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Data-Driven Discovery of Full-Visible-Spectrum Phosphor

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Corrosion and tribocorrosion behavior of equiatomic refractory medium entropy TiZr(Hf, Ta, Nb) alloys in chloride solutions

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Structure-Based Synthesizability Prediction of Crystals Using Partially Supervised Learning

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Tailoring the Cation Lattice for Chloride Lithium‐Ion Conductors

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Application of geopolymers for treatment of industrial solid waste containing heavy metals: State-of-the-art review

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Accelerated materials design of Na 0.5 Bi 0.5 TiO 3 oxygen ionic conductors based on first principles calculations

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Design of Li 1+2x Zn 1−x PS 4 , a new lithium ion conductor

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Computational evaluation of comprehensive properties of MgX3H8 (X = Sc, Ti and Zr) as effective solid state hydrogen storage materials

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Computational and experimental investigation of TmAgTe 2 and XYZ 2 compounds, a new group of thermoelectric materials identified by first-principles high-throughput screening

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Bayesian-Driven First-Principles Calculations for Accelerating Exploration of Fast Ion Conductors for Rechargeable Battery Application

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Predicting stable crystalline compounds using chemical similarity

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Ablation behaviour of (Hf-Ta-Zr-Nb)C high entropy carbide ceramic at temperatures above 2100 °C

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Machine‐Learning‐Assisted Determination of the Global Zero‐Temperature Phase Diagram of Materials

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Toward the Rational Design of Mid‐Infrared Nonlinear Optical Materials with Targeted Properties via a Multi‐Level Data‐Driven Approach

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Predicting density functional theory total energies and enthalpies of formation of metal-nonmetal compounds by linear regression

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Targeted design of advanced electrocatalysts by machine learning

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Accelerating the prediction of stable materials with machine learning

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Structure and Dynamics of Fluorophosphate Na-Ion Battery Cathodes

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Perspectives for next generation lithium-ion battery cathode materials

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Chemical evaluation of immobilization of wastes containing Pb, Cd, Cu and Zn in alkali-activated materials: A critical review

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Symmetry-enforced Weyl phonons

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An efficient rule-based screening approach for discovering fast lithium ion conductors using density functional theory and artificial neural networks

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Electronic Structure Descriptor for the Discovery of Narrow-Band Red-Emitting Phosphors

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Structural and Na-ion conduction characteristics of Na 3 PS x Se 4−x

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Nanoinformatics, and the big challenges for the science of small things

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Analyzing machine learning models to accelerate generation of fundamental materials insights

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Charge-four Weyl phonons

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Rationalizing accurate structure prediction in the meta-GGA SCAN functional

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Assessing High-Throughput Descriptors for Prediction of Transparent Conductors

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Large scale in silico screening of materials for carbon capture through chemical looping

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Similarity of Precursors in Solid-State Synthesis as Text-Mined from Scientific Literature

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Accelerated discovery of two crystal structure types in a complex inorganic phase field

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Lithium Oxide Superionic Conductors Inspired by Garnet and NASICON Structures

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Chemist versus Machine: Traditional Knowledge versus Machine Learning Techniques

George, J., & Hautier, G. (2020) · Trends in Chemistry · doi:10.1016/j.trechm.2020.10.007 · Cited by 79 · see citing works

Large scale computational screening and experimental discovery of novel materials for high temperature CO 2 capture

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Computational Prediction and Evaluation of Solid-State Sodium Superionic Conductors Na7P3X11 (X = O, S, Se)

Wang, Y. E., Richards, W. D., Bo, S., Miara, L. J., & Ceder, G. (2017) · Chemistry of Materials · doi:10.1021/acs.chemmater.7b02476 · Cited by 80 · see citing works

Collaborative crystal structure prediction

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Substitution-Based Analysis of Structural Novelty for Generative Models of MaterialsPreprint

Negishi, M., & Walsh, A. (2026) · arXiv (Cornell University) · doi:10.48550/arxiv.2606.23166

Prediction Of Structure Candidates For Simple Ionic Compounds Using Global Optimisation

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Crystal structure prediction with machine learning-based element substitution

Kusaba, M., Liu, C., & Yoshida, R. (2022) · Computational Materials Science · doi:10.1016/j.commatsci.2022.111496 · Cited by 60 · see citing works

Predicting stable crystalline compounds using chemical similarity

Wang, H., Botti, S., & Marques, M. A. L. (2021) · npj Computational Materials · doi:10.1038/s41524-020-00481-6 · Cited by 115 · see citing works