AFLOW-CHULL: Cloud-Oriented Platform for Autonomous Phase Stability Analysis

Generalized Gradient Approximation Made Simple

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Entropy-stabilized oxides

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Materials Design and Discovery with High-Throughput Density Functional Theory: The Open Quantum Materials Database (OQMD)

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The high-throughput highway to computational materials design

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High-throughput electronic band structure calculations: Challenges and tools

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AFLOW: An automatic framework for high-throughput materials discovery

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Cobalt-Base High-Temperature Alloys

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AFLOWLIB.ORG: A distributed materials properties repository from high-throughput ab initio calculations

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A Combinatorial Approach to Materials Discovery

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Universal fragment descriptors for predicting properties of inorganic crystals

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AiiDA: automated interactive infrastructure and database for computational science

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Combinatorial and High-Throughput Screening of Materials Libraries: Review of State of the Art

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Combinatorial solid-state chemistry of inorganic materials

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The AFLOW standard for high-throughput materials science calculations

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Short Report: Reaction Time Analysis with Outlier Exclusion: Bias Varies with Sample Size

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Efficient generation of generalized Monkhorst-Pack grids through the use of informatics

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High-throughput computational screening of thermal conductivity, Debye temperature, and Grüneisen parameter using a quasiharmonic Debye model

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Accelerated discovery of new magnets in the Heusler alloy family

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An Introduction to the Geometry of N Dimensions

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Statistical Mechanics

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Identification of novel compositions of ferromagnetic shape-memory alloys using composition spreads

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Predicting the thermodynamic stability of perovskite oxides using machine learning models

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The AFLOW Library of Crystallographic Prototypes: Part 1

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The Computational Materials Repository

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Reformulation of DFT + U as a Pseudohybrid Hubbard Density Functional for Accelerated Materials Discovery

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The search for high entropy alloys: A high-throughput ab-initio approach

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The crystal structure of the heusler alloys

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A RESTful API for exchanging materials data in the AFLOWLIB.org consortium

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Predictions of new AB O 3 perovskite compounds by combining machine learning and density functional theory

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Nanograined Half‐Heusler Semiconductors as Advanced Thermoelectrics: An Ab Initio High‐Throughput Statistical Study

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Charge compensation and electrostatic transferability in three entropy-stabilized oxides: Results from density functional theory calculations

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Spectral descriptors for bulk metallic glasses based on the thermodynamics of competing crystalline phases

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Towards high-performance permanent magnets without rare earths

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Hafnium binary alloys from experiments and first principles

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Modeling Off-Stoichiometry Materials with a High-Throughput Ab-Initio Approach

Yang, K., Oses, C., & Stefano Curtarolo, A. (2016) · Chemistry of Materials · doi:10.1021/acs.chemmater.6b01449 · Cited by 119 · see citing works

The AFLOW Library of Crystallographic Prototypes: Part 2

Hicks, D., Mehl, M. J., Gossett, E., Toher, C., Levy, O., Hanson, R. M., Hart, G. L. W., & Stefano Curtarolo, A. (2019) · Computational Materials Science · doi:10.1016/j.commatsci.2018.10.043 · Cited by 115 · see citing works

High-throughput computational search for strengthening precipitates in alloys

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Comprehensive Search for New Phases and Compounds in Binary Alloy Systems Based on Platinum-Group Metals, Using a Computational First-Principles Approach

Hart, G. L. W., Stefano Curtarolo, A., Massalski, T. B., & Levy, O. (2013) · Physical Review X · doi:10.1103/physrevx.3.041035 · Cited by 94 · see citing works

Uncovering Compounds by Synergy of Cluster Expansion and High-Throughput Methods

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AFLOWπ: A minimalist approach to high-throughput ab initio calculations including the generation of tight-binding hamiltonians

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A computational high-throughput search for new ternary superalloys

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Cross Products of Vectors in Higher Dimensional Euclidean Spaces

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Lattice dynamics of NaAlH4 from high-temperature single-crystal Raman scattering andab initiocalculations: Evidence of highly stable AlH4− anions

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Ordered Structures in Rhenium Binary Alloys from First-Principles Calculations

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Thermodynamics in Earth and Planetary Sciences

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The quest for new functionality

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The New Face of Rhodium Alloys: Revealing Ordered Structures from First Principles

Levy, O., Chepulskii, R. V., Hart, G. L. W., & Stefano Curtarolo, A. (2009) · Journal of the American Chemical Society · doi:10.1021/ja908879y · Cited by 71 · see citing works

High-throughput prediction of finite-temperature properties using the quasi-harmonic approximation

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Challenges in Ceramic Science: A Report from the Workshop on Emerging Research Areas in Ceramic Science

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Guiding the experimental discovery of magnesium alloys

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AFLUX: The LUX materials search API for the AFLOW data repositories

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Frequency distribution and selection of space groups in inorganic crystal chemistry

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AFLOW-SYM: platform for the complete, automatic and self-consistent symmetry analysis of crystals

