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RESEARCH PAPER · Nature Communications · 2022

Density of states prediction for materials discovery via contrastive learning from probabilistic embeddings

Shufeng Kong, Francesco Ricci, Dan Guevarra, Jeffrey B. Neaton, Carla Pedro Gomes, John M. Gregoire

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Density of states prediction for materials discovery via contrastive learning from probabilistic embeddingsOriginalOrigin of Bismuth‐Rich Strategy in Bismuth Oxyhalide Photocatalysts38Physically Informed Machine Learning Prediction of Electronic Density of States72Advancing data-driven chemistry by beating benchmarks16Flexible assembled tactile sensor with freely integration design7Examining graph neural networks for crystal structures: limitations and opportunities for capturing periodicity7Substructure interaction graph network with node augmentation for hybrid chemical systems of heterogeneous substructures7Connecting Vibrational Spectroscopy to Atomic Structure via Supervised Manifold Learning: Beyond Peak Analysis13Rapid prediction of phonon structure and properties using the atomistic line graph neural network (ALIGNN)33Structural, Electronic Properties, and Relative Stability Studies of Low-Energy Indium Oxide Polytypes Using First-Principles Calculations11A critical examination of robustness and generalizability of machine learning prediction of materials properties144Representations of Materials for Machine Learning132High-entropy perovskites materials for next-generation energy applications75Computational Simulation for Breakdown and Energy Storage Performances with Optimization in Polymer Dielectrics86Machine Learning Enhanced DFTB Method for Periodic Systems: Learning from Electronic Density of States20Voxelized atomic structure framework for materials design and discovery7A deep learning framework to emulate density functional theory81Atomic Positional Embedding-Based Transformer Model for Predicting the Density of States of Crystalline Materials12Data science and material informatics in physical metallurgy and material science: An overview of milestones and limitations32Navigating Materials Space with ML-Generated Electronic Fingerprints1Machine-Learning-Accelerated Screening of Double-Atom/Cluster Electrocatalysts for the Oxygen Reduction Reaction27MatGPT: A Vane of Materials Informatics from Past, Present, to Future74Applications and potentials of machine learning in optoelectronic materials research: An overview and perspectives10Examining graph neural networks for crystal structures: Limitations and opportunities for capturing periodicity96Application and prospect of machine learning in photoelectron spectroscopy2Phonon DOS‐Based Machine Learning Model for Designing High‐Performance Solid Electrolytes in Li‐Ion Batteries8Theoretical search for characteristic atoms in supported gold nanoparticles: a large-scale DFT study7The Case for a Defect Genome Initiative23Computational Design of Optimized Modular Photovoltaic Electrochemical Reactor for Energy Efficient CO2‐to‐Cn Reduction Reaction with Bandgap Tunable Perovskite Tandem Cells1Machine Learning to Promote Efficient Screening of Low‐Contact Electrode for 2D Semiconductor Transistor Under Limited Data18A review on the applications of graph neural networks in materials science at the atomic scale61Prediction of electronic density of states in guanine-TiO 2 adsorption model based on machine learning7Structure-based out-of-distribution (OOD) materials property prediction: a benchmark study66Virtual node graph neural network for full phonon prediction25Structure to Property: Chemical Element Embeddings for Predicting Electronic Properties of Crystals11Prediction of Frequency-Dependent Optical Spectrum for Solid Materials: A Multioutput and Multifidelity Machine Learning Approach18Discovery of novel materials through machine learning24Self-supervised generative models for crystal structures6Improving thermodynamic stability of double perovskites with machine learning: The role of cation composition28Unifying theory of electronic descriptors of metal surfaces upon perturbation8Investigating Static and Dynamic Behaviors in 3D Chiral Mechanical Metamaterials by Disentangled Generative Models31Discovery of high-performance dielectric materials with machine-learning-guided search9Enhancing perovskite solar cells performance through tailored design: pyridine-functionalized phenothiazine derivatives with modified end-capped acceptor moieties12A Cluster‐Based Deep Learning Model Perceiving