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RESEARCH PAPER · Advanced Science · 2021

Direct Prediction of Phonon Density of States With Euclidean Neural Networks

Zhantao Chen, Nina Andrejevic, Tess Smidt, Zhiwei Ding, Qian Xu, Yen‐Ting Chi, Quynh T. Nguyen, Ahmet Alatas, Jing Kong, Mingda Li

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Direct Prediction of Phonon Density of States With Euclidean Neural NetworksOriginalThe Application of Data-Driven Methods and Physics-Based Learning for Improving Battery Safety303A physics-guided neural network framework for elastic plates: Comparison of governing equations-based and energy-based approaches242Machine learning on neutron and x-ray scattering and spectroscopies116Artificial intelligence for search and discovery of quantum materials68Can a shock-induced phonon up-pumping model relate to impact sensitivity of molecular crystals, polymorphs and cocrystals?19Data-Efficient Learning of Materials’ Vibrational Properties0From DFT to ML: A Hierarchical Framework for Validating GNN Force Fields Against Phonon Spectra Without Spectral Leakage0Introduction0Representations and strategies for transferable machine learning improve model performance in chemical discovery19Modeling the Solid Electrolyte Interphase: Machine Learning as a Game Changer?87Density of states prediction for materials discovery via contrastive learning from probabilistic embeddings91Recent advances and applications of deep learning methods in materials science1161MaterialsAtlas.org: a materials informatics web app platform for materials discovery and survey of state-of-the-art70Formula Graph Self‐Attention Network for Representation‐Domain Independent Materials Discovery26Physically Informed Machine Learning Prediction of Electronic Density of States72Challenges and Opportunities of Machine Learning on Neutron and X-ray Scattering6Mechanism of local lattice distortion effects on vacancy migration barriers in fcc alloys10Surrogate- and invariance-boosted contrastive learning for data-scarce applications in science20Machine learning magnetism classifiers from atomic coordinates28Electronic Configurations of 3d Transition-Metal Compounds Using Local Structure and Neural Networks9A data-science approach to predict the heat capacity of nanoporous materials117Towards overcoming data scarcity in materials science: unifying models and datasets with a mixture of experts framework73Equivariant Neural Networks for Materials: A Review of Architectural Innovations and What They Actually Guarantee0The iron group transition-metal (Fe, Ru, Os) coordination of Se-doped graphitic carbon (Se@g-C 3 N 4 ) nanostructures for the smart therapeutic delivery of zidovudine (ZVD) as an antiretroviral drug: a theoretical calculation perspective7Direct prediction of inelastic neutron scattering spectra from the crystal structure*17Connecting Vibrational Spectroscopy to Atomic Structure via Supervised Manifold Learning: Beyond Peak Analysis13Roadmap on artificial intelligence and big data techniques for superconductivity66Rapid prediction of phonon structure and properties using the atomistic line graph neural network (ALIGNN)33Representations of Materials for Machine Learning133Decoding structure-spectrum relationships with physically organized latent spaces15A Roadmap Review of Thermally Conductive Polymer Composites: Critical Factors, Progress, and Prospects419A2BB’X6/ABX3-type High-performance perovskites screening based on ensemble learning and high throughput screening13Examining graph neural networks for crystal structures: Limitations and opportunities for capturing periodicity96Phonon DOS‐Based Machine Learning Model for Designing High‐Performance Solid Electrolytes in Li‐Ion Batteries8Rapid Prediction of Inelastic Neutron Scattering Spectra0Phase Diagrams of Alloys and Their Hydrides via On-Lattice Graph Neural Networks and Limited Training Data23Robust Machine Learning Inference from X-ray Absorption Near Edge Spectra through Featurization28Machine learning a universal harmonic interatomic potential for predicting phonons in crystalline solids13Methods and applications of machine learning in computational design of optoelectronic semiconductors22Theory for Equivariant Quantum Neural Networks110Symmetry breaking in 2D materials for optimizing second-harmonic generation14Virtual node graph neural network for full phonon prediction25Structure to Property: Chemical Element Embeddings for Predicting Electronic Properties of Crystals11Boosting graph neural networks with virtual nodes to predict phonon properties0Discovery of highly anisotropic dielectric crystals with equivariant graph neural networks14Rational Design of Earth‐Abundant Catalysts toward Sustainability83Unveiling the unexpected sinking and embedding dynamics of surface supported Mo/S clusters on 2D MoS 2 with active machine learning16Universal Ensemble‐Embedding Graph Neural Network for Direct Prediction of Optical Spectra from Crystal Structures41A Cluster‐Based Deep Learning Model Perceiving Series Correlation for Accurate Prediction of Phonon Spectrum4AI-powered exploration of molecular vibrations, phonons, and spectroscopy19Accelerating superconductor discovery through tempered deep learning of the electron-phonon spectral function13LASSO for CALPHAD Model Selection Enables Data-Efficient Thermodynamic Modeling: An Application in Thermochemical Hydrogen Production Materials5AI‐Driven Defect Engineering for Advanced Thermoelectric Materials37Artificial intelligence for science in quantum, atomistic, and continuum systems71Deep Learning for Computational Heterogeneous Catalysis: Fundamentals and Applications3Structural constraint integration in a generative model for the discovery of quantum materials12Acceleration of crystal structure relaxation with deep reinforcement learning5A Machine Learning Framework for Modeling Ensemble Properties of Atomically Disordered Materials7Δ-Learning of High-Fidelity Electronic Structure Using Graph Neural Networks with Modified Node-Level Features0Entropy in materials design: modelling entropic stabilisation of crystalline materials1FSDT-PINN: A Physics-Informed Framework for Nonlinear Bending of Laminated Composite Plates on Elastic Foundations0TopoFormer : a topological-geometric neural network for accurate protein solubility prediction0Machine Learning-Enhanced Molecular Dynamics: Current State, Challenges and Perspectives5Investigating the High-Temperature Oxidation Behavior of Ti-5Sn-xEr and Ti-6.5Ni-xEr Alloys Using Multi-scale Convolutional Neural Networks0Crystalline Material Discovery in the Era of Artificial Intelligence9Accurate prediction of tensorial spectra using equivariant graph neural network3Machine Learning Prediction of Crystal Structure Stability toward the Design of High-Entropy Oxides2

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