Automated Phase Mapping with AgileFD and its Application to Light Absorber Discovery in the V–Mn–Nb Oxide System

New Contributions to the Optics of Intensely Light-Scattering Materials Part I

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An analysis of the optimal band gaps of light absorbers in integrated tandem photoelectrochemical water-splitting systems

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Non-negative Matrix Factor Deconvolution; Extraction of Multiple Sound Sources from Monophonic Inputs

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On-the-fly machine-learning for high-throughput experiments: search for rare-earth-free permanent magnets

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Applications of high throughput (combinatorial) methodologies to electronic, magnetic, optical, and energy-related materials

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High Throughput Light Absorber Discovery, Part 1: An Algorithm for Automated Tauc Analysis

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Thin Film Structure of Triisopropylsilylethynyl‐Functionalized Pentacene and Tetraceno[2,3‐b]thiophene from Grazing Incidence X‐Ray Diffraction

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Rapid structural mapping of ternary metallic alloy systems using the combinatorial approach and cluster analysis

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Perspective: Composition–structure–property mapping in high-throughput experiments: Turning data into knowledge

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Rapid identification of structural phases in combinatorial thin-film libraries using x-ray diffraction and non-negative matrix factorization

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High Throughput Discovery of Solar Fuels Photoanodes in the CuO–V2O5 System

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High-throughput determination of structural phase diagram and constituent phases using GRENDEL

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High-throughput synchrotron X-ray diffraction for combinatorial phase mapping

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Mn2V2O7: An Earth Abundant Light Absorber for Solar Water Splitting

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PolySNAP: a computer program for analysing high-throughput powder diffraction data

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Rapid Identification of Areas of Interest in Thin Film Materials Libraries by Combining Electrical, Optical, X-ray Diffraction, and Mechanical High-Throughput Measurements: A Case Study for the System Ni–Al

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Generalized machine learning technique for automatic phase attribution in time variant high-throughput experimental studies

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Constraint Reasoning and Kernel Clustering for Pattern Decomposition with Scaling

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A reliable methodology for high throughput identification of a mixture of crystallographic phases from powder X-ray diffraction data

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Pattern Decomposition with Complex Combinatorial Constraints: Application to Materials Discovery

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High-throughput on-the-fly scanning ultraviolet-visible dual-sphere spectrometer

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Semi-Supervised Approach to Phase Identification from Combinatorial Sample Diffraction Patterns

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Synthesis, surface group modification of 3D MnV2O6 nanostructures and adsorption effect on Rhodamine B

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Non-equilibrium synthesis, structure, and opto-electronic properties of Cu2−2x Zn x O alloys

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SMT-Aided Combinatorial Materials Discovery

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High Throughput Light Absorber Discovery, Part 2: Establishing Structure–Band Gap Energy Relationships

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Challenges in Materials Discovery – Synthetic Generator and Real Datasets

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An integrated graph cuts segmentation and piece-wise convex unmixing approach for hyperspectral imaging

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Emerging materials intelligence ecosystems propelled by machine learning

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Discovery of new materials using combinatorial synthesis and high-throughput characterization of thin-film materials libraries combined with computational methods

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Tackling climate change with machine learning

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Accelerated Development of Perovskite-Inspired Materials via High-Throughput Synthesis and Machine-Learning Diagnosis

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Fast and interpretable classification of small X-ray diffraction datasets using data augmentation and deep neural networks

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A deep-learning technique for phase identification in multiphase inorganic compounds using synthetic XRD powder patterns

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Emerging Trends in Machine Learning: A Polymer Perspective

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Materials science in the artificial intelligence age: high-throughput library generation, machine learning, and a pathway from correlations to the underpinning physics

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Random forest machine learning models for interpretable X-ray absorption near-edge structure spectrum-property relationships

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Symmetry prediction and knowledge discovery from X-ray diffraction patterns using an interpretable machine learning approach

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Machine learning in nuclear materials research

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Scientific AI in materials science: a path to a sustainable and scalable paradigm

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A perspective on inverse design of battery interphases using multi-scale modelling, experiments and generative deep learning

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Artificial intelligence for materials discovery

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Unsupervised phase mapping of X-ray diffraction data by nonnegative matrix factorization integrated with custom clustering

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Crystallography companion agent for high-throughput materials discovery

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Analyzing machine learning models to accelerate generation of fundamental materials insights

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Machine Learning Roadmap for Perovskite Photovoltaics

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Machine Learning for Perovskites' Reap-Rest-Recovery Cycle

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Accelerating materials discovery: combinatorial synthesis, high-throughput characterization, and computational advances

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Emerging Capabilities for the High-Throughput Characterization of Structural Materials

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Artificial intelligence for search and discovery of quantum materials

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Deep learning for visualization and novelty detection in large X-ray diffraction datasets

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Tracking materials science data lineage to manage millions of materials experiments and analyses

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A data-driven XRD analysis protocol for phase identification and phase-fraction prediction of multiphase inorganic compounds

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Image-based machine learning for materials science

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The Autonomous Formulation Laboratory: An Open Liquid Handling Platform for Formulation Discovery Using X-ray and Neutron Scattering

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Rapid Construction of Fe–Co–Ni Composition-Phase Map by Combinatorial Materials Chip Approach

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Automated estimation of materials parameter from X-ray absorption and electron energy-loss spectra with similarity measures

