# Deep machine learning potentials for multicomponent metallic melts: development, predictability and compositional transferability

@article{Ryltsev2021DeepML,
title={Deep machine learning potentials for multicomponent metallic melts: development, predictability and compositional transferability},
author={Roman Ryltsev and N M Chtchelkatchev},
journal={Journal of Molecular Liquids},
year={2021}
}
• Published 26 October 2021
• Physics
• Journal of Molecular Liquids
The use of machine learning interatomic potentials (MLIPs) in simulations of materials is a state-of-the-art approach, which allows achieving nearly \textit{ab initio} accuracy with orders of magnitude less computational cost. Multicomponent disordered systems have a highly complicated potential energy surface due to both topological and compositional disorder. That arises issues in MLIPs developing, such as optimal design strategy of potentials and their predictability and transferability…

## References

SHOWING 1-10 OF 78 REFERENCES
Deep machine learning interatomic potential for liquid silica.
• Medicine, Physics
Physical review. E
• 2020
Developed NNP allows us to describe the structure of the glassy silica with satisfactory accuracy even though no low-temperature configurations were included in the training procedure, and opens up prospects for simulating structural and dynamical properties of liquids and glasses via NNP.
A general-purpose machine-learning force field for bulk and nanostructured phosphorus
• Medicine
Nature communications
• 2020
It is shown that a universally applicable force field for phosphorus can be created by machine learning from a suitably chosen ensemble of quantum-mechanical results, fitted to density-functional theory plus many-body dispersion data.
SchNet - A deep learning architecture for molecules and materials.
• Computer Science, Medicine
The Journal of chemical physics
• 2018
The deep learning architecture SchNet is presented that is specifically designed to model atomistic systems by making use of continuous-filter convolutional layers and employs SchNet to predict potential-energy surfaces and energy-conserving force fields for molecular dynamics simulations of small molecules.
Phase Transitions of Hybrid Perovskites Simulated by Machine-Learning Force Fields Trained on the Fly with Bayesian Inference.
• Physics, Medicine
Physical review letters
• 2019
An on-the-fly machine learning scheme that generates force fields automatically during molecular dynamics simulations, which opens up the required time and length scales, while retaining the distinctive chemical precision of first principles methods and minimizing the need for human intervention.
Machine Learning Interatomic Potentials as Emerging Tools for Materials Science.
• Medicine, Materials Science
• 2019
By "learning" electronic-structure data, ML-based interatomic potentials give access to atomistic simulations that reach similar accuracy levels but are orders of magnitude faster.
Optimization and validation of a deep learning CuZr atomistic potential: Robust applications for crystalline and amorphous phases with near-DFT accuracy.
• Physics, Materials Science
The Journal of chemical physics
• 2020
This work shows that a deep-learning neural network potential based on density functional theory calculations can well describe Cu-Zr materials, an example of a binary alloy system, that can coexist in as ordered intermetallic and as an amorphous phase, thus enabling accurate computations of realistic atomistic models.
Development of interatomic potential for Al-Tb alloys using a deep neural network learning method.
• Materials Science, Medicine
Physical chemistry chemical physics : PCCP
• 2020
It is shown that the obtained DNN model can well reproduce the energies and forces calculated by AIMD simulations and predicts the formation energies of the crystalline phases of the Al-Tb system with an accuracy comparable to ab initio calculations.
End-to-end Symmetry Preserving Inter-atomic Potential Energy Model for Finite and Extended Systems
• Mathematics, Computer Science
NeurIPS
• 2018
Deep Potential - Smooth Edition (DeepPot-SE), an end-to-end machine learning-based PES model, which is able to efficiently represent the PES for a wide variety of systems with the accuracy of ab initio quantum mechanics models is developed.
Quantum-chemical insights from deep tensor neural networks
• Medicine, Physics
Nature communications
• 2017
An efficient deep learning approach is developed that enables spatially and chemically resolved insights into quantum-mechanical observables of molecular systems, and unifies concepts from many-body Hamiltonians with purpose-designed deep tensor neural networks, which leads to size-extensive and uniformly accurate chemical space predictions.
86 PFLOPS Deep Potential Molecular Dynamics simulation of 100 million atoms with ab initio accuracy
The GPU version of DeePMD-kit is presented, which, upon training a deep neural network model using abinitio data, can drive extremely large-scale molecular dynamics simulation with ab initio accuracy, achieving 86 PFLOPS in double precision.