Titlebook: Machine Learning in Molecular Sciences; Chen Qu,Hanchao Liu Book 2023 The Editor(s) (if applicable) and The Author(s), under exclusive lic

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Lenz Fiedler,Karan Shah,Attila Cangita of ternary alloy systems. Reliable phase diagrams provide materials scientists and engineers with basic information important for fundamental research, development and optimization of materials. ...The often conflicting literature data have been critically evaluated by Materials Science Internati

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Lung-Yi Chen,Yi-Pei Litional scientists.Also available online in www.springerLink..The present volume in the New Series of Landolt-Börnstein provides critically evaluated data on phase diagrams, crystallographic and thermodynamic data of ternary alloy systems. Reliable phase diagrams provide materials scientists and engi

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Dian-Zhao Lin,Guichun Fang,Kuangbiao Liaotional scientists.Also available online in www.springerLink..The present volume in the New Series of Landolt-Börnstein provides critically evaluated data on phase diagrams, crystallographic and thermodynamic data of ternary alloy systems. Reliable phase diagrams provide materials scientists and engi

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Kadina E. Johnston,Clara Fannjiang,Bruce J. Wittmann,Brian L. Hie,Kevin K. Yang,Zachary Wuta of ternary alloy systems. Reliable phase diagrams provide materials scientists and engineers with basic information important for fundamental research, development and optimization of materials. ...The often conflicting literature data have been critically evaluated by Materials Science Internati

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2542-4491 es contributions from experts in the fieldMachine learning and artificial intelligence have propelled research across various molecular science disciplines thanks to the rapid progress in computing hardware, algorithms, and data accumulation. This book presents recent machine learning applications i

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Book 2023, to the design and synthesis of large organic and biological molecules. This book is a valuable resource for researchers and students interested in applying machine learning in the research of molecular sciences.

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An Introduction to Machine Learning in Molecular Sciences,in many recent applications. Finally, we discuss some typical challenges in applying machine learning in molecular sciences, including data scarcity, model explainability, and uncertainty quantification.

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