Machine Learning in molecular sciences:
Machine 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 in the broad research field of molecular sc...
Gespeichert in:
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| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cham, Switzerland
Springer
[2023]
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| Ausgabe: | 2023 |
| Schriftenreihe: | Challenges and advances in computational chemistry and physics
volume 36 |
| Schlagworte: | |
| Zusammenfassung: | Machine 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 in the broad research field of molecular sciences. Written by an international group of renowned experts, this edited volume covers both the machine learning methodologies and state-of-the-art machine learning applications in a wide range of topics in molecular sciences, from electronic structure theory to nuclear dynamics of small molecules, 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 |
| Beschreibung: | An Introduction to Machine Learning in Molecular Sciences.- Graph Neural Networks for Molecules.- Voxelized representations of atomic systems for machine learning applications.- Development of exchange-correlation functionals assisted by machine learning.- Machine-Learning for Static and Dynamic Electronic Structure Theory.- Data Quality, Data Sampling and Data Fitting: A Tutorial Guide for Constructing Full-dimensional Accurate Potential Energy Surfaces (PESs) of Molecules and Reactions.- Machine Learning Applications in Chemical Kinetics and Thermochemistry.- Synthesize in A Smart Way: A Brief Introduction to Intelligence and Automation in Organic Synthesis.- Machine Learning for Protein Engineering |
| Beschreibung: | x, 317 Seiten Illustrationen, Diagramme 235 mm |
| ISBN: | 9783031371981 |
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| 490 | 0 | |a Challenges and advances in computational chemistry and physics |v volume 36 | |
| 500 | |a An Introduction to Machine Learning in Molecular Sciences.- Graph Neural Networks for Molecules.- Voxelized representations of atomic systems for machine learning applications.- Development of exchange-correlation functionals assisted by machine learning.- Machine-Learning for Static and Dynamic Electronic Structure Theory.- Data Quality, Data Sampling and Data Fitting: A Tutorial Guide for Constructing Full-dimensional Accurate Potential Energy Surfaces (PESs) of Molecules and Reactions.- Machine Learning Applications in Chemical Kinetics and Thermochemistry.- Synthesize in A Smart Way: A Brief Introduction to Intelligence and Automation in Organic Synthesis.- Machine Learning for Protein Engineering | ||
| 520 | |a Machine 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 in the broad research field of molecular sciences. Written by an international group of renowned experts, this edited volume covers both the machine learning methodologies and state-of-the-art machine learning applications in a wide range of topics in molecular sciences, from electronic structure theory to nuclear dynamics of small molecules, 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 | ||
| 650 | 4 | |a Artificial intelligence | |
| 650 | 4 | |a Molecules—Models | |
| 650 | 4 | |a Chemistry, Physical and theoretical | |
| 650 | 4 | |a Chemistry—Data processing | |
| 650 | 4 | |a Bioinformatics | |
| 650 | 4 | |a Machine learning | |
| 653 | |a Hardcover, Softcover / Informatik, EDV/Informatik | ||
| 655 | 7 | |0 (DE-588)4143413-4 |a Aufsatzsammlung |2 gnd-content | |
| 700 | 1 | |a Qu, Chen |4 edt | |
| 700 | 1 | |a Liu, Hanchao |4 edt | |
| 776 | 0 | 8 | |i Erscheint auch als |n Online-Ausgabe |z 978-3-031-37196-7 |
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| series2 | Challenges and advances in computational chemistry and physics |
| spelling | Machine Learning in molecular sciences Chen Qu, Hanchao Liu, editors Cham, Switzerland Springer [2023] x, 317 Seiten Illustrationen, Diagramme 235 mm txt rdacontent n rdamedia nc rdacarrier Challenges and advances in computational chemistry and physics volume 36 An Introduction to Machine Learning in Molecular Sciences.- Graph Neural Networks for Molecules.- Voxelized representations of atomic systems for machine learning applications.- Development of exchange-correlation functionals assisted by machine learning.- Machine-Learning for Static and Dynamic Electronic Structure Theory.- Data Quality, Data Sampling and Data Fitting: A Tutorial Guide for Constructing Full-dimensional Accurate Potential Energy Surfaces (PESs) of Molecules and Reactions.- Machine Learning Applications in Chemical Kinetics and Thermochemistry.- Synthesize in A Smart Way: A Brief Introduction to Intelligence and Automation in Organic Synthesis.- Machine Learning for Protein Engineering Machine 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 in the broad research field of molecular sciences. Written by an international group of renowned experts, this edited volume covers both the machine learning methodologies and state-of-the-art machine learning applications in a wide range of topics in molecular sciences, from electronic structure theory to nuclear dynamics of small molecules, 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 Artificial intelligence Molecules—Models Chemistry, Physical and theoretical Chemistry—Data processing Bioinformatics Machine learning Hardcover, Softcover / Informatik, EDV/Informatik (DE-588)4143413-4 Aufsatzsammlung gnd-content Qu, Chen edt Liu, Hanchao edt Erscheint auch als Online-Ausgabe 978-3-031-37196-7 |
| spellingShingle | Qu, Chen Machine Learning in molecular sciences Artificial intelligence Molecules—Models Chemistry, Physical and theoretical Chemistry—Data processing Bioinformatics Machine learning |
| subject_GND | (DE-588)4143413-4 |
| title | Machine Learning in molecular sciences |
| title_auth | Machine Learning in molecular sciences |
| title_exact_search | Machine Learning in molecular sciences |
| title_full | Machine Learning in molecular sciences Chen Qu, Hanchao Liu, editors |
| title_fullStr | Machine Learning in molecular sciences Chen Qu, Hanchao Liu, editors |
| title_full_unstemmed | Machine Learning in molecular sciences Chen Qu, Hanchao Liu, editors |
| title_short | Machine Learning in molecular sciences |
| title_sort | machine learning in molecular sciences |
| topic | Artificial intelligence Molecules—Models Chemistry, Physical and theoretical Chemistry—Data processing Bioinformatics Machine learning |
| topic_facet | Artificial intelligence Molecules—Models Chemistry, Physical and theoretical Chemistry—Data processing Bioinformatics Machine learning Aufsatzsammlung |
| work_keys_str_mv | AT quchen machinelearninginmolecularsciences AT liuhanchao machinelearninginmolecularsciences |