Thermal transport and phase transitions of zirconia by on-the-fly machine-learned interatomic potentials
Abstract Machine-learned interatomic potentials enable realistic finite temperature
calculations of complex materials properties with first-principles accuracy. It is not yet clear …
calculations of complex materials properties with first-principles accuracy. It is not yet clear …
Machine-learned interatomic potentials: Recent developments and prospective applications
High-throughput generation of large and consistent ab initio data combined with advanced
machine-learning techniques are enabling the creation of interatomic potentials of near ab …
machine-learning techniques are enabling the creation of interatomic potentials of near ab …
Raman spectra of 2D titanium carbide MXene from machine-learning force field molecular dynamics
MXenes represent one of the largest classes of 2D materials with promising applications in
many fields and their properties are tunable by altering the surface group composition …
many fields and their properties are tunable by altering the surface group composition …
Combining machine learning and many-body calculations: coverage-dependent adsorption of CO on Rh (111)
Adsorption of carbon monoxide (CO) on transition-metal surfaces is a prototypical process in
surface sciences and catalysis. Despite its simplicity, it has posed great challenges to …
surface sciences and catalysis. Despite its simplicity, it has posed great challenges to …
[HTML][HTML] Density isobar of water and melting temperature of ice: Assessing common density functionals
We investigate the density isobar of water and the melting temperature of ice using six
different density functionals. Machine-learning potentials are employed to ensure …
different density functionals. Machine-learning potentials are employed to ensure …
Machine learned force-fields for an Ab-initio quality description of metal-organic frameworks
Metal-organic frameworks (MOFs) are an incredibly diverse group of highly porous hybrid
materials, which are interesting for a wide range of possible applications. For a meaningful …
materials, which are interesting for a wide range of possible applications. For a meaningful …
Machine-learned acceleration for molecular dynamics in CASTEP
Machine learning (ML) methods are of rapidly growing interest for materials modeling, and
yet, the use of ML interatomic potentials for new systems is often more demanding than that …
yet, the use of ML interatomic potentials for new systems is often more demanding than that …
Layer-by-layer phase transformation in Ti3O5 revealed by machine-learning molecular dynamics simulations
Reconstructive phase transitions involving breaking and reconstruction of primary chemical
bonds are ubiquitous and important for many technological applications. In contrast to …
bonds are ubiquitous and important for many technological applications. In contrast to …
[HTML][HTML] Comparing machine learning potentials for water: Kernel-based regression and Behler–Parrinello neural networks
In this paper, we investigate the performance of different machine learning potentials (MLPs)
in predicting key thermodynamic properties of water using RPBE+ D3. Specifically, we …
in predicting key thermodynamic properties of water using RPBE+ D3. Specifically, we …
Phase transitions of zirconia: Machine-learned force fields beyond density functional theory
Machine-learned force fields (MLFFs) are increasingly used to accelerate first-principles
simulations of many materials properties. However, MLFFs are generally trained from …
simulations of many materials properties. However, MLFFs are generally trained from …