Machine learning with force-field inspired descriptors for materials: fast screening and mapping energy landscape

Abstract

We present a complete set of chemo-structural descriptors to significantly extend the applicability of machine-learning (ML) in material screening and mapping energy landscape for multicomponent systems. These new descriptors allow differentiating between structural prototypes, which is not possible using the commonly used chemical-only descriptors. Specifically, we demonstrate that the combination of pairwise radial, nearest neighbor, bond-angle, dihedral-angle and core-charge distributions plays an important role in predicting formation energies, bandgaps, static refractive indices, magnetic properties, and modulus of elasticity for three-dimensional (3D) materials as well as exfoliation energies of two-dimensional (2D) layered materials. The training data consists of 24549 bulk and 616 monolayer materials taken from JARVIS-DFT database. We obtained very accurate ML models using gradient boosting algorithm. Then we use the trained models to discover exfoliable 2D-layered materials satisfying specific property requirements. Additionally, we integrate our formation energy ML model with a genetic algorithm for structure search to verify if the ML model reproduces the DFT convex hull. This verification establishes a more stringent evaluation metric for the ML model than what commonly used in data sciences. Our learnt model is publicly available on the JARVIS-ML website (https://www.ctcms.nist.gov/jarvisml ) property predictions of generalized materials.

References in corpus (16)

• Scikit-learn: Machine Learning in Python
• TensorFlow: A system for large-scale machine learning
• Gaussian Approximation Potentials: the accuracy of quantum mechanics, without the electrons
• 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
• AFLOW: An automatic framework for high-throughput materials discovery
• Universal Fragment Descriptors for Predicting Electronic Properties of Inorganic Crystals
• How to represent crystal structures for machine learning: towards fast prediction of electronic properties
• Self-driven lattice-model Monte Carlo simulations of alloy thermodynamic
• Alchemical and structural distribution based representation for improved QML
• Representation of compounds for machine-learning prediction of physical properties
• Understanding molecular representations in machine learning: The role of uniqueness and target similarity
• Computational screening of high-performance optoelectronic materials using OptB88vdW and TB-mBJ formalisms
• Elastic properties of bulk and low-dimensional materials using Van der Waals density functional

Cited by in corpus (31)

• Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals
• Recent Advances and Applications of Deep Learning Methods in Materials Science
• DScribe: Library of Descriptors for Machine Learning in Materials Science
• Atomistic Line Graph Neural Network for Improved Materials Property Predictions
• The Joint Automated Repository for Various Integrated Simulations (JARVIS) for data-driven materials design
• Physics-inspired structural representations for molecules and materials
• Benchmarking Materials Property Prediction Methods: The Matbench Test Set and Automatminer Reference Algorithm
• Convergence and machine learning predictions of Monkhorst-Pack k-points and plane-wave cut-off in high-throughput DFT calculations
• A critical examination of robustness and generalizability of machine learning prediction of materials properties
• High-throughput Density Functional Perturbation Theory and Machine Learning Predictions of Infrared, Piezoelectric and Dielectric Responses
• A Deep Dive into Machine Learning Density Functional Theory for Materials Science and Chemistry
• Graph convolutional neural networks as "general-purpose" property predictors: the universality and limits of applicability
• Uncertainty Prediction for Machine Learning Models of Material Properties
• Designing High-Tc Superconductors with BCS-inspired Screening, Density Functional Theory and Deep-learning
• Data-driven Discovery of 3D and 2D Thermoelectric Materials
• High-throughput search for magnetic and topological order in transition metal oxides
• Computational Search for Magnetic and Non-magnetic 2D Topological Materials using Unified Spin-orbit Spillage Screening
• Toward Accurate Interpretable Predictions of Materials Properties within Transformer Language Models
• High-throughput search for magnetic topological materials using spin-orbit spillage, machine-learning and experiments
• Recent progress in the JARVIS infrastructure for next-generation data-driven materials design
• Minimal set of crystallographic descriptors for sorption properties in hypothetical Metal Organic Frameworks: Role in sequential learning optimization
• DenseGNN: universal and scalable deeper graph neural networks for high-performance property prediction in crystals and molecules
• Artificial Intelligence in Materials Science and Engineering: Current Landscape, Key Challenges, and Future Trajectorie
• Density Functional Theory based Electric Field Gradient Database
• Random projections and Kernelised Leave One Cluster Out Cross-Validation: Universal baselines and evaluation tools for supervised machine learning for materials properties
• Linear Jacobi-Legendre expansion of the charge density for machine learning-accelerated electronic structure calculations
• The JARVIS Infrastructure is All You Need for Materials Design
• Density Functional Theory and Deep-learning to Accelerate Data Analytics in Scanning Tunneling Microscopy
• Energy-GNoME: A Living Database of Selected Materials for Energy Applications
• Interpretation of Crystal Energy Landscapes with Kolmogorov-Arnold Networks
• Predicting Many Crystal Properties via an Adaptive Transformer-based Framework