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
Cluster-Based Machine Learning Potentials to Describe Disordered Metal–Organic Frameworks up to the Mesoscale
Sign Up to like & getrecommendations! Published in 2025 at "Chemistry of Materials"
DOI: 10.1021/acs.chemmater.5c00821
Abstract: Metal-organic frameworks (MOFs) are highly interesting and tunable materials. By incorporating spatial defects into their atomic structure, MOFs can be finetuned to exhibit precise chemical functionalities, extending their applicability in various technological fields. Defect engineering… read more here.
Keywords: cluster based; machine learning; organic frameworks; learning potentials ... See more keywords
ColabReaction: Accelerating Transition State Searches with Machine Learning Potentials on Google Colaboratory.
Sign Up to like & getrecommendations! Published in 2025 at "Journal of chemical information and modeling"
DOI: 10.1021/acs.jcim.5c02398
Abstract: We have developed a rapid and automated transition state (TS) search method for chemical reactions by combining the double-ended method, Direct MaxFlux (DMF), with machine learning (ML) potentials. Compared to conventional quantum mechanical (QM) scan-based… read more here.
Keywords: google colaboratory; transition state; machine learning; learning potentials ... 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
DeePMD-kit v3: A Multiple-Backend Framework for Machine Learning Potentials.
Sign Up to like & getrecommendations! Published in 2025 at "Journal of chemical theory and computation"
DOI: 10.1021/acs.jctc.5c00340
Abstract: In recent years, machine learning potentials (MLPs) have become indispensable tools in physics, chemistry, and materials science, driving the development of software packages for molecular dynamics (MD) simulations and related applications. These packages, typically built… read more here.
Keywords: framework; learning potentials; deepmd kit; machine learning ... See more keywords
Modeling Diffusion in Metal-Organic Frameworks Using On-the-fly Probability Enhanced Sampling-Based Machine Learning Potentials.
Sign Up to like & getrecommendations! Published in 2025 at "Journal of chemical theory and computation"
DOI: 10.1021/acs.jctc.5c01191
Abstract: Machine learning potentials (MLPs) can help bridge the length- and time-scale gaps required to study diverse physicochemical phenomena in nanoporous materials with ab initio accuracy. These MLPs are typically trained on quantum chemical data obtained… read more here.
Keywords: using fly; machine learning; fly probability; learning potentials ... See more keywords
Machine Learning Potentials for Heterogeneous Catalysis
Sign Up to like & getrecommendations! Published in 2024 at "ACS Catalysis"
DOI: 10.1021/acscatal.4c06717
Abstract: The sustainable production of many bulk chemicals relies on heterogeneous catalysis. The rational design or improvement of the required catalysts critically depends on insights into the underlying mechanisms at the atomic scale. In recent years,… read more here.
Keywords: catalysis machine; machine learning; learning potentials; heterogeneous catalysis ... 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
Accelerating melting temperature predictions by leveraging LASP machine learning potentials in the SLUSCHI package
Sign Up to like & getrecommendations! Published in 2025 at "Journal of the American Ceramic Society"
DOI: 10.1111/jace.70398
Abstract: The automated computational package SLUSCHI , originally interfaced with the first‐principles package VASP , has demonstrated effectiveness but remains computationally demanding for accurately calculating melting temperatures. This study leverages machine learning potentials via the efficient… read more here.
Keywords: machine learning; melting temperatures; learning potentials; package ... 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