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Ordered phases in ruthenium binary alloys from high-throughput first-principles calculations

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A Procedure for the Clasification of Non-Organic Crystal Structures. I. Theoretical Background

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Structure maps for hcp metals from first-principles calculations

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Robust topological surface state in Kondo insulator SmB₆ thin films

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Evaluation of the tantalum-titanium phase diagram from ab-initio calculations

Barzilai, S., Toher, C., Stefano Curtarolo, A., & Levy, O. (2016) · Acta Materialia · doi:10.1016/j.actamat.2016.08.053 · Cited by 44 · see citing works

First-principles thermodynamics study of phase stability in inorganic halide perovskite solid solutions

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A high-throughput ab initio review of platinum-group alloy systemsPreprint

Hart, G. L. W., Stefano Curtarolo, A., Massalski, T. B., & Levy, O. (2013) · doi:10.48550/arxiv.1308.4357 · Cited by 31 · see citing works

Molybdenum-titanium phase diagram evaluated from ab initio calculations

Barzilai, S., Toher, C., Stefano Curtarolo, A., & Levy, O. (2017) · Physical Review Materials · doi:10.1103/physrevmaterials.1.023604 · Cited by 28 · see citing works

The tetragonal MBe12structure of silver, palladium, platinum and gold

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Prediction and Hydrogen Acceleration of Ordering in Iron-Vanadium Alloys

Bloch, J., Levy, O., Pejova, B., Jacob, J., Stefano Curtarolo, A., & Hjörvarsson, B. (2012) · Physical Review Letters · doi:10.1103/physrevlett.108.215503 · Cited by 18 · see citing works

Stable ordered structures of binary technetium alloys from first principles

Levy, O., Junkai Xue, A., Wang, S., Hart, G. L. W., & Stefano Curtarolo, A. (2012) · Physical Review B · doi:10.1103/physrevb.85.012201 · Cited by 17 · see citing works

The AFLOW Library of Crystallographic PrototypesPreprint

Mehl, M. J., Hicks, D., Toher, C., Levy, O., Hanson, R. M., Hart, G. L. W., & Stefano Curtarolo, A. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1607.02532 · Cited by 7 · see citing works

The Structure and Composition Statistics of 6A Binary and Ternary Crystalline Materials

Hever, A., Oses, C., Stefano Curtarolo, A., Levy, O., & Natan, A. (2017) · Inorganic Chemistry · doi:10.1021/acs.inorgchem.7b02462 · Cited by 7 · see citing works

The effect of lattice stability determination on the computational phase diagrams of intermetallic alloys

Barzilai, S., Toher, C., Stefano Curtarolo, A., & Levy, O. (2017) · Journal of Alloys and Compounds · doi:10.1016/j.jallcom.2017.08.263 · Cited by 7 · see citing works

An Innovative Approach to Balancing Chemical-Reaction Equations: A Simplified Matrix-Inversion Technique for Determining The Matrix Null SpacePreprint

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AFLOW-XTAL-MATCH: Automated method for quantifying the structural similarity of materials and identifying unique crystal prototypes

Hicks, D., Toher, C., Ford, D. C., Santo, C. D., Levy, O., Mehl, M. J., & Stefano Curtarolo, A. (2019) · Bulletin of the American Physical Society · Cited by 5 · see citing works

Supramolecular engineering with macromolecules: An alternative concept for block copolymers

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High-entropy high-hardness metal carbides discovered by entropy descriptors

Sarker, P., Harrington, T., Toher, C., Oses, C., Samiee, M., Maria, J., Brenner, D. W., Vecchio, K. S., & Stefano Curtarolo, A. (2018) · Nature Communications · doi:10.1038/s41467-018-07160-7 · Cited by 1,086 · see citing works

QSAR without borders

Muratov, E. N., Bajorath, J., Sheridan, R. P., Tetko, I. V., Filimonov, D., Poroikov, V., Oprea, T. I., Baskin, I. I., Varnek, A., Roitberg, A., et al. (2020) · Chemical Society Reviews · doi:10.1039/d0cs00098a · Cited by 913 · see citing works

Machine learning for alloys

Hart, G. L. W., Mueller, T., Toher, C., & Curtarolo, S. (2021) · Nature Reviews Materials · doi:10.1038/s41578-021-00340-w · Cited by 703 · see citing works

Materials design for hypersonics

Peters, A. B., Zhang, D., Chen, S. Y., Ott, C., Oses, C., Stefano Curtarolo, A., McCue, I., Pollock, T. M., & Prameela, S. E. (2024) · Nature Communications · doi:10.1038/s41467-024-46753-3 · Cited by 496 · see citing works

Heusler alloys: Past, properties, new alloys, and prospects

Tavares, S. S., Yang, K., & Meyers, M. A. (2022) · Progress in Materials Science · doi:10.1016/j.pmatsci.2022.101017 · Cited by 274 · see citing works

