Machine Learning and Digital Manufacturing Approaches for Solid‐State Materials Development

Glasby, Lawson T.; Whaites, Emily H.; Moghadam, Peyman Z.

doi:10.1002/9781119819783.ch14

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AI‐Guided Design and Property Prediction for Zeolites and Nanoporous Materials 2023

DOI: 10.1002/9781119819783.ch14

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Machine Learning and Digital Manufacturing Approaches for Solid‐State Materials Development

Lawson T. Glasby¹,

Emily H. Whaites²,

Peyman Z. Moghadam³

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Cited by 3 publications

References 64 publications

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Recent Advances in Multifunctional Reticular Framework Nanoparticles: A Paradigm Shift in Materials Science Road to a Structured Future

Chafiq,

Chaouiki,

2023

Nano-Micro Lett.

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Porous organic frameworks (POFs) have become a highly sought-after research domain that offers a promising avenue for developing cutting-edge nanostructured materials, both in their pristine state and when subjected to various chemical and structural modifications. Metal–organic frameworks, covalent organic frameworks, and hydrogen-bonded organic frameworks are examples of these emerging materials that have gained significant attention due to their unique properties, such as high crystallinity, intrinsic porosity, unique structural regularity, diverse functionality, design flexibility, and outstanding stability. This review provides an overview of the state-of-the-art research on base-stable POFs, emphasizing the distinct pros and cons of reticular framework nanoparticles compared to other types of nanocluster materials. Thereafter, the review highlights the unique opportunity to produce multifunctional tailoring nanoparticles to meet specific application requirements. It is recommended that this potential for creating customized nanoparticles should be the driving force behind future synthesis efforts to tap the full potential of this multifaceted material category.

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Recent Advances in Multifunctional Reticular Framework Nanoparticles: A Paradigm Shift in Materials Science Road to a Structured Future

Chafiq,

Chaouiki,

2023

Nano-Micro Lett.

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A calcium aluminum rhenium sodalite with reducible rhenium in the sodalite cage

Ni,

Cheng,

Jin

et al. 2024

MRS Communications

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Classification and Regression Using Automatic Machine Learning (AutoML) – Open Source Code for Quick Adaptation and Comparison

Topsakal

Akıncı

2023

Balkan Journal of Electrical and Computer Engineering

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This paper presents a comprehensive exploration of automatic machine learning (AutoML) tools in the context of classification and regression tasks. The focus lies on understanding and illustrating the potential of these tools to accelerate and optimize the process of machine learning, thereby making it more accessible to non-experts. Specifically, we delve into multiple popular open-source AutoML tools and provide illustrative examples of their application. We first discuss the fundamental principles of AutoML, including its key features such as automated data preprocessing, feature engineering, model selection, hyperparameter tuning, and model validation. We subsequently venture into the hands-on application of these tools, demonstrating the implementation of classification and regression tasks using multiple open-source AutoML tools. We provide open-source code samples for two data scenarios for classification and regression, designed to assist readers in quickly adapting AutoML tools for their own projects and in comparing the performance of different tools. We believe that this contribution will aid both practitioners and researchers in harnessing the power of AutoML for efficient and effective machine learning model development.

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Machine Learning and Digital Manufacturing Approaches for Solid‐State Materials Development

Cited by 3 publications

References 64 publications

Recent Advances in Multifunctional Reticular Framework Nanoparticles: A Paradigm Shift in Materials Science Road to a Structured Future

Recent Advances in Multifunctional Reticular Framework Nanoparticles: A Paradigm Shift in Materials Science Road to a Structured Future

A calcium aluminum rhenium sodalite with reducible rhenium in the sodalite cage

Classification and Regression Using Automatic Machine Learning (AutoML) – Open Source Code for Quick Adaptation and Comparison

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