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RESEARCH PAPER · Digital Discovery · 2022

DiSCoVeR: a materials discovery screening tool for high performance, unique chemical compositions

Sterling G. Baird, Tran Diep, Taylor D. Sparks

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28 direct citing papers in this saved network. Explore the next layer with second-order citations.

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DiSCoVeR: a materials discovery screening tool for high performance, unique chemical compositionsOriginalLatent Space Collapse in Materials Representation Learning: A Systems-Level Conceptual Analysis0Representation Learning in Materials Science: Architectures, Data Modalities, and Discovery Applications0High-dimensional Bayesian optimization of 23 hyperparameters over 100 iterations for an attention-based network to predict materials property: A case study on CrabNet using Ax platform and SAASBO33Serendipity based recommender system for perovskites material discovery: balancing exploration and exploitation across multiple models7Serendipity based recommender system for perovskites material discovery: balancing exploration and exploitation across multiple models5Into the Unknown: How Computation Can Help Explore Uncharted Material Space65Computational and Data-Driven Materials Engineering: High-Throughput Computational Screening Platforms, Workflows, and Discovery Outcomes0Quantifying the performance of machine learning models in materials discovery47The Liverpool materials discovery server: a suite of computational tools for the collaborative discovery of materials2Tales from Sabbatical II: During your stay1ET-AL: Entropy-targeted active learning for bias mitigation in materials data30Discovering Chemically Novel, High-TemperatureSuperconductors10Curated materials data of hybrid perovskites: approaches and potential usage3Closed-loop superconducting materials discovery34Not as simple as we thought: a rigorous examination of data aggregation in materials informatics13A human-in-the-loop approach for visual clustering of overlapping materials science data7Uncertainty-Aware Surrogate Modeling of High-Throughput DFT Data for Rapid Materials Screening0What Does “Sparsity” Mean in High-Dimensional Materials Design? A Boundary Problem for Active Learning0The master key: structural science in unlocking functional materials advancements5Optimizing Methane Uptake on N/O Functionalized Graphene via DFT, Machine Learning, and Uniform Manifold Approximation and Projection (UMAP) Techniques10Machine learning prediction of materials properties from chemical composition: Status and prospects29Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials4Mutual information informed novelty estimation of materials along chemical and structural axes1A materials map integrating experimental and computational data via graph-based machine learning for enhanced materials discovery4Enhancing Flame Retardancy in Polypropylene Composites: A Bayesian Optimization Approach0Out-of-distribution machine learning for materials discovery: Challenges and opportunities5Machine Learning of Band Gaps and Thermodynamic Stability in A2BB′X6 (X = F, Cl, Br, I) Double Perovskites with Uncertainty-Aware Multi-Objective Screening0Application of UMAP and DBSCAN algorithms to identify critical factors in extending MgO-C refractories lifetime in a steel ladle0

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  1. Latent Space Collapse in Materials Representation Learning: A Systems-Level Conceptual Analysis

    Journal of Computational and Data-Driven Materials Engineering · 2022-01-01

    Hiroshi Nakamura, Yuta Kato

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  2. Representation Learning in Materials Science: Architectures, Data Modalities, and Discovery Applications

    Journal of Computational and Data-Driven Materials Engineering · 2022-01-01

    Claire Martin, Julien Martin, Sophie Bernard

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  3. High-dimensional Bayesian optimization of 23 hyperparameters over 100 iterations for an attention-based network to predict materials property: A case study on CrabNet using Ax platform and SAASBO

    Computational Materials Science · 2022-05-19

    Sterling G. Baird, Marianne Liu, Taylor D. Sparks

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  4. Serendipity based recommender system for perovskites material discovery: balancing exploration and exploitation across multiple models

    ChemRxiv · 2022-07-19

    Venkateswaran Shekar, Vincent Yu, Benjamin J. Garcia, David Benjamin Gordon, Gemma E. Moran, David M. Blei, Loı̈c M. Roch, Alberto García-Durán, Mansoor Ani Najeeb, Margaret Zeile, Philip W. Nega, Zhi Li, Mina A. Kim, Emory M. Chan, Alexander J. Norquist, Sorelle A. Friedler, Joshua Schrier

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  5. Serendipity based recommender system for perovskites material discovery: balancing exploration and exploitation across multiple models

    ChemRxiv · 2022-07-20

    Venkateswaran Shekar, Vincent Yu, Benjamin J. Garcia, David Benjamin Gordon, Gemma E. Moran, David M. Blei, Loı̈c M. Roch, Alberto García-Durán, Mansoor Ani Najeeb, Margaret Zeile, Philip W. Nega, Zhi Li, Mina A. Kim, Emory M. Chan, Alexander J. Norquist, Sorelle A. Friedler, Joshua Schrier

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  6. Into the Unknown: How Computation Can Help Explore Uncharted Material Space

    Journal of the American Chemical Society · 2022-10-07

    Austin M. Mroz, Víctor Posligua, Andrew Tarzia, Emma Helen Wolpert, Kim E. Jelfs

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  7. Computational and Data-Driven Materials Engineering: High-Throughput Computational Screening Platforms, Workflows, and Discovery Outcomes

    Journal of Computational and Data-Driven Materials Engineering · 2023-01-01

    Hiroshi Nakamura, Yuta Kato

    Read original paper ↗ ·

  8. Quantifying the performance of machine learning models in materials discovery

    Digital Discovery · 2023-01-01

    Christopher K. H. Borg, Eric S. Muckley, Clara M. Nyby, James E. Saal, Logan T. Ward, Apurva Mehta, Bryce Meredig

