potentials has experienced a rapid surge in progress, thanks
to advances in machine learning theory, algorithms, and hardware capabilities. While the …
to advances in machine learning theory, algorithms, and hardware capabilities. While the …
CatFlow: An Automated Workflow for Training Machine Learning Potentials to Compute Free Energies in Dynamic Catalysis
Dynamic effects of catalysts play a crucial role in catalytic reactions, necessitating the
incorporation of statistical sampling and understanding of the impact of dynamic structures in …
incorporation of statistical sampling and understanding of the impact of dynamic structures in …
Systematic and unbiased pathway exploration by artificial force application to a generic neural network potential
T Ichino, H Nabata, K Matsumoto, T Hasegawa… - 2024 - chemrxiv.org
Computational pathway exploration can unravel complex catalytic mechanisms and even
predict unexplored catalytic reactions when performed in a fully systematic and unbiased …
predict unexplored catalytic reactions when performed in a fully systematic and unbiased …