Disordered enthalpy–entropy descriptor for high-entropy ceramics discovery

Divilov, S., Eckert, H., Hicks, D., Oses, C., Toher, C., Friedrich, R., Esters, M., Mehl, M. J., Zettel, A. C., Lederer, Y., et al. (2024) · Nature · doi:10.1038/s41586-023-06786-y · Cited by 254 · see citing works

Part II: Experimental verification of computationally predicted preferential oxidation of refractory high entropy ultra-high temperature ceramics

Backman, L., Gild, J., Luo, J., & Opila, E. J. (2020) · Acta Materialia · doi:10.1016/j.actamat.2020.07.004 · Cited by 214 · see citing works

Data‐Driven Materials Innovation and Applications

Wang, Z., Sun, Z., Yin, H., Liu, X., Wang, J., Zhao, H., Pang, C. H., Wu, T., Li, S., Yin, Z., et al. (2022) · Advanced Materials · doi:10.1002/adma.202104113 · Cited by 193 · see citing works

Roadmap on Machine learning in electronic structure

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Crystal graph attention networks for the prediction of stable materials

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Unavoidable disorder and entropy in multi-component systems

Toher, C., Oses, C., Hicks, D., & Stefano Curtarolo, A. (2019) · npj Computational Materials · doi:10.1038/s41524-019-0206-z · Cited by 123 · see citing works

The AFLOW Library of Crystallographic Prototypes: Part 2

Hicks, D., Mehl, M. J., Gossett, E., Toher, C., Levy, O., Hanson, R. M., Hart, G. L. W., & Stefano Curtarolo, A. (2019) · Computational Materials Science · doi:10.1016/j.commatsci.2018.10.043 · Cited by 115 · see citing works

Emerging high-entropy compounds for electrochemical energy storage and conversion

Liu, D., Guo, P., Pan, H., & Wu, R. (2024) · Progress in Materials Science · doi:10.1016/j.pmatsci.2024.101300 · Cited by 114 · see citing works

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

Schmidt, J., Hoffmann, N., Wang, H., Borlido, P., Carriço, P. J. M. A., Cerqueira, T. F. T., Botti, S., & Marques, M. A. L. (2023) · Advanced Materials · doi:10.1002/adma.202210788 · Cited by 112 · see citing works

Advances in data‐assisted high‐throughput computations for material design

Xu, D., Zhang, Q., Huo, X., Wang, Y., & Yang, M. (2023) · Materials Genome Engineering Advances · doi:10.1002/mgea.11 · Cited by 115 · see citing works

Review of high-throughput computational design of Heusler alloys

Jiang, S., & Yang, K. (2021) · Journal of Alloys and Compounds · doi:10.1016/j.jallcom.2021.158854 · Cited by 84 · see citing works

Settling the matter of the role of vibrations in the stability of high-entropy carbides

Esters, M., Oses, C., Hicks, D., Mehl, M. J., Jahnátek, M., Hossain, M. D., Maria, J., Brenner, D. W., Toher, C., & Stefano Curtarolo, A. (2021) · Nature Communications · doi:10.1038/s41467-021-25979-5 · Cited by 76 · see citing works

Plasmonic high-entropy carbides

Calzolari, A., Oses, C., Toher, C., Esters, M., Campilongo, X., Stepanoff, S. P., Wolfe, D. E., & Stefano Curtarolo, A. (2022) · Nature Communications · doi:10.1038/s41467-022-33497-1 · Cited by 67 · see citing works

aflow.org: A web ecosystem of databases, software and tools

Esters, M., Oses, C., Divilov, S., Eckert, H., Friedrich, R., Hicks, D., Mehl, M. J., Rose, F., Smolyanyuk, A., Calzolari, A., et al. (2022) · Computational Materials Science · doi:10.1016/j.commatsci.2022.111808 · Cited by 65 · see citing works

Coordination corrected ab initio formation enthalpies

Friedrich, R., Usanmaz, D., Oses, C., Supka, A., Fornari, M., Nardelli, M. B., Toher, C., & Stefano Curtarolo, A. (2019) · npj Computational Materials · doi:10.1038/s41524-019-0192-1 · Cited by 59 · see citing works

Metastable materials discovery in the age of large-scale computation

Therrien, F., Jones, E. B., & Stevanović, V. (2021) · Applied Physics Reviews · doi:10.1063/5.0049453 · Cited by 52 · see citing works

A dataset of 175k stable and metastable materials calculated with the PBEsol and SCAN functionals

Schmidt, J., Wang, H., Cerqueira, T. F. T., Botti, S., & Marques, M. A. L. (2022) · Scientific Data · doi:10.1038/s41597-022-01177-w · Cited by 52 · see citing works

Data-driven design of inorganic materials with the Automatic Flow Framework for Materials Discovery

Oses, C., Toher, C., & Stefano Curtarolo, A. (2018) · MRS Bulletin · doi:10.1557/mrs.2018.207 · Cited by 50 · see citing works

