商品描述
This textbook is designed for students and researchers who are interested in materials and catalysts informatics with little to no prior experience in data science or programming languages. Starting with a comprehensive overview of the concept and historical context of materials and catalysts informatics, it serves as a guide for establishing a robust materials informatics environment. This essential resource is designed to teach vital skills and techniques required for conducting informatics-driven research, including the intersection of hardware, software, programming, machine learning within the field of data science and informatics.
Readers will explore fundamental programming techniques, with a specific focus on Python, a versatile and widely-used language in the field. The textbook explores various machine learning techniques, equipping learners with the knowledge to harness the power of data science effectively. The textbook provides Python code examples, demonstrating materials informatics applications, and offers a deeper understanding through real-world case studies using materials and catalysts data. This practical exposure ensures readers are fully prepared to embark on their informatics-driven research endeavors upon completing the textbook.
Instructors will also find immense value in this resource, as it consolidates the skills and information required for materials informatics into one comprehensive repository. This streamlines the course development process, significantly reducing the time spent on creating course material. Instructors can leverage this solid foundation to craft engaging and informative lecture content, making the teaching process more efficient and effective.
商品描述(中文翻譯)
這本教科書是為對材料與催化劑資訊學感興趣的學生和研究人員設計的，適合那些幾乎沒有數據科學或程式語言經驗的人。書中首先提供了材料與催化劑資訊學的概念及歷史背景的全面概述，並作為建立穩健的材料資訊學環境的指導。這本重要的資源旨在教授進行資訊驅動研究所需的關鍵技能和技術，包括硬體、軟體、程式設計和機器學習在數據科學和資訊學領域的交集。
讀者將探索基本的程式設計技術，特別專注於 Python，這是一種在該領域中廣泛使用的多功能語言。教科書探討了各種機器學習技術，使學習者具備有效利用數據科學的知識。書中提供了 Python 代碼範例，展示材料資訊學的應用，並通過使用材料和催化劑數據的實際案例研究提供更深入的理解。這種實踐經驗確保讀者在完成教科書後，能充分準備好展開他們的資訊驅動研究工作。
教師也會發現這本資源的巨大價值，因為它將材料資訊學所需的技能和信息整合成一個全面的資料庫。這簡化了課程開發過程，顯著減少了創建課程材料所需的時間。教師可以利用這個堅實的基礎來設計引人入勝且具資訊性的講座內容，使教學過程更加高效和有效。
作者簡介
Keisuke Takahashi is a full professor at the Department of Chemistry at Hokkaido University in Japan. He has earned a B.S. in Materials Science and Engineering at the University of Arizona, followed by an M.S. at Chalmers University of Technology and a Ph.D. at Hokkaido University. He is the principal investigator of the Information Chemistry group at Hokkaido University where he conducts research in materials and catalyst informatics. His group performs experiment, theory, computation, and data science for designing heterogeneous catalysis, water splitting, artificial photosynthesis, CO2 reduction, perovskite solar cells, and 2-dimensional materials. His research interests are focused on designing catalysts and materials via materials and catalysts informatics.
Lauren Takahashi is an assistant professor at the Department of Chemistry at Hokkaido University in Japan. She is also a member of the Information Chemistry group with Professor Takahashi where she applies ontology and data science towards material design and catalyst-centered research. She has earned a B.A. in Linguistics at the University of Arizona, an M.S. in Communication at the University of Gothenburg, and a Ph.D. in Chemical Systems Engineering at The University of Tokyo. Her research interests focus on improving the structure, usability, and semantics of materials and catalyst data through tactical applications of ontology, data science, machine learning, graph theory, and information science, with the aim of improving the materials and catalysts design process.
作者簡介(中文翻譯)
高橋圭介是日本北海道大學化學系的正教授。他在亞利桑那大學獲得材料科學與工程學士學位，隨後在查爾默斯科技大學獲得碩士學位，並在北海道大學獲得博士學位。他是北海道大學資訊化學研究小組的主要研究者，專注於材料與催化劑資訊學的研究。他的研究小組進行實驗、理論、計算和數據科學，以設計異質催化、水分解、人工光合作用、二氧化碳還原、鈣鈦礦太陽能電池和二維材料。他的研究興趣集中在通過材料與催化劑資訊學設計催化劑和材料。
高橋蘭是日本北海道大學化學系的助理教授。她也是高橋教授資訊化學研究小組的成員，專注於將本體論和數據科學應用於材料設計和以催化劑為中心的研究。她在亞利桑那大學獲得語言學學士學位，在哥德堡大學獲得傳播學碩士學位，並在東京大學獲得化學系統工程博士學位。她的研究興趣集中在通過本體論、數據科學、機器學習、圖論和資訊科學的戰術應用來改善材料和催化劑數據的結構、可用性和語義，旨在改善材料和催化劑的設計過程。
