Accelerating crystal structure prediction by machine-learning interatomic potentials with active learning

Abstract

In this letter we propose a new methodology for crystal structure prediction, which is based on the evolutionary algorithm USPEX and the machine-learning interatomic potentials actively learning on-the-fly. Our methodology allows for an automated construction of an interatomic interaction model from scratch replacing the expensive DFT with a speedup of several orders of magnitude. Predicted low-energy structures are then tested on DFT, ensuring that our machine-learning model does not introduce any prediction error. We tested our methodology on a problem of prediction of carbon allotropes, dense sodium structures and boron allotropes including those which have more than 100 atoms in the primitive cell. All the the main allotropes have been reproduced and a new 54-atom structure of boron have been found at very modest computational efforts.

References in corpus (13)

• Gaussian Approximation Potentials: the accuracy of quantum mechanics, without the electrons
• Intrinsic Correlation between Hardness and Elasticity in Polycrystalline Materials and Bulk Metallic Glasses
• On representing chemical environments
• ANI-1: An extensible neural network potential with DFT accuracy at force field computational cost
• Moment Tensor Potentials: a class of systematically improvable interatomic potentials
• A Spectral Analysis Method for Automated Generation of Quantum-Accurate Interatomic Potentials
• Ionic high-pressure form of elemental boron
• Transparent dense sodium
• Active learning of linearly parametrized interatomic potentials
• Data-driven learning of total and local energies in elemental boron
• Adaptive Genetic Algorithm for Crystal Structure Prediction
• Constrained evolutionary algorithm for structure prediction of molecular crystals: methodology and applications
• Symmetry-broken crystal structure of elemental boron at low temperature

Cited by in corpus (71)