Meta-GGA SCAN Functional in the Prediction of Ground State Properties of Magnetic Materials: Review of the Current State

Sokolovskiy, V. V., Baigutlin, D. R., Miroshkina, O. N., & Buchelnikov, V. D. (2023) · Metals · doi:10.3390/met13040728 · Cited by 48 · see citing works

High-throughput reaction engineering to assess the oxidation stability of MAX phases

Sauceda, D., Singh, P. K., Falkowski, A. R., Chen, Y., Doung, T., Vazquez, G., Radović, M., & Arróyave, R. (2021) · npj Computational Materials · doi:10.1038/s41524-020-00464-7 · Cited by 43 · see citing works

aflow++: A C++ framework for autonomous materials design

Oses, C., Esters, M., Hicks, D., Divilov, S., Eckert, H., Friedrich, R., Mehl, M. J., Smolyanyuk, A., Campilongo, X., van de Walle, A., et al. (2022) · Computational Materials Science · doi:10.1016/j.commatsci.2022.111889 · Cited by 41 · see citing works

Synthesis, structure and properties of MAB phase MoAlB ceramics produced by combination of SHS and HP techniques

Potanin, A. Y., Bashkirov, E. A., Pogozhev, Y. С., Rupasov, S. I., & Levashov, E. A. (2022) · Journal of the European Ceramic Society · doi:10.1016/j.jeurceramsoc.2022.07.038 · Cited by 38 · see citing works

Metallic glasses for biodegradable implants

Ford, D. C., Hicks, D., Oses, C., Toher, C., & Stefano Curtarolo, A. (2019) · Acta Materialia · doi:10.1016/j.actamat.2019.07.008 · Cited by 36 · see citing works

Targeting Productive Composition Space through Machine-Learning-Directed Inorganic Synthesis

Lotfi, S., Zhang, Z., Viswanathan, G., Fortenberry, K., Tehrani, A. M., & Brgoch, J. (2020) · Matter · doi:10.1016/j.matt.2020.05.002 · Cited by 36 · see citing works

Active Site Representation in First-Principles Microkinetic Models: Data-Enhanced Computational Screening for Improved Methanation Catalysts

Deimel, M., Reuter, K., & Andersen, M. (2020) · ACS Catalysis · doi:10.1021/acscatal.0c04045 · Cited by 35 · see citing works

Materials Informatics

Senderowitz, H., & Tropsha, A. (2018) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00927 · Cited by 31 · see citing works

Elastic energy of multi-component solid solutions and strain origins of phase stability in high-entropy alloys

Kamachali, R. D., & Wang, L. (2021) · Scripta Materialia · doi:10.1016/j.scriptamat.2021.114226 · Cited by 29 · see citing works

The AFLOW Fleet for Materials Discovery

Toher, C., Oses, C., Hicks, D., Gossett, E., Rose, F., Nath, P., Usanmaz, D., Ford, D. C., Perim, E., Calderón, C. E., et al. (2018) · Springer eBooks · doi:10.1007/978-3-319-42913-7_63-1 · Cited by 27 · see citing works

Theoretical investigation of the structural, elastic, electronic, and dielectric properties of alkali-metal-based bismuth ternary chalcogenides

Syam Kumar, R., Akande, A., El‐Mellouhi, F., Park, H., & Sanvito, S. (2020) · Physical Review Materials · doi:10.1103/physrevmaterials.4.075401 · Cited by 26 · see citing works

High entropy powering green energy: hydrogen, batteries, electronics, and catalysis

Qiu, G., Li, T., Xiao, X. R., Liu, Y., Niyogi, M., Cariaga, K., & Oses, C. (2025) · npj Computational Materials · doi:10.1038/s41524-025-01594-6 · Cited by 25 · see citing works

Design of Multifunctional Quinternary Metal-Halide Perovskite Compounds Based on Cation–Anion Co-Ordering

Huang, Y., Zhang, T., Wang, J., Xu, S.-G., Zhang, P., Chen, S., Yin, W., Zhang, X., & Wei, S. (2020) · Chemistry of Materials · doi:10.1021/acs.chemmater.0c00674 · Cited by 25 · see citing works

Low-temperature synthesis of single-phase refractory metal compound carbides

Wu, K., Wang, Y., Chou, K., & Zhang, G. (2021) · International Journal of Refractory Metals and Hard Materials · doi:10.1016/j.ijrmhm.2021.105567 · Cited by 25 · see citing works

Long‐Term Cycling Stability and Dendrite Suppression in Garnet‐Type Solid‐State Lithium Batteries via Plasma‐Induced Artificial SEI Layer Formation

Hao, B., Chen, W., Wu, J., Jiang, Z., Chen, X., & Jiang, Z. (2025) · Advanced Functional Materials · doi:10.1002/adfm.202502429 · Cited by 24 · see citing works

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

Engineering of strong and hard in-situ Al-Al3Ti nanocomposite via high-energy ball milling and spark plasma sintering

