Evaluating Machine Learning Interatomic Potentials for Accurate and Scalable Modeling of Organometallic Precursors.
Sign Up to like & getrecommendations! Published in 2025 at "ACS applied materials & interfaces"
DOI: 10.1021/acsami.5c09107
Abstract: Accurate modeling of organometallic precursors is essential for developing atomic layer deposition (ALD) processes that are required for fabricating high-performance thin films. The melting point of these precursors is often challenging to measure experimentally due… read more here.
Keywords: learning interatomic; interatomic potentials; machine learning; modeling organometallic ... See more keywords
Interpolation and differentiation of alchemical degrees of freedom in machine learning interatomic potentials
Sign Up to like & getrecommendations! Published in 2024 at "Nature Communications"
DOI: 10.1038/s41467-025-59543-2
Abstract: Machine learning interatomic potentials (MLIPs) have become a workhorse of modern atomistic simulations, and recently published universal MLIPs, pre-trained on large datasets, have demonstrated remarkable accuracy and generalizability. However, the computational cost of MLIPs limits… read more here.
Keywords: learning interatomic; interatomic potentials; alchemical degrees; degrees freedom ... See more keywords
Atomistic modeling of the mechanical properties: the rise of machine learning interatomic potentials.
Sign Up to like & getrecommendations! Published in 2023 at "Materials horizons"
DOI: 10.1039/d3mh00125c
Abstract: Since the birth of the concept of machine learning interatomic potentials (MLIPs) in 2007, a growing interest has been developed in the replacement of empirical interatomic potentials (EIPs) with MLIPs, in order to conduct more… read more here.
Keywords: interatomic potentials; machine learning; modeling mechanical; mechanical properties ... See more keywords
Applicability of universal machine learning interatomic potentials to the simulation of steels
Sign Up to like & getrecommendations! Published in 2025 at "Modelling and Simulation in Materials Science and Engineering"
DOI: 10.1088/1361-651x/adb483
Abstract: Bearing steels are complex materials composed of an iron matrix and a well defined and precise amount of several alloying elements. In order to improve sustainability and circularity, there is a tendency to increase the… read more here.
Keywords: universal machine; learning interatomic; interatomic potentials; machine learning ... See more keywords
Machine-learning interatomic potentials from a users perspective: a comparison of accuracy, speed and data efficiency
Sign Up to like & getrecommendations! Published in 2025 at "Modelling and Simulation in Materials Science and Engineering"
DOI: 10.1088/1361-651x/adf56d
Abstract: Machine learning interatomic potentials (MLIPs) have massively changed the field of atomistic modeling. They enable the accuracy of density functional theory in large-scale simulations while being nearly as fast as classical interatomic potentials (IPs). Over… read more here.
Keywords: learning interatomic; interatomic potentials; potentials users; machine learning ... See more keywords
Accessing negative Poisson’s ratio of graphene by machine learning interatomic potentials
Sign Up to like & getrecommendations! Published in 2022 at "Nanotechnology"
DOI: 10.1088/1361-6528/ac5cfd
Abstract: The negative Poisson’s ratio (NPR) is a novel property of materials, which enhances the mechanical feature and creates a wide range of application prospects in lots of fields, such as aerospace, electronics, medicine, etc. Fundamental… read more here.
Keywords: graphene; negative poisson; machine learning; poisson ratio ... See more keywords
Thermal, mechanical, and electrical properties of Si-stacked nanosheet transistors using machine learning interatomic potentials
Sign Up to like & getrecommendations! Published in 2024 at "Nanotechnology"
DOI: 10.1088/1361-6528/ad8357
Abstract: Thermal and mechanical properties play a key role in optimizing the performance of nanoelectronic devices. In this study, the lattice thermal conductivity (κL) and elastic constants of Si nanosheets at different sheet thicknesses were determined… read more here.
Keywords: thermal mechanical; learning interatomic; interatomic potentials; machine learning ... See more keywords
Molecular dynamics study on magnesium hydride nanoclusters with machine-learning interatomic potential
Sign Up to like & getrecommendations! Published in 2020 at "Physical Review B"
DOI: 10.1103/physrevb.102.094111
Abstract: We introduce a machine-learning (ML) interatomic potential for Mg-H system based on Behler-Parrinello approach. In order to fit the complex bonding conditions in the cluster structure, we combine multiple sampling strategies to obtain training samples… read more here.
Keywords: interatomic potential; mathrm; molecular dynamics; machine learning ... See more keywords
Deep-learning interatomic potentials of the ɛ-ZrX_{2} series (X=H, D, and T).
Sign Up to like & getrecommendations! Published in 2025 at "Physical review. E"
DOI: 10.1103/physreve.111.055303
Abstract: The ɛ-ZrH_{2} species act as an important component in zirconium-based composite hydrides, which have various applications in nuclear energy, hydrogen storage, and catalysis. In this work, deep-learning interatomic potentials for ɛ-ZrH_{2} have been developed by… read more here.
Keywords: deep learning; zrx series; learning interatomic; interatomic potentials ... See more keywords
Integrating machine learning interatomic potentials with hybrid reverse Monte Carlo structure refinements in RMCProfile
Sign Up to like & getrecommendations! Published in 2024 at "Journal of Applied Crystallography"
DOI: 10.1107/s1600576724009282
Abstract: New software capabilities in RMCProfile allow researchers to study the structure of materials by combining machine learning interatomic potentials and reverse Monte Carlo. read more here.
Keywords: learning interatomic; interatomic potentials; structure; machine learning ... See more keywords
Applications of machine‐learning interatomic potentials for modeling ceramics, glass, and electrolytes: A review
Sign Up to like & getrecommendations! Published in 2024 at "Journal of the American Ceramic Society"
DOI: 10.1111/jace.19934
Abstract: The emergence of artificial intelligence has provided efficient methodologies to pursue innovative findings in material science. Over the past two decades, machine‐learning potential (MLP) has emerged as an alternative technology to density functional theory (DFT)… read more here.
Keywords: learning interatomic; interatomic potentials; machine learning; applications machine ... See more keywords