Improving Supervised Machine Learning For Materials Science

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Improving Supervised Machine Learning For Materials Science

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Author : Sheng Gong (Materials scientist)
language : en
Publisher:
Release Date : 2022

Improving Supervised Machine Learning For Materials Science written by Sheng Gong (Materials scientist) and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.

Despite the widespread applications of machine learning models in materials science, in many cases the performance of machine learning models is not sufficiently accurate enough to meet the needs of materials design. In this thesis, we propose and apply a series of strategies to exam and improve upon the performance of machine learning models for specific materials problems. First, we exam whether current deep representation learning models for atomistic systems can capture human knowledge of crystal structures, and find that current graph neural networks can capture knowledge of local atomic environments but cannot capture periodicity of crystal structures. As an initial solution, we propose to hybridize human knowledge with deep representation learning models, and find that the hybridization can lead to large improvement for predicting vibrational properties of materials. Then, for situations where the datasets of target materials properties are small while there are large relevant materials datasets, we propose to use transfer learning and multi-fidelity learning to transfer information between the large and small datasets to facilitate the learning of target properties. We use experimentally measured formation enthalpy and lattice thermal conductivity as case studies to exam the usefulness of information transfer and understand where and why information transfer helps. For situations where expansion of datasets is necessary, we propose to use active learning/Bayesian Optimization to sample the materials space efficiently and mitigate bias, and as a case study, we apply Bayesian Optimization to find the optimal laser processing parameters for poly(acrylonitrile) sheet as porous carbon electrode. Finally, if generation of data is time-consuming, we propose to use machine learning to accelerate materials experiments and simulations. For this goal , we develop a framework to use graph neural networks to predict charge density distribution of materials. The machine learning models developed in this thesis not only deepen human understanding of where and how machine learning can be used to facilitate materials development, but also lead to the discovery of new materials systems, new processes, and new insights, such as new candidate thermoelectric materials, new processes for lasering poly(acrylonitrile), and new insights into the evaluation of the stability of materials.

Machine Learning And Deep Learning In Efficacy Improvement Of Healthcare Systems

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Author : Om Prakash Jena
language : en
Publisher: CRC Press
Release Date : 2022-05-18

Machine Learning And Deep Learning In Efficacy Improvement Of Healthcare Systems written by Om Prakash Jena and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-18 with Computers categories.

The goal of medical informatics is to improve life expectancy, disease diagnosis and quality of life. Medical devices have revolutionized healthcare and have led to the modern age of machine learning, deep learning and Internet of Medical Things (IoMT) with their proliferation, mobility and agility. This book exposes different dimensions of applications for computational intelligence and explains its use in solving various biomedical and healthcare problems in the real world. This book describes the fundamental concepts of machine learning and deep learning techniques in a healthcare system. The aim of this book is to describe how deep learning methods are used to ensure high-quality data processing, medical image and signal analysis and improved healthcare applications. This book also explores different dimensions of computational intelligence applications and illustrates its use in the solution of assorted real-world biomedical and healthcare problems. Furthermore, it provides the healthcare sector with innovative advances in theory, analytical approaches, numerical simulation, statistical analysis, modelling, advanced deployment, case studies, analytical results, computational structuring and significant progress in the field of machine learning and deep learning in healthcare applications. FEATURES Explores different dimensions of computational intelligence applications and illustrates its use in the solution of assorted real-world biomedical and healthcare problems Provides guidance in developing intelligence-based diagnostic systems, efficient models and cost-effective machines Provides the latest research findings, solutions to the concerning issues and relevant theoretical frameworks in the area of machine learning and deep learning for healthcare systems Describes experiences and findings relating to protocol design, prototyping, experimental evaluation, real testbeds and empirical characterization of security and privacy interoperability issues in healthcare applications Explores and illustrates the current and future impacts of pandemics and mitigates risk in healthcare with advanced analytics This book is intended for students, researchers, professionals and policy makers working in the fields of public health and in the healthcare sector. Scientists and IT specialists will also find this book beneficial for research exposure and new ideas in the field of machine learning and deep learning.

Enhancing Hybrid Nanodevice Fabrication Efficiency Using Machine Learning

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Author : Udit Mamodiya
language : en
Publisher: John Wiley & Sons
Release Date : 2026-09-01

Enhancing Hybrid Nanodevice Fabrication Efficiency Using Machine Learning written by Udit Mamodiya and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2026-09-01 with Technology & Engineering categories.

Gain a competitive edge in the semiconductor industry with this essential guide, which provides the practical insights and machine learning techniques needed to optimize the fabrication of hybrid nanodevices for integrated circuits. Enhancing Hybrid Nanodevice Fabrication Efficiency Using Machine Learning explores the intersection of advanced manufacturing techniques and machine learning applications in the field of nanotechnology, specifically focusing on hybrid nanodevices for integrated circuits. This book provides a comprehensive understanding of how machine learning algorithms and techniques can optimize the fabrication processes of hybrid nanodevices, improving their efficiency, reliability, and performance in integrated circuit applications. The book begins with an introduction to the fundamentals of hybrid nanodevice fabrication and the role of machine learning in enhancing these processes. It then delves into various machine learning algorithms and models used for process optimization, quality control, and predictive maintenance in integrated circuit fabrication. Case studies and practical examples illustrate real-world applications of machine learning in improving yield, reducing costs, and accelerating time-to-market for hybrid nanodevices. It also addresses the pressing need for a comprehensive guide on machine learning applications in nanodevice fabrication. It provides researchers, engineers, and industry professionals with practical insights for implementing machine learning techniques to tackle challenges such as variability reduction, defect detection, and process optimization. By bridging the gap between theory and practice, the book equips readers with the knowledge and tools necessary to leverage machine learning for a competitive advantage in the semiconductor industry.