Vorotilo, S., Nepapushev, A. A., Moskovskikh, D. O., Buinevich, V. S., Трусов, Г. В., Kovalev, D. Y., Semenyuk, A., Stepanov, N., Vorotilo, K., Nalivaiko, A. Y., et al. (2021) · Journal of Alloys and Compounds · doi:10.1016/j.jallcom.2021.162676 · Cited by 24 · see citing works

Design of Fe2B-based ductile high temperature ceramics: First-principles calculations and experimental validation

Wang, G., Chong, X., Li, Z., Feng, J., & Jiang, Y. (2022) · Ceramics International · doi:10.1016/j.ceramint.2022.06.028 · Cited by 24 · see citing works

Crystalline or amorphous? A critical evaluation of phenomenological phase selection rules

Wieczerzak, K., Sharma, A. S., Hain, C., & Michler, J. (2023) · Materials & Design · doi:10.1016/j.matdes.2023.111994 · Cited by 24 · see citing works

High-Throughput Design of Interfacial Perpendicular Magnetic Anisotropy at Heusler/MgO Heterostructures

Jiang, S., Nazir, S., & Yang, K. (2022) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.1c20945 · Cited by 23 · see citing works

Super-hardening and localized plastic deformation behaviors in ZrB2 –TaВ2 ceramics

Vorotilo, S., Sidnov, K., Kurbatkina, V. V., Логинов, П. А., Patsera, E. I., Свиридова, Т. А., Lobova, T. A., Levashov, E. A., & Klechkovskaya, V. V. (2021) · Journal of Alloys and Compounds · doi:10.1016/j.jallcom.2021.163368 · Cited by 23 · see citing works

Electrochemical Pourbaix diagrams of Mg–Zn alloys from first-principles calculations and experimental thermodynamic data

Dong, X., Wei, B., Legut, D., Zhang, H., & Zhang, R. (2021) · Physical Chemistry Chemical Physics · doi:10.1039/d1cp02754a · Cited by 20 · see citing works

Tuning anionic components to control the phase stability and mechanical properties of High-Entropy carbonitrides

Li, Y., Ouyang, Z., Ding, Y., Liu, Y., Jin, N., & Ye, J. (2025) · Materials & Design · doi:10.1016/j.matdes.2025.113775 · Cited by 18 · see citing works

High-throughput design of perpendicular magnetic anisotropy at quaternary Heusler and MgO interfaces

Jiang, S., & Yang, K. (2023) · npj Computational Materials · doi:10.1038/s41524-023-01079-4 · Cited by 17 · see citing works

Automated coordination corrected enthalpies with AFLOW-CCE

Friedrich, R., Esters, M., Oses, C., Ki, S., Brenner, M. J., Hicks, D., Mehl, M. J., Toher, C., & Stefano Curtarolo, A. (2021) · Physical Review Materials · doi:10.1103/physrevmaterials.5.043803 · Cited by 16 · see citing works

A theoretical and experimental investigation on the SHS synthesis of (HfTiCN)-TiB2 high-entropy composite

Евсеев, Н. С., Matveev, A., Nikitin, P. Y., Abzaev, Y. A., & Zhukov, I. (2022) · Ceramics International · doi:10.1016/j.ceramint.2022.02.144 · Cited by 14 · see citing works

Machine-Learning-Assisted Construction of Ternary Convex Hull Diagrams

Rossignol, H., Minotakis, M., Cobelli, M., & Sanvito, S. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c01391 · Cited by 13 · see citing works

First-principles investigations of orthorhombic-cubic phase transition and its effect on thermoelectric properties in cobalt-based ternary alloys

Singh, S., Zeeshan, M., Singh, U. K., van den Brink, J., & Kandpal, H. C. (2019) · Journal of Physics Condensed Matter · doi:10.1088/1361-648x/ab4e71 · Cited by 13 · see citing works

Feature Space of XRD Patterns Constructed by an Autoencoder

Utimula, K., Yano, M., Kimoto, H., Hongo, K., Nakano, K., & Maezono, R. (2022) · Advanced Theory and Simulations · doi:10.1002/adts.202200613 · Cited by 13 · see citing works

Prediction of a Heusler alloy with switchable metal-to-half-metal behavior

Buchelnikov, V. D., Sokolovskiy, V. V., Miroshkina, O. N., Baigutlin, D. R., Zagrebin, M. A., Barbiellini, B., & Lähderanta, E. (2021) · Physical review. B./Physical review. B · doi:10.1103/physrevb.103.054414 · Cited by 13 · see citing works

AFLOW4: Heading Toward Disorder

Divilov, S., Eckert, H., Thiel, S. D., Griesemer, S. D., Friedrich, R., Anderson, N. H., Mehl, M. J., Hicks, D., Esters, M., Hotz, N., et al. (2025) · High Entropy Alloys & Materials · doi:10.1007/s44210-025-00058-2 · Cited by 12 · see citing works

First principles prediction of the Al-Li phase diagram including configurational and vibrational entropic contributions