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  9. The Liverpool materials discovery server: a suite of computational tools for the collaborative discovery of materials

    Digital Discovery · 2023-01-01

    Samantha Durdy, Cameron J. Hargreaves, Mark Dennison, Benjamin Wagg, Michael Moran, Jon A. Newnham, Michael W. Gaultois, Matthew J. Rosseinsky, Matthew Stephen Dyer

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  10. Tales from Sabbatical II: During your stay

    Matter · 2023-03-01

    Taylor D. Sparks

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  11. ET-AL: Entropy-targeted active learning for bias mitigation in materials data

    Applied Physics Reviews · 2023-04-10

    James M. Rondinelli, Wei Chen

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  12. Discovering Chemically Novel, High-TemperatureSuperconductors

    Computational Materials Science · 2023-06-27

    Colton C. Seegmiller, Sterling G. Baird, Hasan Muhammad Sayeed, Taylor D. Sparks

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  13. Curated materials data of hybrid perovskites: approaches and potential usage

    Trends in Chemistry · 2023-09-15

    Rayan Chakraborty, Volker Blüm

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  14. Closed-loop superconducting materials discovery

    npj Computational Materials · 2023-10-05

    Elizabeth A. Pogue, Alexander New, K. McElroy, Nam Q. Le, Michael J. Pekala, Ian McCue, Edwin B. Gienger, Janna Domenico, Elizabeth Hedrick, Tyrel M. McQueen, Brandon Wilfong, Christine Piatko, Christopher R. Ratto, Andrew M. Lennon, Christine Chung, Timothy J. Montalbano, Gregory Bassen, Christopher D. Stiles

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  15. Not as simple as we thought: a rigorous examination of data aggregation in materials informatics

    Digital Discovery · 2023-12-28

    Federico Ottomano, Giovanni de Felice, Vladimir V. Gusev, Taylor D. Sparks

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  16. A human-in-the-loop approach for visual clustering of overlapping materials science data

    Digital Discovery · 2024-01-01

    Satyanarayana Bonakala, Michael J Marie Aupetit, Halima Bensmail, Fedwa El‐Mellouhi

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  17. Uncertainty-Aware Surrogate Modeling of High-Throughput DFT Data for Rapid Materials Screening

    Journal of Artificial Intelligence for Materials Science · 2024-01-01

    Daniel Fischer, Laura Meier, Thomas Braun

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  18. What Does “Sparsity” Mean in High-Dimensional Materials Design? A Boundary Problem for Active Learning

    Journal of Computational and Data-Driven Materials Engineering · 2024-01-01

    Luis Herrera, Daniela Rojas, Andres Castro

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  19. The master key: structural science in unlocking functional materials advancements

    Journal of Applied Crystallography · 2024-05-24

    S. Suárez

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  20. Optimizing Methane Uptake on N/O Functionalized Graphene via DFT, Machine Learning, and Uniform Manifold Approximation and Projection (UMAP) Techniques

    Industrial & Engineering Chemistry Research · 2024-10-25

    Mohammad Rahimi, Amir Mehrpanah, Parastoo Mouchani, Ehsan Rahimi, Shakirudeen A. Salaudeen

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  21. Machine learning prediction of materials properties from chemical composition: Status and prospects

    Chemical Physics Reviews · 2024-12-01

    Mohammed Alghadeer, Nufida D. Aisyah, Mahmoud Hezam, Saad M. Alqahtani, Ahmer A.B. Baloch, Fahhad H. Alharbi

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  22. Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials

    Digital Discovery · 2025-01-01

    Federico Ottomano, John Yannis Goulermas, Vladimir V. Gusev, Rahul Savani, Michael W. Gaultois, Troy D. Manning, Hai Lin, Teresa Partida Manzanera, Emma Poole, Matthew Stephen Dyer, John B. Claridge, Jonathan Alaria, Luke M. Daniels, Su Varma, David Rimmer, Kevin D. Sanderson, Matthew J. Rosseinsky

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  23. Mutual information informed novelty estimation of materials along chemical and structural axes

    Digital Discovery · 2025-01-01

    Andrew Falkowski, Taylor D. Sparks

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  24. A materials map integrating experimental and computational data via graph-based machine learning for enhanced materials discovery

    APL Machine Learning · 2025-07-28

    Yusuke Hashimoto, Xue Jia, Hao Li, Takaaki Tomai

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  25. Enhancing Flame Retardancy in Polypropylene Composites: A Bayesian Optimization Approach

    Fire · 2025-11-17

    Eric Verret, Sophie Duquesne, Anthony Collin, Martin Stievenard

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  26. Out-of-distribution machine learning for materials discovery: Challenges and opportunities

    Chemical Physics Reviews · 2026-03-01

    Miguel Tenorio, Md. Habibur Rahman, Arun Mannodi‐Kanakkithodi, James Chapman

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  27. Machine Learning of Band Gaps and Thermodynamic Stability in A2BB′X6 (X = F, Cl, Br, I) Double Perovskites with Uncertainty-Aware Multi-Objective Screening

    Nanomaterials · 2026-09-26

    Qilin Wei, Haiyan Zhang, Chengyu Peng, Ye Tian, Yan Zhang, Yongmeng Yang, Menghong Liu, Nianxi Xu

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  28. Application of UMAP and DBSCAN algorithms to identify critical factors in extending MgO-C refractories lifetime in a steel ladle

    Ceramics International · 2026-10-01

    Wiesław Zelik, Sebastian Sado, Ilona Jastrzębska

    Read original paper ↗ ·
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