Advanced Materials And Computer Science

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Author : Garry Zhu
language : en
Publisher: Trans Tech Publications Ltd
Release Date : 2011-04-19

Advanced Materials And Computer Science written by Garry Zhu and has been published by Trans Tech Publications Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-04-19 with Technology & Engineering categories.

Selected, peer reviewed paper from 2011 International Conference on Advanced Materials and Computer Science (ICAMCS 2011), May 1-2, 2010 in Chengdu, China

Computational Materials Science

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Author : Feng Xiong
language : en
Publisher: Trans Tech Publications Ltd
Release Date : 2011-07-04

Computational Materials Science written by Feng Xiong and has been published by Trans Tech Publications Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-07-04 with Technology & Engineering categories.

Selected, peer reviewed papers from the 2011 International Conference on Computational Materials Science (CMS 2011) in April 17-18, Guangzhou, China

Advances In Clean Energy Technologies

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Author : Gaurav Dwivedi
language : en
Publisher: Springer Nature
Release Date : 2025-04-14

Advances In Clean Energy Technologies written by Gaurav Dwivedi and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-14 with Business & Economics categories.

This book contains select peer-reviewed proceedings from the International Conference on Innovations in Clean Energy Technologies (ICET 2023). It explores a variety of durable, energy-efficient, and next-generation smart green technologies aimed at promoting a sustainable future. The topics covered include smart technology-based products, energy-efficient systems, solar and wind energy, carbon sequestration, green transportation, green buildings, energy materials, biomass energy, smart cities, hydropower, bio-energy, and fuel cells. The book also discusses the performance attributes of these clean energy technologies, as well as their workability and carbon footprint. It is a valuable reference for beginners, researchers, and professionals interested in clean energy technologies.

Applications Of Mathematics In Science And Technology

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Author : Bui Thanh Hung
language : en
Publisher: CRC Press
Release Date : 2025-04-29

Applications Of Mathematics In Science And Technology written by Bui Thanh Hung and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-29 with Mathematics categories.

The Conference dealt with one of the most important problems faced in International development in Pure Mathematics and Applied mathematics development in engineering such as Cryptography, Cyber Security, Network, Operations Research, Heat Equation and so forth. The aim of the conference was to provide a platform for researchers, engineers, academicians, as well as industrial professionals, to present their research results and development activities in Pure and Apply Mathematics, and its applied technology. It provided opportunities for the delegates to exchange new ideas and application experiences, to establish business or research relations and to find global partners for future collaboration.

Revolutionizing Cardiac Muscle Detection Harnessing The Power Of Machine Learning

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Author : Yenni Rajasekhar
language : en
Publisher: GRIN Verlag
Release Date : 2024-01-10

Revolutionizing Cardiac Muscle Detection Harnessing The Power Of Machine Learning written by Yenni Rajasekhar and has been published by GRIN Verlag this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-01-10 with Medical categories.

Bachelor Thesis from the year 2023 in the subject Medicine - Biomedical Engineering, , language: English, abstract: This analysis explores advanced deep-learning techniques for stock price prediction, assessing transfer learning-based DTRSI, CNNs, and collaborative networks with sentiment analysis. DTRSI effectively addresses overfitting, outperforming traditional models. CNNs excel in predicting stock trends across time frames, while collaborative networks combining sentiment analysis and candlestick data show promise, particularly for specific stocks over longer periods. The study investigates the relevance of sentiment analysis from platforms like Twitter and StockTwits in predicting market movements. It introduces an innovative active deep learning approach for stock price forecasting, considering data size and sector impact. Emphasizing LSTM-based models, it highlights their potential to enhance stock price forecasting, offering insights for traders and investors by consolidating diverse prediction methods. This research lays the groundwork for future studies optimizing trading systems via data integration and advanced neural network architectures.

Aluminum 2003

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Author : Minerals, Metals and Materials Society. Annual Meeting
language : en
Publisher: Minerals, Metals, & Materials Society
Release Date : 2003

Aluminum 2003 written by Minerals, Metals and Materials Society. Annual Meeting and has been published by Minerals, Metals, & Materials Society this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003 with Science categories.

This collection of papers combines the proceedings of three aluminum-related symposia: - Automotive Alloys Details the ongoing research, development, and testing activities for use of aluminum and magnesium alloys in automotive applications - Fundamentals of Aluminum Offers an educational perspective on the metal - Energy Efficiency in Aluminum A presentation of reports on current research projects on increased energy efficiency of aluminum melting, casting, and processing performed by Secat, national laboratories, and universities, as well as projects being funded by the U.S. Department of Energy's Office of Information Technology and the aluminum production industry.

Artificial Intelligence In Material Science

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Author : Mohamed Arezki Mellal
language : en
Publisher: CRC Press
Release Date : 2024-12-11

Artificial Intelligence In Material Science written by Mohamed Arezki Mellal and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-11 with Technology & Engineering categories.

Artificial intelligence (AI) in the form of machine learning and nature-inspired optimization algorithms are vastly used in material science. These techniques improve many quality metrics, such as reliability and ergonomics. This book highlights the recent challenges in this field and helps readers to understand the subject and develop future works. It reviews the latest methods and applications of AI in material science. It covers a wide range of topics, including Material processing; Properties prediction; Conventional machining, such as turning, boring, grinding, and milling; non-conventional machining, such as electrical discharge machining, electrochemical machining, laser machining, plasma machining, ultrasonic machining, chemical machining, and water-jet machining; Machine tools, such as programming, design, and maintenance. AI techniques reviewed in the book include Machine learning, Fuzzy logic, Genetic algorithms, Particle swarm optimization, Cuckoo search, Grey wolf optimizer, and Ant colony optimization.