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RESEARCH PAPER · npj Computational Materials · 2023

Element selection for functional materials discovery by integrated machine learning of elemental contributions to properties

Andrij Vasylenko, Dmytro Antypov, Vladimir V. Gusev, Michael W. Gaultois, Matthew Stephen Dyer, Matthew J. Rosseinsky

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Open in your workspace →Saved 2026-10-14 · complete coverage
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Element selection for functional materials discovery by integrated machine learning of elemental contributions to propertiesOriginalAlgorithmic Settling Time: A Conceptual Framework for When Materials AI Outputs Stabilize0Inferring energy–composition relationships with Bayesian optimization enhances exploration of inorganic materials6Automated Crystallization Monitoring in Material Development using Computer Vision and Neuronal Networks2Embedding human knowledge in material screening pipeline as filters to identify novel synthesizable inorganic materials4Confluence of electronic structure calculations (DFT) and machine learning (ML) for lithium and sodium-ion batteries: a theoretical perspective8Multi-omics and advance technologies in biodegradation of emerging contaminants and eco-estrogens in environmental waste34Digital features of chemical elements extracted from local geometries in crystal structures2Convolutional neural network-based prediction of hardness in bulk metallic glasses with small data3Synergistic Machine Learning Guided Discovery of ABa 3 (BSe 3 ) 2 X (A = Rb, Cs; X = Cl, Br, I): A Promising Family as Property‐Balanced IR Functional Materials10Topology Augmented with Geometry in the Assembly of Structural Databases: Kagome Intermetallics3A Curated Dataset of Tensile Properties for Cantor-Type Alloys0The synergy of geometric tolerance factor and machine learning in discovering stable materials0Anomaly-Detection-Driven Screening of Thermodynamic Stability from Composition Descriptors Alone0Machine learning-driven prediction of halogen-tuned multifunctional properties in CsBX3 halide perovskites1A curated dataset of tensile properties for cantor-type alloys0

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  1. Algorithmic Settling Time: A Conceptual Framework for When Materials AI Outputs Stabilize

    Journal of Artificial Intelligence for Materials Science · 2023-01-01

    Maria Gonzalez, Javier Ruiz, Lucia Torres, Elena Ruiz

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  2. Inferring energy–composition relationships with Bayesian optimization enhances exploration of inorganic materials

    The Journal of Chemical Physics · 2024-02-07

    Andrij Vasylenko, Benjamin M. Asher, Christopher M. Collins, Michael W. Gaultois, George R. Darling, Matthew Stephen Dyer, Matthew J. Rosseinsky

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  3. Automated Crystallization Monitoring in Material Development using Computer Vision and Neuronal Networks

    Chemie Ingenieur Technik · 2024-05-08

    Simon‐Johannes Burgdorf, Thomas Roddelkopf, Kerstin Thurow

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  4. Embedding human knowledge in material screening pipeline as filters to identify novel synthesizable inorganic materials

    Faraday Discussions · 2024-07-15

    Basita Das, Kangyu Ji, Fang Sheng, Kyle M. McCall, Tonio Buonassisi

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  5. Confluence of electronic structure calculations (DFT) and machine learning (ML) for lithium and sodium-ion batteries: a theoretical perspective

    Engineering Research Express · 2024-08-16

    Henu Sharma, Vinay Katari, Kisor Kumar Sahu, Anjali Singh

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  6. Multi-omics and advance technologies in biodegradation of emerging contaminants and eco-estrogens in environmental waste

    Total Environment Advances · 2024-08-16

    Manan Kaur Ghai, Annu Khatri, Krishan Kumar, I. S. Thakur

    Read original paper ↗ ·

  7. Digital features of chemical elements extracted from local geometries in crystal structures

    Digital Discovery · 2025-01-01

    Andrij Vasylenko, Dmytro Antypov, Sven Schewe, Luke M. Daniels, John B. Claridge, Matthew Stephen Dyer, Matthew J. Rosseinsky

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  8. Convolutional neural network-based prediction of hardness in bulk metallic glasses with small data

    Journal of Non-Crystalline Solids · 2025-02-19

    Chunghee Nam

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  9. Synergistic Machine Learning Guided Discovery of ABa 3 (BSe 3 ) 2 X (A = Rb, Cs; X = Cl, Br, I): A Promising Family as Property‐Balanced IR Functional Materials

    Advanced Science · 2025-04-26

    Yihan Yun, Mengfan Wu, Zhihua Yang, Guangmao Li, Shilie Pan

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  10. Topology Augmented with Geometry in the Assembly of Structural Databases: Kagome Intermetallics

    Advanced Science · 2025-07-04

    Nataliya L. Gulay, Dongsheng Wen, Joshua E. Griffiths, Judith Clymo, Luke M. Daniels, Jonathan Alaria, Matthew Stephen Dyer, John B. Claridge, Matthew J. Rosseinsky

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  11. A Curated Dataset of Tensile Properties for Cantor-Type Alloys

    SSRN Electronic Journal · 2026-01-01

    kailai tian

    Read original paper ↗ ·

  12. The synergy of geometric tolerance factor and machine learning in discovering stable materials

    Journal of Materials Informatics · 2026-01-12

    Zhilong Wang, Fengqi You

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  13. Anomaly-Detection-Driven Screening of Thermodynamic Stability from Composition Descriptors Alone

    The Journal of Physical Chemistry Letters · 2026-02-10

    Keisuke Makino, Yudai Yamaguchi, Naoto Tanibata, Hayami Takeda, RYO KOBAYASHI, Masayuki Karasuyama, Masanobu Nakayama

    Read original paper ↗ ·

  14. Machine learning-driven prediction of halogen-tuned multifunctional properties in CsBX3 halide perovskites

    Physica B Condensed Matter · 2026-07-08

    Muhammad Wajdan Jamal, A Abdul Ali, Refka Ghodhbani, Ramesh Sharma, Nikhil Shrivastav, Afef Dhahbi, Abdullah Almohammedi

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  15. A curated dataset of tensile properties for cantor-type alloys

    Chemical Data Collections · 2026-08-10

    kailai tian

    Read original paper ↗ ·
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