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RESEARCH PAPER · Nature Computational Science · 2021

Crystallography companion agent for high-throughput materials discovery

Phillip M. Maffettone, Lars Banko, Peng Cui, Yury Lysogorskiy, Marc A. Little, Daniel Olds, Alfred Ludwig, Andrew I. Cooper

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Crystallography companion agent for high-throughput materials discoveryOriginalToward autonomous design and synthesis of novel inorganic materials159AI tool makes phase identification crystal clear1Probabilistic Deep Learning Approach to Automate the Interpretation of Multi-phase Diffraction Spectra103Deep learning for visualization and novelty detection in large X-ray diffraction datasets63Constrained non-negative matrix factorization enabling real-time insights of in situ and high-throughput experiments31Application of machine learning classifiers to X‐ray diffraction imaging with medically relevant phantoms5Rechargeable Batteries of the Future—The State of the Art from a BATTERY 2030+ Perspective376Benchmarking Autonomous Scattering Experiments Illustrated on TAS5Recent advances and applications of deep learning methods in materials science1161Powder X‐Ray Diffraction Pattern Is All You Need for Machine‐Learning‐Based Symmetry Identification and Property Prediction55Advancing Discovery with Artificial Intelligence and Machine Learning at NSLS-II12Perovskite or Not Perovskite? A Deep‐Learning Approach to Automatically Identify New Hybrid Perovskites from X‐ray Diffraction Patterns51Human- and machine-centred designs of molecules and materials for sustainability and decarbonization137Why big data and compute are not necessarily the path to big materials science51Interpretable Deep Learning Model for Analyzing the Relationship between the Electronic Structure and Chemisorption Property6Identification of chemical compositions from “featureless” optical absorption spectra: Machine learning predictions and experimental validations24Delivering real-time multi-modal materials analysis with enterprise beamlines5Machine-learning for designing nanoarchitectured materials by dealloying13High-Throughput Electrochemistry to Study Materials Degradation in Extreme Environments11Feature Space of XRD Patterns Constructed by an Autoencoder13Machine learning for analysis of experimental scattering and spectroscopy data in materials chemistry52Neural networks trained on synthetically generated crystals can extract structural information from ICSD powder X-ray diffractograms11CrystalMELA : a new crystallographic machine learning platform for crystal system determination23Exploring supervised machine learning for multi-phase identification and quantification from powder X-ray diffraction spectra36Adaptively driven X-ray diffraction guided by machine learning for autonomous phase identification85Autonomous x-ray scattering26A Deep Learning Approach to Powder X‐Ray Diffraction Pattern Analysis: Addressing Generalizability and Perturbation Issues Simultaneously29Validating neural networks for spectroscopic classification on a universal synthetic dataset34Application of self-supervised approaches to the classification of X-ray diffraction spectra during phase transitions5An optimization-based supervised learning algorithm for PXRD phase fraction estimation2Deep Learning Models to Identify Common Phases across Material Systems from X-ray Diffraction12Deep Learning Enables Rapid Identification of a New Quasicrystal from Multiphase Powder Diffraction Patterns16Flexible formulation of value for experiment interpretation and design6Should We Teach AI a Better Scientific Method?5Learning material synthesis–process–structure–property relationship by data fusion: Bayesian co-regionalization N-dimensional piecewise function learning9Towards a science exocortex11XRDMatch: a semi-supervised learning framework to efficiently discover room temperature lithium superionic conductors12MatNexus: A comprehensive text mining and analysis suite for materials discovery9Integrated analysis of X-ray diffraction patterns and pair distribution functions for machine-learned phase identification46Crystal Structure Assignment for Unknown Compounds from X-ray Diffraction Patterns with Deep Learning31Development of a high-energy x-ray diffraction system for determining materials' signatures relevant to transportation security0Crystallographic phase identifier of a convolutional self-attention neural network (CPICANN) on powder diffraction patterns10From Characterization to Discovery: Artificial Intelligence, Machine Learning and High-Throughput Experiments for Heterogeneous Catalyst Design190X-ray diffraction and its emerging applications in the food industry27Machine learning assisted sorting of active microswimmers2Towards end-to-end structure determination from x-ray diffraction data using deep learning17Deep learning for symmetry classification using sparse 3D electron density data for inorganic compounds5Crystal