We introduce interatomic potentials for tungsten in the bcc crystal phase and its defects within the Gaussian approximation potential framework, fitted to a database of first-principles density functional theory calculations. We investigate the performance of a sequence of models based on databases of increasing coverage in configuration space and showcase our strategy of choosing representative small unit cells to train models that predict properties observable only using thousands of atoms. The most comprehensive model is then used to calculate properties of the screw dislocation, including its structure, the Peierls barrier and the energetics of the vacancy-dislocation interaction. All software and raw data are available at www.libatoms....
Screw dislocations in bcc metals display non-planar cores at zero temperature which result in high l...
The analysis of the damage on plasma facing materials (PFM), due to their direct interaction with th...
Funding Information: We would like to thank M.-C. Marinica and J. Alcalá for inspiring conversations...
We introduce interatomic potentials for tungsten in the bcc crystal phase and its defects within the...
We introduce interatomic potentials for tungsten in the bcc crystal phase and its defects within the...
An accurate description of atomic interactions, such as that provided by first principles quantum me...
We present a bond-order potential (BOP) for the bcc transition metal tungsten. The bond-order potent...
Data-driven, or machine learning (ML), approaches have become viable alternatives to semiempirical m...
We present a bond-order potential (BOP) for the bcc transition metal tungsten. The bond-order potent...
We introduce a machine-learning interatomic potential for tungsten using the Gaussian approximation ...
In the present work, we have evaluated the performance of different embedded atom method (EAM) and s...
We show that the Gaussian Approximation Potential (GAP) machine-learning framework can describe comp...
International audiencePrediction of condensed matter properties requires an accurate description of ...
International audienceCalculations of dislocation-defect interactions are essential to model metalli...
The Gaussian approximation potential (GAP) is an accurate machine-learning interatomic potential tha...
Screw dislocations in bcc metals display non-planar cores at zero temperature which result in high l...
The analysis of the damage on plasma facing materials (PFM), due to their direct interaction with th...
Funding Information: We would like to thank M.-C. Marinica and J. Alcalá for inspiring conversations...
We introduce interatomic potentials for tungsten in the bcc crystal phase and its defects within the...
We introduce interatomic potentials for tungsten in the bcc crystal phase and its defects within the...
An accurate description of atomic interactions, such as that provided by first principles quantum me...
We present a bond-order potential (BOP) for the bcc transition metal tungsten. The bond-order potent...
Data-driven, or machine learning (ML), approaches have become viable alternatives to semiempirical m...
We present a bond-order potential (BOP) for the bcc transition metal tungsten. The bond-order potent...
We introduce a machine-learning interatomic potential for tungsten using the Gaussian approximation ...
In the present work, we have evaluated the performance of different embedded atom method (EAM) and s...
We show that the Gaussian Approximation Potential (GAP) machine-learning framework can describe comp...
International audiencePrediction of condensed matter properties requires an accurate description of ...
International audienceCalculations of dislocation-defect interactions are essential to model metalli...
The Gaussian approximation potential (GAP) is an accurate machine-learning interatomic potential tha...
Screw dislocations in bcc metals display non-planar cores at zero temperature which result in high l...
The analysis of the damage on plasma facing materials (PFM), due to their direct interaction with th...
Funding Information: We would like to thank M.-C. Marinica and J. Alcalá for inspiring conversations...