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RESEARCH PAPER · Elsevier eBooks · 2022

Data-driven materials discovery and synthesis using machine learning methods

Sterling G. Baird, Marianne Liu, Hasan Muhammad Sayeed, Taylor D. Sparks

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

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Data-driven materials discovery and synthesis using machine learning methodsOriginalArtificial Intelligence Approaches for Energetic Materials by Design: State of the Art, Challenges, and Future Directions45Investigating representation schemes for surrogate modeling of High Entropy Alloys8NLP meets Materials Science: Quantifying the presentation of materials data in scientific literature1Uncertainty-Aware Surrogate Modeling of High-Throughput DFT Data for Rapid Materials Screening0NLP meets materials science: Quantifying the presentation of materials data in literature12Accelerating Dental Adhesive Innovations Through Active Learning and Bayesian Optimization1Accelerating the Discovery of New, Single Phase High Entropy Ceramics via Active Learning11Machine Learning Algorithms for Applications in Materials Science II0Overcoming sparse datasets with multi-task learning as applied to high entropy alloys3Stabilization of ambient pressure rocksalt crystal structure and high critical field superconductivity in ReC via Mo and W substitution0AI Driven Discovery and Design of Smart Materials0SEM images data set development of ML models to predict hydrogen embrittlement in welded 316 L/304 L stainless steels1Fast and Interpretable Machine Learning Modeling of Atmospheric Molecular Clusters1Spatial Prediction of Electronic Wavefunctions from Reciprocal Lattices: Visualization of Electronic Properties of 2D Materials Using Deep Convolutional Neural Networks2

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  1. Artificial Intelligence Approaches for Energetic Materials by Design: State of the Art, Challenges, and Future Directions

    Propellants Explosives Pyrotechnics · 2023-02-18

    Joseph B. Choi, Phong C. H. Nguyen, Oishik Sen, Holavanahalli S. Udaykumar, Stephen Baek

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  2. Investigating representation schemes for surrogate modeling of High Entropy Alloys

    Computational Materials Science · 2023-09-05

    Arindam Debnath, Wesley F. Reinhart

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  3. NLP meets Materials Science: Quantifying the presentation of materials data in scientific literature

    ChemRxiv · 2023-12-28

    Hasan Muhammad Sayeed, Wade Smallwood, Sterling G. Baird, Taylor D. Sparks

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  4. 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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  5. NLP meets materials science: Quantifying the presentation of materials data in literature

    Matter · 2024-03-01

    Hasan Muhammad Sayeed, Wade Smallwood, Sterling G. Baird, Taylor D. Sparks

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  6. Accelerating Dental Adhesive Innovations Through Active Learning and Bayesian Optimization

    ChemRxiv · 2024-05-17

    Ramsey Issa, Robert Sorenson, Taylor D. Sparks

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  7. Accelerating the Discovery of New, Single Phase High Entropy Ceramics via Active Learning

    Chemistry of Materials · 2024-11-05

    Calen J. Leverant, Jacob Harvey

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  8. Machine Learning Algorithms for Applications in Materials Science II

    Challenges and advances in computational chemistry and physics · 2025-01-01

    Ayushi Ayushi, Neeraj Kumar Tiwari, Terry-Elinor R. Reid, Nikita Basant

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  9. Overcoming sparse datasets with multi-task learning as applied to high entropy alloys

    Machine Learning Science and Technology · 2025-02-12

    Arindam Debnath, Wesley F. Reinhart

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  10. Stabilization of ambient pressure rocksalt crystal structure and high critical field superconductivity in ReC via Mo and W substitution

    Superconductor Science and Technology · 2025-02-13

    P. K. Meena, Sonika Jangid, R. K. Kushwaha, P Manna, Shashank Srivastava, S. Sharma, P. Mishra, Ravi Prakash Singh

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  11. AI Driven Discovery and Design of Smart Materials

    Journal of Mines Metals and Fuels · 2025-10-09

    Mahesh Ashok Bote, Naeem Shaikh, Shruti Pandit, A. C. Bhagali, Azhar Mulla

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  12. SEM images data set development of ML models to predict hydrogen embrittlement in welded 316 L/304 L stainless steels

    Manufacturing Letters · 2026-01-10

    Arivazhagan Anbalagan, John Edward Graves, Oliver James Curnick, Danijela Rostohar, Margret Anouncia Savarimuthu, M. Anthony Xavior, Rajkumar Soundarapandiyan, Jeyapandiarajan Paulchamy, Mythili Thirugnanam

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  13. Fast and Interpretable Machine Learning Modeling of Atmospheric Molecular Clusters

    The Journal of Physical Chemistry A · 2026-01-15

    Lauri Seppäläinen, Jakub Kubečka, Jonas Elm, Kai Puolamäki

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  14. Spatial Prediction of Electronic Wavefunctions from Reciprocal Lattices: Visualization of Electronic Properties of 2D Materials Using Deep Convolutional Neural Networks

    AI Materials · 2026-02-13

    Rubén Guerrero‐Rivera, Norma A. García-Vidaña, Francisco J. Godínez-Garcia, Zhipeng Wang, Morinobu Endo, Josué Ortiz‐Medina

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