Shao, W., Liu, S., & LLorca, J. (2022) · Computational Materials Science · doi:10.1016/j.commatsci.2022.111898 · Cited by 12 · see citing works

Experimental data management platform for data-driven investigation of combinatorial alloy thin films

Song, J., Jo, H., Kim, T., & Lee, D. (2023) · APL Materials · doi:10.1063/5.0162158 · Cited by 11 · see citing works

Physics in the Machine: Integrating Physical Knowledge in Autonomous Phase-Mapping

Kusne, A. G., McDannald, A., DeCost, B., Oses, C., Toher, C., Stefano Curtarolo, A., Mehta, A., & Takeuchi, I. (2022) · Frontiers in Physics · doi:10.3389/fphy.2022.815863 · Cited by 11 · see citing works

Lattice distortion-driven electronic engineering of high-entropy alloys for efficient polysulfide catalysis in LiS batteries

Zhang, L., Wang, Y., Lin, B.-W., Liang, R.-R., Dang, T.-H., & Zhao, Z. (2026) · Chemical Engineering Journal · doi:10.1016/j.cej.2026.174481 · Cited by 9 · see citing works

Enthalpy‐Driven Self‐Healing in Thin Metallic Films on Flexible Substrates

Trost, C. O. W., Lassnig, A., Kreiml, P., Jörg, T., Terziyska, V. L., Mitterer, C., & Cordill, M. J. (2024) · Advanced Materials · doi:10.1002/adma.202401007 · Cited by 10 · see citing works

Probabilistic prediction of material stability: integrating convex hulls into active learning

Novick, A. C., Cai, D., Nguyen, Q., Garnett, R., Adams, R. P., & Toberer, E. S. (2024) · Materials Horizons · doi:10.1039/d4mh00432a · Cited by 10 · see citing works

Nucleation and growth of the Fe2AlB2 MAB phase in the combustion wave of mechanically activated Fe–Al–B reaction mixtures

Potanin, A. Y., Bashkirov, E. A., Levashov, E. A., Логинов, П. А., Berezin, M. A., & Kovalev, D. Y. (2023) · Ceramics International · doi:10.1016/j.ceramint.2023.09.113 · Cited by 10 · see citing works

Invited Article: The oxidation kinetics and mechanisms observed during ultra-high temperature oxidation of (HfZrTiTaNb)C and (HfZrTiTaNb)B2

Backman, L., Hunter, B. M., Weaver, M. L., & Opila, E. J. (2024) · Journal of Applied Physics · doi:10.1063/5.0206227 · Cited by 9 · see citing works

Evolutionary search for new compounds in the Ti–Si system

Poletaev, D. O., Aksyonov, D. A., & Геннадьевич, Л. А. (2020) · Calphad · doi:10.1016/j.calphad.2020.102201 · Cited by 9 · see citing works

A novel route to fibers with volatile crystalline semiconductor cores part 2: Selenides and phosphides

Zaengle, T., Martínez, E., Hawkins, T. W., McMillen, C. D., & Ballato, J. M. (2023) · Optical Materials · doi:10.1016/j.optmat.2023.114388 · Cited by 9 · see citing works

Exploring the Electronic Structure of New Doped Salt Hydrates, Mg1– x Ca x Cl2· n H2O, for Thermochemical Energy Storage

Heijmans, K., Trancă, I., Gaastra-Nedea, S. V., & Smeulders, D. (2020) · The Journal of Physical Chemistry C · doi:10.1021/acs.jpcc.0c05799 · Cited by 9 · see citing works

Thermodynamic assessment of the Ga–La and Ga–Pr systems supported by ab-initio calculations

Boudraa, S., Djaballah, Y., Mansouri, Y., & Belgacem-Bouzida, A. (2022) · Calphad · doi:10.1016/j.calphad.2021.102387 · Cited by 7 · see citing works

GMSAC : A New Software for Searching the Global‐Minimum Structures of Solids and Alloys by the Improved Genetic Algorithm and Embedded Atom Method

Zhang, Y. (2026) · Journal of Computational Chemistry · doi:10.1002/jcc.70315 · Cited by 6 · see citing works

Design of a Stable Heusler Alloy with Switchable Metal‐to‐Half‐Metal Transition at Finite Temperature

Buchelnikov, V. D., Sokolovskiy, V. V., Miroshkina, O. N., Baigutlin, D. R., Zagrebin, M. A., Barbiellini, B., Singh, B., Bansil, A., & Lähderanta, E. (2021) · Advanced Theory and Simulations · doi:10.1002/adts.202100311 · Cited by 6 · see citing works

Modeling Single-Crystal Battery Materials: From Fundamental Understanding to Performance Evaluation

Yuan, S., Weitzner, S. E., Jeong, W., Zhang, S., Wang, B., Feng, L., Kaufman, J. L., Kim, K., Qi, Y., & Wan, L. F. (2026) · Chemical Reviews · doi:10.1021/acs.chemrev.5c00360 · Cited by 5 · see citing works