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Machine Learning for Accelerating Energy Materials Discovery: Bridging Quantum Accuracy with Computational Efficiency

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Carbon materials for high-performance potassium-ion energy-storage devices

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Structure–Property-Performance Relationship of Ultrathin Pd–Au Alloy Catalyst Layers for Low-Temperature Ethanol Oxidation in Alkaline Media

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Strategies for accelerating the adoption of materials informatics

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CRYSTAL: a multi-agent AI system for automated mapping of materials’ crystal structures

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Machine learning for scattering data: strategies, perspectives and applications to surface scattering

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Constrained non-negative matrix factorization enabling real-time insights of in situ and high-throughput experiments

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Application of machine learning to quantification of mineral composition on gas hydrate-bearing sediments, Ulleung Basin, Korea

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Application of machine learning to reflection high-energy electron diffraction images for automated structural phase mapping

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Synchrotron radiation data-driven artificial intelligence approaches in materials discovery

Li, Q., Xing, R., Li, L., Yao, H., Wu, L., & Zhao, L. (2024) · Artificial Intelligence Chemistry · doi:10.1016/j.aichem.2024.100045 · Cited by 30 · see citing works

Machine learning-facilitated multiscale imaging for energy materials

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Application of Machine Learning in Determining the Mechanical Properties of Materials

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Phase‐Mapper: Accelerating Materials Discovery with AI

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Machine‐Learning Clustering Technique Applied to Powder X‐Ray Diffraction Patterns to Distinguish Compositions of ThMn12‐Type Alloys

Utimula, K., Hunkao, R., Yano, M., Kimoto, H., Hongo, K., Kawaguchi, S., Suwanna, S., & Maezono, R. (2020) · Advanced Theory and Simulations · doi:10.1002/adts.202000039 · Cited by 24 · see citing works

On-the-Fly Data Assessment for High-Throughput X-ray Diffraction Measurements

Ren, F., Pandolfi, R., Campen, D. G. V., Hexemer, A., & Mehta, A. (2017) · ACS Combinatorial Science · doi:10.1021/acscombsci.7b00015 · Cited by 24 · see citing works

Experimental Establishment of Phase Diagrams Guided by Uncertainty Sampling: An Application to the Deposition of Zn–Sn–P Films by Molecular Beam Epitaxy

Katsube, R., Terayama, K., Tamura, R., & Nose, Y. (2020) · ACS Materials Letters · doi:10.1021/acsmaterialslett.0c00104 · Cited by 24 · see citing works

Machine learning in X-ray diffraction for materials discovery and characterization

Davel, C. M., Bassiri‐Gharb, N., & Correa‐Baena, J. (2025) · Matter · doi:10.1016/j.matt.2025.102272 · Cited by 22 · see citing works

Combinatorial Discovery of Lanthanum–Tantalum Oxynitride Solar Light Absorbers with Dilute Nitrogen for Solar Fuel Applications

Suram, S. K., Fackler, S., Zhou, L., N’Diaye, A. T., Drisdell, W. S., Yano, J., & Gregoire, J. M. (2017) · ACS Combinatorial Science · doi:10.1021/acscombsci.7b00143 · Cited by 17 · see citing works

Automated Data Analysis for Powder X-Ray Diffraction Using Machine Learning

Suzuki, Y. (2022) · Synchrotron Radiation News · doi:10.1080/08940886.2022.2112496 · Cited by 15 · see citing works

Feature Space of XRD Patterns Constructed by an Autoencoder

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An Improved High-Throughput Data Processing Based on Combinatorial Materials Chip Approach for Rapid Construction of Fe–Cr–Ni Composition-Phase Map

Zhao, Z., Jin, Y. J., Shi, P., Xue, Y., Zhao, B., Zhang, Y., Huang, F., Bi, P. Y., & Wang, Q. (2019) · ACS Combinatorial Science · doi:10.1021/acscombsci.9b00149 · Cited by 13 · see citing works

Relaxation Methods for Constrained Matrix Factorization Problems: Solving the Phase Mapping Problem in Materials Discovery

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Learning material synthesis–process–structure–property relationship by data fusion: Bayesian co-regionalization N-dimensional piecewise function learning

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Probabilistic phase labeling and lattice refinement for autonomous materials research

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Autonomous Small-Angle Scattering for Accelerated Soft Material Formulation Optimization

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Autonomous phase mapping of gold nanoparticles synthesis with differentiable models of spectral shape

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Identifying crystal structures beyond known prototypes from x-ray powder diffraction spectra

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Materials structure–property factorization for identification of synergistic phase interactions in complex solar fuels photoanodes

Guevarra, D., Zhou, L., Richter, M. H., Shinde, A., Chen, D., Gomes, C. P., & Gregoire, J. M. (2022) · npj Computational Materials · doi:10.1038/s41524-022-00747-1 · Cited by 7 · see citing works

Interpretable X-ray diffraction spectra analysis using confidence evaluated deep learning enhanced by template element replacement

Xing, R., Yao, H., Xi, Z., Sun, M., Li, Q., Tian, J., Wang, H., Xu, D., Ma, Z., & Zhao, L. (2025) · npj Computational Materials · doi:10.1038/s41524-025-01743-x · Cited by 6 · see citing works

Direct feature extraction from two-dimensional X-ray diffraction images of semiconductor thin films for fabrication analysis

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Inferring phase diagrams from X-ray data with background signals using graph segmentation

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Learning from the big picture

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