Recent Advances and Applications of Deep Learning Methods in Materials Science

Abstract

Deep learning (DL) is one of the fastest growing topics in materials data science, with rapidly emerging applications spanning atomistic, image-based, spectral, and textual data modalities. DL allows analysis of unstructured data and automated identification of features. Recent development of large materials databases has fueled the application of DL methods in atomistic prediction in particular. In contrast, advances in image and spectral data have largely leveraged synthetic data enabled by high quality forward models as well as by generative unsupervised DL methods. In this article, we present a high-level overview of deep-learning methods followed by a detailed discussion of recent developments of deep learning in atomistic simulation, materials imaging, spectral analysis, and natural language processing. For each modality we discuss applications involving both theoretical and experimental data, typical modeling approaches with their strengths and limitations, and relevant publicly available software and datasets. We conclude the review with a discussion of recent cross-cutting work related to uncertainty quantification in this field and a brief perspective on limitations, challenges, and potential growth areas for DL methods in materials science. The application of DL methods in materials science presents an exciting avenue for future materials discovery and design.

References in corpus (17)

• PyTorch: An Imperative Style, High-Performance Deep Learning Library
• Semi-Supervised Classification with Graph Convolutional Networks
• ANI-1: An extensible neural network potential with DFT accuracy at force field computational cost
• A General-Purpose Machine Learning Framework for Predicting Properties of Inorganic Materials
• Opportunities and Challenges for Machine Learning in Materials Science
• DGL-LifeSci: An Open-Source Toolkit for Deep Learning on Graphs in Life Science
• Machine Learning Guided Discovery of Gigantic Magnetocaloric Effect in HoB${}_2$ Near Hydrogen Liquefaction Temperature
• Similarity of Precursors in Solid-state Synthesis as Text-Mined from Scientific Literature
• Ultralow lattice thermal conductivity and electronic properties of monolayer 1T phase semimetal SiTe2 and SnTe2
• High-throughput search for magnetic topological materials using spin-orbit spillage, machine-learning and experiments
• IRNet: A General Purpose Deep Residual Regression Framework for Materials Discovery
• Manifestation of the thermoelectric properties in Ge-based halide perovskites
• Qualitative Analysis of Monte Carlo Dropout
• A General Framework Combining Generative Adversarial Networks and Mixture Density Networks for Inverse Modeling in Microstructural Materials Design
• DeepFreak: Learning Crystallography Diffraction Patterns with Automated Machine Learning
• Inverse design of crystal structures for multicomponent systems
• Graph convolutional network for predicting abnormal grain growth in Monte Carlo simulations of microstructural evolution

Cited by in corpus (56)