The AFLOW Fleet for Materials Discovery

Toher, C., Oses, C., Hicks, D., Gossett, E., Rose, F., Nath, P., Usanmaz, D., Ford, D. C., Perim, E., Calderón, C. E., et al. (2020) · Springer eBooks · doi:10.1007/978-3-319-44677-6_63 · Cited by 6 · see citing works

Machine-learning surrogate model for accelerating the search of stable ternary alloys

Minotakis, M., Rossignol, H., Cobelli, M., & Sanvito, S. (2023) · Physical Review Materials · doi:10.1103/physrevmaterials.7.093802 · Cited by 5 · see citing works

High temperature oxidation behavior of disordered (Ti0.5Zr0.5)2AlC MAX phase via a Machine Learning-Augmented DFT approach

Singh, P. K., Sauceda, D., & Arróyave, R. (2021) · Materials Letters X · doi:10.1016/j.mlblux.2021.100062 · Cited by 5 · see citing works

AI-driven new materials development and data standardization

Zhang, L., Liu, M., Yuan, H., & Wang, H. (2025) · Chinese Science Bulletin (Chinese Version) · doi:10.1360/csb-2025-0397 · Cited by 4 · see citing works

High-performance photocatalytic water splitting for hydrogen production, amplified solar cell efficiency, and energy harvesting in X@Na-doped Na2ZrTeO6 (X = K, Rb, and Cs)

Saeed, U., Gohar, O., Alburaih, H. A., & Nazir, S. (2026) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2026.155842 · Cited by 4 · see citing works

Spin and current transport in the robust half-metallic magnet c-CoFeGe

Chaudhary, V., Singh, S., Gujjar, D., Nautiyal, T., Maitra, T., van den Brink, J., & Kandpal, H. C. (2023) · Journal of Physics Condensed Matter · doi:10.1088/1361-648x/accc68 · Cited by 4 · see citing works

Thermodynamic modelling of the Al–Ni–V system

Hallstedt, B., & Noori, M. (2025) · Calphad · doi:10.1016/j.calphad.2025.102887 · Cited by 3 · see citing works

The search for high-entropy fuel-cell catalysts using disorder descriptors

Han, G., Li, T., Xu, X., Lee, J., Qiu, G., Sequeira, S. G., Ajith, A., & Oses, C. (2025) · Nano Futures · doi:10.1088/2399-1984/ae19b0 · Cited by 3 · see citing works

Electrochemical stability of biodegradable Zn–Cu alloys through machine-learning accelerated high-throughput discovery

Luo, K., Liu, Z., Yu, R., Xu, T., Legut, D., Yin, X., & Zhang, R. (2024) · Physical Chemistry Chemical Physics · doi:10.1039/d4cp02307b · Cited by 3 · see citing works

Fast ab initio design of high-entropy magnetic materials

Bista, D., Beeson, W. B., Sengupta, T., Jackson, J., Khanna, S. N., Liu, K., & Yin, G. (2025) · Physical Review Materials · doi:10.1103/physrevmaterials.9.l031401 · Cited by 3 · see citing works

Resolving the Oxygen Species on Ozone Activated AgAu Alloy Catalysts for Oxidative Methanol Coupling

Pu, T., Jehng, J., Setiawan, A., Lis, B. M., Ford, M. E., Rangarajan, S., & Wachs, I. E. (2022) · The Journal of Physical Chemistry C · doi:10.1021/acs.jpcc.2c03769 · Cited by 3 · see citing works

Structure and Phase Composition of Natural Magnesite in 1173–6500 K Temperature Range

Валерьевич, Ш. В., Abzaev, Y. A., Геннадьевич, В. О., Скрипникова, Н. К., & Клопотов, А. А. (2022) · Russian Physics Journal · doi:10.1007/s11182-022-02743-w · Cited by 3 · see citing works

Thermodynamic Study of Er-Bi and Er-Te Systems by Combination of First-Principles Calculations and the CALPHAD Method

Bennoui, S., Djaballah, Y., Vassiliev, V. P., & Belgacem-Bouzida, A. (2022) · Journal of Phase Equilibria and Diffusion · doi:10.1007/s11669-022-00947-8 · Cited by 3 · see citing works

Na2Mn(CO3)2: A carbonate based prototype cathode material for Na-ion batteries with high rate capability — An ab-initio study

Sudarsanan, V., Augustine, A. M., & Ravindran, P. (2022) · Electrochimica Acta · doi:10.1016/j.electacta.2022.141687 · Cited by 3 · see citing works

The AFLOW Fleet for Materials Discovery

Toher, C., Oses, C., Hicks, D., Gossett, E., Rose, F., Nath, P., Usanmaz, D., Ford, D. C., Perim, E., Calderón, C. E., et al. (2019) · Springer eBooks · doi:10.1007/978-3-319-42913-7_63-2 · Cited by 4 · see citing works