• A Performance and Cost Assessment of Machine Learning Interatomic Potentials
• Big-Data Science in Porous Materials: Materials Genomics and Machine Learning
• A Perspective on Conventional High-Temperature Superconductors at High Pressure: Methods and Materials
• Machine learning for electronically excited states of molecules
• Machine-learning interatomic potentials for materials science
• Machine learning force fields and coarse-grained variables in molecular dynamics: application to materials and biological systems
• De novo exploration and self-guided learning of potential-energy surfaces
• Towards high-throughput superconductor discovery via machine learning
• Machine-Learning Interatomic Potentials Enable First-Principles Multiscale Modeling of Lattice Thermal Conductivity in Graphene/Borophene Heterostructures
• Deep Potentials for Materials Science
• Perspective on integrating machine learning into computational chemistry and materials science
• Nanoporous C3N4, C3N5 and C3N6 nanosheets; Novel strong semiconductors with low thermal conductivities and appealing optical/electronic properties
• Exploring Phononic Properties of Two-Dimensional Materials using Machine Learning Interatomic Potentials
• PyXtal: a Python Library for Crystal Structure Generation and Symmetry Analysis
• Deep Potential generation scheme and simulation protocol for the Li10GeP2S12-type superionic conductors
• A Deep Dive into Machine Learning Density Functional Theory for Materials Science and Chemistry
• Atomic-scale origin of the low grain-boundary resistance in perovskite solid electrolytes
• Multi-scale Investigation of Chemical Short-Range Order and Dislocation Glide in the MoNbTi and TaNbTi Refractory Multi-Principal Element Alloys
• Tuning Chemical Precompression: Theoretical Design and Crystal Chemistry of Novel Hydrides in the Quest for Warm and Light Superconductivity at Ambient Pressures
• Ephemeral data derived potentials for random structure search
• Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules
• Modular machine learning-based elastoplasticity: generalization in the context of limited data
• High thermal conductivity in semiconducting Janus and non-Janus diamanes
• Crystal structure prediction with machine learning-based element substitution
• Application of machine learning potentials to predict grain boundary properties in fcc elemental metals
• MAISE: Construction of neural network interatomic models and evolutionary structure optimization
• Group-theoretical high-order rotational invariants for structural representations: Application to linearized machine learning interatomic potential
• Combining Machine Learning and Many-Body Calculations: Coverage-Dependent Adsorption of CO on Rh(111)
• Systematic Atomic Structure Datasets for Machine Learning Potentials: Application to Defects in Magnesium
• Enhancing Crystal Structure Prediction by decomposition methods based on graph theory
• Toward Accurate Interpretable Predictions of Materials Properties within Transformer Language Models
• Developments and Further Applications of Ephemeral Data Derived Potentials
• Training machine-learning potentials for crystal structure prediction using disordered structures
• Machine learning potentials for multicomponent systems: The Ti-Al binary system
• Accelerated identification of equilibrium structures of multicomponent inorganic crystals using machine learning potentials
• Thermo-mechanical properties of nitrogenated holey graphene (C2N): A comparison of machine-learning-based and classical interatomic potentials
• PyXtal FF: a Python Library for Automated Force Field Generation
• A first-principles machine-learning force field for heterogeneous ice nucleation on microcline feldspar
• Materials under high pressure: A chemical perspective
• Progress, challenges and perspectives of computational studies on glassy superionic conductors for solid-state batteries
• ænet-PyTorch: a GPU-supported implementation for machine learning atomic potentials training
• Indirect Learning of Interatomic Potentials for Accelerated Materials Simulations
• Linear Jacobi-Legendre expansion of the charge density for machine learning-accelerated electronic structure calculations
• In operando active learning of interatomic interaction during large-scale simulations
• Prediction of stable Li-Sn compounds: boosting ab initio searches with neural network potentials
• Crystal structure prediction of materials with high symmetry using differential evolution
• Machine Learned Interatomic Potentials for Ternary Carbides trained on the AFLOW Database
• Ring Polymer Molecular Dynamics and Active Learning of Moment Tensor Potential for Gas-Phase Barrierless Reactions: Application to S + H2
• InvDesFlow-AL: active learning-based workflow for inverse design of functional materials
• Neural Networks Potential from the Bispectrum Component: A Case Study on Crystalline Silicon
• Mean-Field Density Matrix Decompositions
• Neural evolution structure generation: High Entropy Alloys
• Generating candidates in global optimization algorithms using complementary energy landscapes
• Facilitating {\it ab initio} configurational sampling of multicomponent solids using an on-lattice neural network model and active learning
• Structural Motifs and Bonding in Two Families of Boron Structures Predicted at Megabar Pressures
• Machine learning search for stable binary Sn alloys with Na, Ca, Cu, Pd, and Ag
• Machine learning at the atomic-scale
• AlphaCrystal: Contact map based crystal structure prediction using deep learning
• Efficient Generation of Stable Linear Machine-Learning Force Fields with Uncertainty-Aware Active Learning
• Distance Matrix based Crystal Structure Prediction using Evolutionary Algorithms
• Active-learning-based efficient prediction of ab-initio atomic energy: a case study on a Fe random grain boundary model with millions of atoms
• Accessing negative Poisson`s ratio of graphene by machine learning interatomic potentials
• Navigating the Complex Compositional Landscape of High-Entropy Alloys
• MDEA: Malware Detection with Evolutionary Adversarial Learning
• Contact Map based Crystal Structure Prediction using Global Optimization
• Atomistic Global Optimization X: A Python package for optimization of atomistic structures
• Score-based diffusion models for accurate crystal-structure inpainting and reconstruction of hydrogen positions
• Iterative learning scheme for crystal structure prediction with anharmonic lattice dynamics
• Systematic global structure search of bismuth-based binary systems under pressure using machine learning potentials
• Tuning Thermal Conductivity and Electron-Phonon Interactions in Carbon and Boron Nitride Moiré Diamanes via Twist Angle Manipulation
• Evaluating approaches for on-the-fly machine learning interatomic potential for activated mechanisms sampling with the activation-relaxation technique nouveau