Machine-Learning Surrogate Model for Accelerating the Search of Stable Ternary Alloys

Abstract

The prediction of phase diagrams in the search for new phases is a complex and computationally intensive task. Density functional theory provides, in many situations, the desired accuracy, but its throughput becomes prohibitively limited as the number of species involved grows, even when used with local and semi-local functionals. Here, we explore the possibility of integrating machine-learning models in the workflow for the construction of ternary convex hull diagrams. In particular, we train a set of spectral neighbour-analysis potentials (SNAPs) over readily available binary phases and we establish whether this is good enough to predict the energies of novel ternaries. Such a strategy does not require any new calculations specific for the construction of the model, but just avails of data stored in binary-phase-diagram repositories. We find that a so-constructed SNAP is capable of accurate total-energy estimates for ternary phases close to the equilibrium geometry but, in general, is not able to perform atomic relaxation. This is because during a typical relaxation path a given phase traverses regions in the parameter space poorly represented by the training set. Different metrics are then investigated to assess how an unknown structure is well described by a given SNAP model, and we find that the standard deviation of an ensemble of SNAPs provides a fast and non-specie-specific metric.

References in corpus (26)

• Quantum ESPRESSO: a modular and open-source software project for quantum simulations of materials
• Gaussian Approximation Potentials: the accuracy of quantum mechanics, without the electrons
• On representing chemical environments
• Moment Tensor Potentials: a class of systematically improvable interatomic potentials
• Ab initio Random Structure Searching
• A Spectral Analysis Method for Automated Generation of Quantum-Accurate Interatomic Potentials
• A Performance and Cost Assessment of Machine Learning Interatomic Potentials
• By-passing the Kohn-Sham equations with machine learning
• Active learning of linearly parametrized interatomic potentials
• Machine-learning based interatomic potential for amorphous carbon
• Machine learning a general purpose interatomic potential for silicon
• Developing an improved Crystal Graph Convolutional Neural Network framework for accelerated materials discovery
• An Accurate and Transferable Machine Learning Potential for Carbon
• A Transferable Machine-Learning Model of the Electron Density
• Extending the Accuracy of the SNAP Interatomic Potential Form
• A RESTful API for exchanging Materials Data in the AFLOWLIB.org consortium
• Accurate Force Field for Molybdenum by Machine Learning Large Materials Data
• Constructing first-principles phase diagrams of amorphous LixSi using machine-learning-assisted sampling with an evolutionary algorithm
• Quantum-Accurate Spectral Neighbor Analysis Potential Models for Ni-Mo Binary Alloys and FCC Metals
• Automated discovery of a robust interatomic potential for aluminum
• Stratified construction of neural network based interatomic models for multicomponent materials
• Global optimization of atomistic structure enhanced by machine learning
• Machine-learning accelerated geometry optimization in molecular simulation
• The AFLOW Library of Crystallographic Prototypes: Part 3
• Phase stability of Au-Li binary systems studied using neural network potential
• Linear Jacobi-Legendre expansion of the charge density for machine learning-accelerated electronic structure calculations

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