Excess Thermodynamic Properties of Solutions in Ln-Ru (Ln = Nd, Gd, Dy) Binary Systems Based on Quadratic, Exponential and Combined Models Supported by Ab-Initio Calculations

Kardellass, S., Vassiliev, V. P., Mahdouk, K., Laaroussi, N., & Selhaoui, N. (2023) · Journal of Phase Equilibria and Diffusion · doi:10.1007/s11669-022-01023-x · Cited by 2 · see citing works

AFLOW-XtalFinder: a reliable choice to identify crystalline prototypes

Hicks, D., Toher, C., Ford, D. C., Rose, F., Santo, C. D., Levy, O., Mehl, M. J., & Stefano Curtarolo, A. (2021) · npj Computational Materials · doi:10.1038/s41524-020-00483-4 · Cited by 54 · see citing works

Thermodynamics of the metallic fission products: An assessment of the Mo-Pd-Rh-Ru-Tc system

Croze, T., Gossé, S., Crivello, J., Joubert, J., BONNET, C., & Tougait, O. (2025) · Journal of Nuclear Materials · doi:10.1016/j.jnucmat.2025.155970 · Cited by 1 · see citing works

AI meets high-entropy alloys: From classification and regression to explainable models and predictive discovery

Gorsse, S., & Lee, W. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-33542-6.00006-4 · Cited by 2 · see citing works

High-Throughput Prediction of Exfoliable Non-van der Waals Materials from a Universal Potential

Barnowsky, T., Timm, C., & Friedrich, R. (2026) · Nature Communications · doi:10.1038/s41467-026-76806-8 · Cited by 1 · see citing works

Thermodynamic Modeling of the (Ce and Sm)-Ru Binary Systems Based on Linear, Exponential, and Combined Models Aided by Ab-Initio Calculations

Kardellass, S., Moya, C. C., & Vassiliev, V. P. (2023) · Journal of Phase Equilibria and Diffusion · doi:10.1007/s11669-023-01043-1 · Cited by 1 · see citing works

Electronic and thermoelectric properties of KMgP: A meta-GGA functional within Maximally-Localized Wannier functions basis set Study

Mouchou, S., Toual, Y., Azouaoui, A., Rezzouk, A., Hormatallah, A., & Benzakour, N. (2025) · Journal of Solid State Chemistry · doi:10.1016/j.jssc.2025.125393 · Cited by 1 · see citing works

Self-propagating high-temperature synthesis of MoAlB boride ceramics based on MAB-phase

Potanin, A. Y., Bashkirov, E. A., Pogozhev, Y. С., Kovalev, D. Y., Кочетов, Н. А., Логинов, П. А., & Levashov, E. A. (2022) · Powder Metallurgy аnd Functional Coatings · doi:10.17073/1997-308x-2022-2-38-51 · Cited by 1 · see citing works

Machine learning, phase stability, and disorder with the Automatic Flow Framework for Materials DiscoveryPreprint

Oses, C. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.08464

Coordination corrected ab initio formation enthalpiesPreprint

Friedrich, R., Usanmaz, D., Oses, C., Supka, A., Fornari, M., Nardelli, M. B., Toher, C., & Stefano Curtarolo, A. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.08952

Metallic glasses for biodegradable implantsPreprint

Ford, D. C., Hicks, D., Oses, C., Toher, C., & Stefano Curtarolo, A. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.00485

Perovskite-based Oxidative Dehydrogenation of Ethane to Ethylene using a Solid Oxide Cell Configuration for Process IntensificationPreprint

Daramola, D. A., Velraj, S., & Trembly, J. (2022) · ChemRxiv · doi:10.26434/chemrxiv-2022-vqgr1

STRUCTURAL PARAMETERS OF SiO2 AT DIFFERENT TEMPERATURE FACTORS

Валерьевич, Ш. В., Геннадьевич, В. О., & Abzaev, Y. A. (2022) · Physical Mesomechanics of Materials. Physical Principles of Multi-Layer Structure Forming and Mechanisms of Non-Linear Behavior: Conference Proceedings · doi:10.25205/978-5-4437-1353-3-315

Thermodynamics, mechanical, electronic, magnetic aspects, and favorable energy harvesting response in LaCu3Fe2TiSbO12: Strain deformation

Farooq, A., Felemban, B. F., Ali, H. T., & Nazir, S. (2026) · Physica B Condensed Matter · doi:10.1016/j.physb.2026.418711

Computing phase diagrams using a convex hull algorithm

Dullemond, C. P., & Young, E. D. (2026) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202659967

Thermodynamics, mechanical, electronic, and magnetic aspects of the Mn @ Ti-doped La 2 CuTiO 6 semiconductor

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Existence and stability of chemical compounds

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Automated procedure to determine the thermodynamic stability of a material and the range of chemical potentials necessary for its formation relative to competing phases and compounds

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Review of computational approaches to predict the thermodynamic stability of inorganic solids

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Date: Approved:

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Computational Materials Genome Initiative by High-Throughput Approaches

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