Machine Learning for compositional disorder: A Comparison Between Different Descriptors and Machine Learning Frameworks

Abstract

Compositional disorder is common in crystal compounds. In these compounds, some atoms are randomly distributed at some crystallographic sites. For such compounds, randomness forms many non-identical independent structures. Thus, calculating the energy of all structures using ordinary quantum ab initio methods can be significantly time-consuming. Machine learning can be a reliable alternative to ab initio methods. We calculate the energy of these compounds with an accuracy close to that of density functional theory calculations in a considerably shorter time using machine learning. In this study, we use kernel ridge regression and neural network to predict energy. In the KRR, we employ sine matrix, Ewald sum matrix, SOAP, ACSF, and MBTR. To implement the neural network, we use two important classes of application of the neural network in material science, including high-dimensional neural network and convolutional neural network based on crystal graph representation. We show that kernel ridge regression using MBTR and neural network using ACSF can provide better accuracy than other methods.

arXiv comment15pages,3figures,1table

References in corpus (18)

• Quantum ESPRESSO: a modular and open-source software project for quantum simulations of materials
• Restoring the density-gradient expansion for exchange in solids and surfaces
• Generalized gradient approximation for solids and their surfaces
• Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties
• On representing chemical environments
• Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
• A General-Purpose Machine Learning Framework for Predicting Properties of Inorganic Materials
• Kernel methods in machine learning
• DScribe: Library of Descriptors for Machine Learning in Materials Science
• Machine learning prediction errors better than DFT accuracy
• Predicting materials properties without crystal structure: Deep representation learning from stoichiometry
• Representation of compounds for machine-learning prediction of physical properties
• Automatic Selection of Atomic Fingerprints and Reference Configurations for Machine-Learning Potentials
• Unified Representation of Molecules and Crystals for Machine Learning
• Atom-Density Representations for Machine Learning
• Optimizing many-body atomic descriptors for enhanced computational performance of machine learning based interatomic potentials
• Representations of molecules and materials for interpolation of quantum-mechanical simulations via machine learning
• Magnetic and structural properties of Ni-substituted magnetoelectric Co${}_4$Nb${}_2$O${}_9$

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