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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.
| Clasificación: | Libro Electrónico |
|---|---|
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
London :
Royal Society of Chemistry,
2020.
|
| Colección: | Theoretical and computational chemistry ;
17. |
| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- 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.