• Explainable Artificial Intelligence: A Survey of Needs, Techniques, Applications, and Future Direction
• 14 Examples of How LLMs Can Transform Materials Science and Chemistry: A Reflection on a Large Language Model Hackathon
• Recent Advances and Applications of Machine Learning in Experimental Solid Mechanics: A Review
• A critical examination of robustness and generalizability of machine learning prediction of materials properties
• AdsorbML: A Leap in Efficiency for Adsorption Energy Calculations using Generalizable Machine Learning Potentials
• On the redundancy in large material datasets: efficient and robust learning with less data
• Advances of Machine Learning in Materials Science: Ideas and Techniques
• Graph Neural Network Predictions of Metal Organic Framework CO2 Adsorption Properties
• Thermodynamics and its Prediction and CALPHAD Modeling: Review, State of the Art, and Perspectives
• Designing High-Tc Superconductors with BCS-inspired Screening, Density Functional Theory and Deep-learning
• Uncertainty Quantification in Multivariable Regression for Material Property Prediction with Bayesian Neural Networks
• JARVIS-Leaderboard: A Large Scale Benchmark of Materials Design Methods
• ChemNLP: A Natural Language Processing based Library for Materials Chemistry Text Data
• A Prompt-Engineered Large Language Model, Deep Learning Workflow for Materials Classification
• Artificial intelligence approaches for materials-by-design of energetic materials: state-of-the-art, challenges, and future directions
• A Deep-learning Model for Fast Prediction of Vacancy Formation in Diverse Materials
• Artificial Intelligence in Materials Science and Engineering: Current Landscape, Key Challenges, and Future Trajectorie
• AlabOS: A Python-based Reconfigurable Workflow Management Framework for Autonomous Laboratories
• Convolution, aggregation and attention based deep neural networks for accelerating simulations in mechanics
• Machine-learning approach for discovery of conventional superconductors
• Robust Machine Learning Inference from X-ray Absorption Near Edge Spectra through Featurization
• InterMat: Accelerating Band Offset Prediction in Semiconductor Interfaces with DFT and Deep Learning
• Reproducibility in Computational Materials Science: Lessons from 'A General-Purpose Machine Learning Framework for Predicting Properties of Inorganic Materials'
• Efficient first principles based modeling via machine learning: from simple representations to high entropy materials
• Multi-task graph neural networks for simultaneous prediction of global and atomic properties in ferromagnetic systems
• Macroscale fracture surface segmentation via semi-supervised learning considering the structural similarity
• Hardness and fracture toughness models by symbolic regression
• Monitoring MBE substrate deoxidation via RHEED image-sequence analysis by deep learning
• Data-driven Design of High Pressure Hydride Superconductors using DFT and Deep Learning
• AtomVision: A machine vision library for atomistic images
• Spatially resolved lock-in micro-thermography (SR-LIT): A tensor analysis-enhanced method for anisotropic thermal characterization
• DiffractGPT: Atomic Structure Determination from X-ray Diffraction Patterns using Generative Pre-trained Transformer
• The JARVIS Infrastructure is All You Need for Materials Design
• Towards an automated workflow in materials science for combining multi-modal simulative and experimental information using data mining and large language models
• Three-dimensional (3D) tensor-based methodology for characterizing 3D anisotropic thermal conductivity tensor
• Multi-scale attention-based instance segmentation for measuring crystals with large size variation
• Local structure, thermodynamics, and melting curve of boron phosphide at high pressures by deep learning-driven ab initio simulations
• Reduced-Dimension Surrogate Modeling to Characterize the Damage Tolerance of Composite/Metal Structures
• Unraveling the Impact of Initial Choices and In-Loop Interventions on Learning Dynamics in Autonomous Scanning Probe Microscopy
• Machine-learned models for magnetic materials
• Analysis of Black Hole Solutions in Parabolic Class Using Neural Networks
• Automated Real-Space Lattice Extraction for Atomic Force Microscopy Images
• Identifying Crystal Structures Beyond Known Prototypes from X-ray Powder Diffraction Spectra
• Mind the Gap: Bridging the Divide Between AI Aspirations and the Reality of Autonomous Characterization
• A Study on Quantum Graph Neural Networks Applied to Molecular Physics
• Self-Supervised Generative Models for Crystal Structures
• Nano1D: An accurate Computer Vision software for analysis and segmentation of low-dimensional nanostructures
• Leveraging Interpolation Models and Error Bounds for Verifiable Scientific Machine Learning
• Neural Structure Fields with Application to Crystal Structure Autoencoders
• Confusion-driven machine learning of structural phases of a flexible, magnetic Stockmayer polymer
• Self-supervised Representations and Node Embedding Graph Neural Networks for Accurate and Multi-scale Analysis of Materials
• Predicting Many Crystal Properties via an Adaptive Transformer-based Framework
• Physics-enhanced neural networks for equation-of-state calculations
• Accelerating Ensemble Error Bar Prediction with Single Models Fits
• A generative machine learning model for designing metal hydrides applied to hydrogen storage
• KAN-Enhanced Contrastive Learning Accelerating Crystal Structure Identification from XRD Patterns