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RESEARCH PAPER · Processes · 2018

Solving Materials’ Small Data Problem with Dynamic Experimental Databases

Michael McBride, Nils E. Persson, Elsa Reichmanis, Martha A. Grover

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Solving Materials’ Small Data Problem with Dynamic Experimental DatabasesOriginalSpecial Issue on Feature Papers for Celebrating the Fifth Anniversary of the Founding of Processes1New Technical Parameters and Operational Improvements of the Metal Oxide Varistors Manufacturing Process18Case Study about the Energy Absorption Capacity of Metal Oxide Varistors with Thermal Coupling13Embedding domain knowledge for machine learning of complex material systems71Toward data-enabled process optimization of deformable electronic polymer-based devices10High-throughput experimentation meets artificial intelligence: a new pathway to catalyst discovery179Discovery of superionic conductors by ensemble-scope descriptor31Generating Multiscale Amorphous Molecular Structures Using Deep Learning: A Study in 2D20Small Data Machine Learning: Classification and Prediction of Poly(ethylene terephthalate) Stabilizers Using Molecular Descriptors32Materials Informatics Approach to Predictive Models for Elastic Modulus of Polypropylene Composites Reinforced by Fillers and Additives2Prediction the correlations between hardness and tensile properties of aluminium-silicon alloys produced by various modifiers and grain refineries using regression analysis and an artificial neural network model25Integration of data-intensive, machine learning and robotic experimental approaches for accelerated discovery of catalysts in renewable energy-related reactions28Data Science Guided Experiments Identify Conjugated Polymer Solution Concentration as a Key Parameter in Device Performance23Service Limits for Metal Oxide Varistors Having Cylindrical Symmetry as Function of the Ambient Temperature4Using a neural network model and regression analysis to predict the wear and impact energy of Al-Si alloys modified with various modifiers and grain refiners using its hardness5Conjugated Polymer Process Ontology and Experimental Data Repository for Organic Field-Effect Transistors9Improvements in the Electronic Performance of ZnO-Based Varistors by Modifying the Manufacturing Process Parameters8Understanding the Performance of (Ni−Fe−Co−Ce)Ox‐Based Water Oxidation Catalysts via Explainable Artificial Intelligence Framework21Real-Time Modeling for Design and Control of Material Additive Manufacturing Processes17Optimizing the processing parameters of producing Al–Si alloys using sodium fluosilicate via artificial neural network1

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  1. Special Issue on Feature Papers for Celebrating the Fifth Anniversary of the Founding of Processes

    Processes · 2019-01-01

    Michael A. Henson

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  2. New Technical Parameters and Operational Improvements of the Metal Oxide Varistors Manufacturing Process

    Processes · 2019-01-02

    Flaviu Mihai Frigura-Iliasa, Sorin Muşuroi, Ciprian Șorândaru, Doru Vatau

    Read original paper ↗ ·

  3. Case Study about the Energy Absorption Capacity of Metal Oxide Varistors with Thermal Coupling

    Energies · 2019-02-08

    Flaviu Mihai Frigura-Iliasa, Sorin Muşuroi, Ciprian Șorândaru, Doru Vatau

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  4. Embedding domain knowledge for machine learning of complex material systems

    MRS Communications · 2019-07-10

    Christopher M. Childs, Newell R. Washburn

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  5. Toward data-enabled process optimization of deformable electronic polymer-based devices

    Current Opinion in Chemical Engineering · 2019-12-19

    Michael McBride, Aaron Liu, Elsa Reichmanis, Martha A. Grover

    Read original paper ↗ ·

  6. High-throughput experimentation meets artificial intelligence: a new pathway to catalyst discovery

    Physical Chemistry Chemical Physics · 2020-01-01

    Katherine McCullough, Travis Williams, Kathleen Mingle, Pooyan Jamshidi, Jochen A. Lauterbach

    Read original paper ↗ ·

  7. Discovery of superionic conductors by ensemble-scope descriptor

    NPG Asia Materials · 2020-04-23

    Seiji Kajita, Nobuko Ohba, Akitoshi Suzumura, Shin Tajima, Ryoji Asahi

    Read original paper ↗ ·

  8. Generating Multiscale Amorphous Molecular Structures Using Deep Learning: A Study in 2D

    The Journal of Physical Chemistry Letters · 2020-09-24

    Michael Kilgour, Nicolas Gastellu, David Yu-Tung Hui, Yoshua Bengio, Lena Simine

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  9. Small Data Machine Learning: Classification and Prediction of Poly(ethylene terephthalate) Stabilizers Using Molecular Descriptors

