The data was interpreted in the following article: Machine Learning-Assisted High-Throughput Exploration of Interface Energy Space in Multi-Phase-FieldModel with CALPHAD potential Vahid Attari, Raymundo Arroyave Texas A&M University Link to article: https://doi.org/10.1186/s41313-021-00038-0 For more information please refer to Open Phase-field Microstructure Database (OPMD) curated at https://microstructures.net
Understanding materials dynamics under extreme conditions of pressure, temperature, and strain rate ...
Photoelectrochemical (PEC) water splitting cells, used to create hydrogen from solar energy, are cru...
Accurate modelling of chemical and physical interactions is crucial for obtaining thermodynamic and ...
Outline ======= We assembled data from Potential Energy Surface (PES) construction invoking the ...
Atomic-scale modeling and understanding of materials have made remarkable progress, but they are sti...
Supplementary files for article: Machine learning force fields based on local parametrization of dis...
In recent years, we have been witnessing a paradigm shift in computational materials science. In fac...
In recent years, we have been witnessing a paradigm shift in computational materials science. In fac...
Machine learning techniques are being increasingly used as flexible non-linear fitting and predictio...
Funding Information: The authors acknowledge funding from the Academy of Finland, under Projects No....
Funding Information: The ion-beam experiments were performed at the LEIBF beamline of Inter Universi...
Title: Analysis of magnetic skyrmions using machine learning methods Author: Ondřej Dušek Department...
Abstract: Interatomic potential models based on machine learning (ML) are rapidly developing as tool...
When creating training data for machine-learned interatomic potentials (MLIPs), it is common to crea...
In this work we developed a microstructure database for a model alloy, i.e., Iron Chromium Alloy (Fe...
Understanding materials dynamics under extreme conditions of pressure, temperature, and strain rate ...
Photoelectrochemical (PEC) water splitting cells, used to create hydrogen from solar energy, are cru...
Accurate modelling of chemical and physical interactions is crucial for obtaining thermodynamic and ...
Outline ======= We assembled data from Potential Energy Surface (PES) construction invoking the ...
Atomic-scale modeling and understanding of materials have made remarkable progress, but they are sti...
Supplementary files for article: Machine learning force fields based on local parametrization of dis...
In recent years, we have been witnessing a paradigm shift in computational materials science. In fac...
In recent years, we have been witnessing a paradigm shift in computational materials science. In fac...
Machine learning techniques are being increasingly used as flexible non-linear fitting and predictio...
Funding Information: The authors acknowledge funding from the Academy of Finland, under Projects No....
Funding Information: The ion-beam experiments were performed at the LEIBF beamline of Inter Universi...
Title: Analysis of magnetic skyrmions using machine learning methods Author: Ondřej Dušek Department...
Abstract: Interatomic potential models based on machine learning (ML) are rapidly developing as tool...
When creating training data for machine-learned interatomic potentials (MLIPs), it is common to crea...
In this work we developed a microstructure database for a model alloy, i.e., Iron Chromium Alloy (Fe...
Understanding materials dynamics under extreme conditions of pressure, temperature, and strain rate ...
Photoelectrochemical (PEC) water splitting cells, used to create hydrogen from solar energy, are cru...
Accurate modelling of chemical and physical interactions is crucial for obtaining thermodynamic and ...