# Machine-learned interatomic potentials for alloys and alloy phase diagrams

@article{Rosenbrock2021MachinelearnedIP, title={Machine-learned interatomic potentials for alloys and alloy phase diagrams}, author={Conrad W. Rosenbrock and Konstantin Gubaev and Alexander V. Shapeev and L{\'i}via B. P{\'a}rtay and Noam Bernstein and G{\'a}bor Cs{\'a}nyi and Gus L. W. Hart}, journal={npj Computational Materials}, year={2021}, volume={7}, pages={1-9} }

We introduce machine-learned potentials for Ag-Pd to describe the energy of alloy configurations over a wide range of compositions. We compare two different approaches. Moment tensor potentials (MTPs) are polynomial-like functions of interatomic distances and angles. The Gaussian approximation potential (GAP) framework uses kernel regression, and we use the smooth overlap of atomic position (SOAP) representation of atomic neighborhoods that consist of a complete set of rotational and…

## 34 Citations

Spinel nitride solid solutions: charting properties in the configurational space with explainable machine learning

- Computer Science
- 2022

An approach to accelerate ab initio predictions via a combination of density functional theory and machine learning is presented, using the cubic spinel nitride GeSn 2 N 4 as a case study, exploring how formation energy and electronic bandgap are affected by configurational variations.

Machine-Learning Interatomic Potentials for Materials Science

- PhysicsSSRN Electronic Journal
- 2021

Machine learned interatomic potentials using random features

- Computer Sciencenpj Computational Materials
- 2022

The proposed model approximates the energy/forces using a linear combination of random features, thereby enabling fast parameter estimation by solving a linear least-squares problem and addressing scalability issues encountered in this class of machine learning problems.

Bandgap Engineering in the Configurational Space of Solid Solutions via Machine Learning: (Mg,Zn)O Case Study

- Materials Science, Computer ScienceThe journal of physical chemistry letters
- 2021

It is shown here that a simple descriptor based on the Coulomb matrix eigenspectrum clearly outperforms the cluster expansion for both total energy and bandgap energy predictions in the configurational space of a MgO–ZnO solid solution, a prototypical oxide alloy for bandgap engineering.

Taking materials dynamics to new extremes using machine learning interatomic potentials

- Materials ScienceJournal of Materials Informatics
- 2021

Understanding materials dynamics under extreme conditions of pressure, temperature, and strain rate is a scientific quest that spans nearly a century. Atomic simulations have had a considerable…

Discovering New Superalloys Using Machine-Learned Interatomic Potentials

- Materials Science
- 2021

It is more important today than ever before to accelerate the discovery of new revolutionary materials. Advances in computing, transportation, spaceflight, and all other technology sectors are…

Accurate large-scale simulations of siliceous zeolites by neural network potentials

- Chemistry
- 2021

The computational discovery and design of zeolites is a crucial part of the chemical industry. Finding highly accurate while computationally feasible protocol for identification of hypothetical…

Efficient implementation of atom-density representations.

- Computer ScienceThe Journal of chemical physics
- 2021

An implementation of librascal, whose modular design lends itself both to developing refinements to the density-based formalism and to rapid prototyping for new developments of rotationally equivariant atomistic representations, is presented.

Structure and Dynamics of Energy Materials from Machine Learning Simulations: A Topical Review

- PhysicsChinese Journal of Chemistry
- 2021

Energy materials featuring the capability to store and release chemical energy reversibly involve generally complex geometrical structures with multiple elements. It has been a great challenge to…

Explicit Multi-element Extension of the Spectral Neighbor Analysis Potential for Chemically Complex Systems.

- Materials ScienceThe journal of physical chemistry. A
- 2020

This work produces an interatomic potential for indium phosphide capable of capturing high-energy defects that result from radiation damage cascades and reproduces the relaxed defect formation energies with substantially greater accuracy than weighted-density SNAP.

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