Structure Determination from Powder Diffraction Patterns with Generative Machine Learning25Identifying crystal structures beyond known prototypes from x-ray powder diffraction spectra7Efficient Exploratory Synthesis of Quaternary Cesium Chlorides Guided by In Silico Predictions5A Database of Experiments for the Development of Sulfide-Type Solid Electrolytes1Inverse design workflow discovers hole-transport materials tailored for perovskite solar cells183Intelligent understanding of spectra: from structural elucidation to property design27Machine learning-driven prediction of ultrafast spin relaxation in metal halide perovskites for spintronic applications5End‐to‐End Crystal Structure Prediction from Powder X‐Ray Diffraction24Anodic oxidation using 3D carbon felt/PbO 2 anode: a electron transfer-mediated system for degradation of Rhodamine B7DiffractGPT: Atomic Structure Determination from X-ray Diffraction Patterns Using a Generative Pretrained Transformer15Screening of modular supramolecular star polymers for 3D printing of biomedical devices2Enhancing phase characterization of AlCuCrFeNi high entropy alloys using hybrid machine learning models: A comprehensive XRD analysis11Ab initio structure solutions from nanocrystalline powder diffraction data via diffusion models27Machine learning approaches for intentional materials engineering5Probabilistic phase labeling and lattice refinement for autonomous materials research9Machine learning assisted nanobeam X-ray diffraction based analysis on hydride vapor-phase epitaxy GaN2AI-driven new materials development and data standardization4Real-time experiment-theory closed-loop interaction for autonomous materials science29Artificial Intelligence for Materials Discovery, Development, and Optimization171Substance Science Insight Based on Spectroscopic Large Model0Machine learning in X-ray diffraction for materials discovery and characterization19Advances in metastable phase catalysis with thermodynamic-kinetic adaptability13XQueryer: an intelligent crystal structure identifier for powder X-ray diffraction5Agentic AI for Accelerated Materials Characterization in Synchrotron Science2Automated phase mapping of high-throughput X-ray diffraction data encoded with domain-specific materials science knowledge7Computationally accelerated experimental materials characterization—drawing inspiration from high-throughput simulation workflows6AI agents for solid electrolytes: opportunities, challenges, and future directions14Accelerating catalytic advancements through the precision of high-throughput experiments & calculations4Automated Signal Processing for Crystallite Size Estimation from XRD Data: A Simplified Alternative to Advanced Microscopy0Combinatorial to autonomous experimentation0A Neural Network‐Based Numerical Approach for X‐Ray Diffraction Analysis: Application to Spinel Materials2Machine learning approaches for crystallographic classification from synthetic 2D X-ray diffraction data0Dara: Automated Multiple-Hypothesis Phase Identification and Refinement from Powder X-ray Diffraction6Machine Learning for Catalytic Reaction Systems: A Framework for Complex Chemical Processes7Agentic material science5Leveraging transfer learning for accurate estimation of ionic migration barriers in solids3KAN-enhanced contrastive learning: the accelerator of crystal structure identification from XRD patterns1Physics-Informed Unsupervised Clustering Approach to the Identification of High-Pressure Elusive Phases from Spatially Resolved X-ray Diffraction Large Datasets1High-Throughput Electrochemistry Using Scanning Electrochemical Cells42026 Roadmap on compositionally complex and high entropy materials for energy applications5Parameter-free Kα2 elimination for X-ray diffraction via dual-wavelength q-space differencing1Sustainable Materials Design With Multi‐Modal Artificial Intelligence1IPSBrain: A Unified Intelligent Data Analysis Platform for Multimodal Experimental Characterization at Advanced Photon Source2Toward Class Imbalance and Uncertainty in Powder XRD Analysis: A Dual-Channel Fusion Network for Space Group Classification0Real-time Multi-instrument Autonomous Materials Screening for Discovery of Novel Phase-change Memory Materials0A Comparative Study of Structural Representations for 2D Materials: Insights from Dynamic Collision Fingerprint and Matminer Library0Virtual commissioning of the Korea Light Source Hard X-ray Nanoprobe beamline with multilingual natural language control0Autonomous laboratories for sustainable nanomaterials discovery0Enhancing high-throughput battery materials discovery and design with artificial intelligence0Particulate discrimination via multimodal machine learning in coordinated space0Advancements in nanostructure characterization through XRD peak broadening analysis0A comparative study of convolutional neural networks and vision transformers for quantitative phase analysis from X-ray diffraction0

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