Machine learning in chemistry : the impact of artificial intelligence
This book provides practical examples of machine learning applied to science to help researchers make an informed choice about using the method in chemistry.
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| Other Authors | |
|---|---|
| Format | Electronic eBook |
| Language | English |
| Published |
London :
Royal Society of Chemistry,
2020.
|
| Series | Theoretical and computational chemistry ;
17. |
| Subjects | |
| Online Access | Full text |
| ISBN | 9781839160233 1839160233 9781839160240 1839160241 9781788017893 1788017897 |
| Physical Description | 1 online resource |
Cover
Table of Contents:
- Computers as scientists
- How do machines learn?
- MedChemInformatics: an introduction to machine learning for drug discovery
- Machine learning for nonadiabatic molecular dynamics
- Machine learning in science-a role for mechanic sympathy?
- A prediction of future states: AI-powered chemical innovation for defense applications
- Machine learning for chemical synthesis
- Constraining chemical networks in astrochemistry
- Machine learning at he (nano)materials-biology interface
- Machine learning techniques applied to a complex polymerization process
- machine learning and scoring functions (SFs) for molecular drug discovery: prediction and characterisation of druggable drugs and targets
- Artificial intelligence applied to the prediction of organic materials
- A new era of inorganic materials discovery powered by data science
- Machine learning application sin chemical engineering
- representation learning in chemistry
- Demystifying artificial neural networks as generators of new chemical knowledge: antimalarial drug discovery as a case study
- Machine learning for core-loss spectrum
- Autonomous science: big data tools for small data problems in chemistry
- Machine learning for heterogeneous catalysis: global neural network potential from construction to applications
- A few guiding principles for practical applications of machine learning to chemistry and materials.