Series Correlation for Accurate Prediction of Phonon Spectrum4Investigation of electronic, optoelectronic, and mechanical properties of lead-free cubic perovskite compound using density functional theory21Data driven studies of magnetic ground state and transition temperature in two-dimensional magnets2Colloidal phenomena reflect the interplay between interfacial solution structure, interparticle forces, and dynamical response10Kolmogorov–Arnold Network Made Learning Physics Laws Simple15Recent Advances in Machine Learning‐Assisted Multiscale Design of Energy Materials188First-principles calculations on structural stability and electronic properties of the Strontium-Argon System at High Pressure2AI-powered exploration of molecular vibrations, phonons, and spectroscopy19Algorithmic Discovery vs. Scientific Discovery: A Conceptual Boundary for AI-Driven Materials Research0Screening Vanadium-Based Electrode Materials for Lithium-Ion Batteries Based on Machine Learning and First-Principles1Reusability report: Deep learning-based analysis of images and spectroscopy data with AtomAI9Adaptive energy reference for machine-learning models of the electronic density of states9Multimodal foundation models for material property prediction and discovery42CrysToGraph: A Comprehensive Predictive Model for Crystal Material Properties and the Benchmark4Accurate classification of materials with elEmBERT: Element embeddings for chemical benchmarks1Predicting Macroscopic Properties of Amorphous Monolayer Carbon via Pair Correlation Function9Predicting Many Crystal Properties via an Adaptive Transformer-Based Framework1High-throughput computation and machine learning modeling of magnetic moments and Mössbauer parameters for Fe-based intermetallics3Graph Transformer Model Integrating Physical Features for Projected Electronic Density of States Prediction3AI‐Driven Defect Engineering for Advanced Thermoelectric Materials37Machine learning and data-driven methods in computational surface and interface science27Accelerated data-driven materials science with the Materials Project296Exploiting Multitask Deep Learning to Identify Multiple Heavy Metal Contamination at Large Scales0Heat capacity estimation of complex materials for energy technologies11Density of States Prediction by a Metric-Optimized Equivariant Graph Neural Network0Accelerating density of states prediction in Zn-doped MgO nanoparticles via kernel-optimized weighted k-NN28Deep Learning for Computational Heterogeneous Catalysis: Fundamentals and Applications3Scalable density of states prediction in nanoalloys via machine learning0Future of condensed matter physics for the next 10 years*0Review of machine learning tight-binding models: Route to accurate and scalable electronic simulations0Δ-Learning of High-Fidelity Electronic Structure Using Graph Neural Networks with Modified Node-Level Features0T2MAT (text-to-material): A universal agent for generating material structures with goal properties from a single sentence1Mol2Raman: a graph neural network model for predicting Raman spectra from SMILES representations4Rationalizing the d-Band Model from Theory to Practice in Catalyst Design108A Physically Interpretable Descriptor for Predicting and Designing Mechanically Exfoliable Two-Dimensional Nanomaterials1Entropy in materials design: modelling entropic stabilisation of crystalline materials1Informatics for thermodynamics: Stability to synthesizability0Learning unfolded band structures from spectra0Physically consistent learning of electronic structures via bidirectional atomic-DOS mapping0Rational design of nitrogen-doped MXene quantum dots: Structure–defect–surface synergy for high photoluminescence and tunable emission1Resonant Enhancement of Ion Transport in Graphene Channels by Alternating Electric Fields0Learning atomic representations for data-driven materials design2AI-driven multi-property prediction and manufacturability-aware design for electronic materials and devices0A foundation model for non-destructive defect identification from vibrational spectra1Artificial intelligence-driven paradigms for layered cathode materials: A systematic framework for multi-scale engineering2Recent advances in electronic structure learning0Computational strategy to explore AlMgX3 (X = F, Cl, Br, I, H) perovskites for energy applications0Navigating the Materials Space with Machine-Learning-Generated Electronic Fingerprints0Structural benchmarks overlook density-of-states errors in machine-learned interatomic potentials0

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