    ACS Applied Polymer Materials · 2020-11-23

    Aaron L. Liu, Rahul Venkatesh, Michael McBride, Elsa Reichmanis, J. Carson Meredith, Martha A. Grover

    Read original paper ↗ ·

  10. Materials Informatics Approach to Predictive Models for Elastic Modulus of Polypropylene Composites Reinforced by Fillers and Additives

    Journal of Computer Chemistry Japan -International Edition · 2021-01-01

    Yuko IKEDA, Michihiro OKUYAMA, Yukihito Nakazawa, Tomohiro Oshiyama, Kimito Funatsu

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  11. Prediction the correlations between hardness and tensile properties of aluminium-silicon alloys produced by various modifiers and grain refineries using regression analysis and an artificial neural network model

    Engineering Science and Technology an International Journal · 2021-01-14

    Mohamed Mahmoud Ali, Abdel-Nasser Mohamed OMRAN, Mohamed Abd‐El‐Hakeem Mohamed

    Read original paper ↗ ·

  12. Integration of data-intensive, machine learning and robotic experimental approaches for accelerated discovery of catalysts in renewable energy-related reactions

    Materials Reports Energy · 2021-07-05

    Oyawale Adetunji Moses, Wei Chen, Mukhtar Lawan Adam, Zhuo Wang, Kaili Liu, Junming Shao, Zhengsheng Li, Wentao Li, Chensu Wang, Haitao Zhao, Cheng Heng Pang, Zongyou Yin, Xue‐Feng Yu

    Read original paper ↗ ·

  13. Data Science Guided Experiments Identify Conjugated Polymer Solution Concentration as a Key Parameter in Device Performance

    ACS Materials Letters · 2021-08-11

    Rahul Venkatesh, Yulong Zheng, Campbell Viersen, Aaron L. Liu, Carlos Silva, Martha A. Grover, Elsa Reichmanis

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  14. Service Limits for Metal Oxide Varistors Having Cylindrical Symmetry as Function of the Ambient Temperature

    Symmetry · 2022-06-30

    Attila Simó, Flaviu Mihai Frigura-Iliasa, Mihaela Frigura-Iliasa, Petru Andea, Sorin Muşuroi

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  15. Using a neural network model and regression analysis to predict the wear and impact energy of Al-Si alloys modified with various modifiers and grain refiners using its hardness

    Materials Today Communications · 2023-09-18

    Gamal M. A. Mahran, Abdel-Nasser Mohamed OMRAN

    Read original paper ↗ ·

  16. Conjugated Polymer Process Ontology and Experimental Data Repository for Organic Field-Effect Transistors

    Chemistry of Materials · 2023-10-25

    Aaron L. Liu, Myeongyeon Lee, Rahul Venkatesh, Jessica A. Bonsu, Ron Volkovinsky, J. Carson Meredith, Elsa Reichmanis, Martha A. Grover

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  17. Improvements in the Electronic Performance of ZnO-Based Varistors by Modifying the Manufacturing Process Parameters

    Electronics · 2023-12-07

    Attila Simó, Flaviu Mihai Frigura-Iliasa, Mihaela Frigura-Iliasa, Petru Andea

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  18. Understanding the Performance of (Ni−Fe−Co−Ce)Ox‐Based Water Oxidation Catalysts via Explainable Artificial Intelligence Framework

    ChemElectroChem · 2024-03-08

    Paul Rossener Regonia, Christian Mark Pelicano

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  19. Real-Time Modeling for Design and Control of Material Additive Manufacturing Processes

    Metals · 2024-11-08

    Amir M. Horr

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  20. Optimizing the processing parameters of producing Al–Si alloys using sodium fluosilicate via artificial neural network

    Materials Advances · 2025-01-01

    Gamal M. A. Mahran, Abdel-Nasser Mohamed OMRAN, Hassan E. M. Gomaa, Mahamed Abd-El-Hakeem Mahamed

    Read original paper ↗ ·
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