Bayesian optimization for materials science /
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| Format: | eBook |
| Language: | English |
| Published: |
Singapore :
Springer,
[2017]
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| Series: | SpringerBriefs in the mathematics of materials ;
v. 3. |
| Subjects: | |
| Online Access: | Connect to the full text of this electronic book |
Table of Contents:
- ""Preface""; ""Contents""; ""1 Overview of Bayesian Optimization in Materials Science""; ""1.1 Brief Overview of Bayesian Optimisation""; ""1.2 Examples of Bayesian Optimisation in Materials Science""; ""1.2.1 Prediction of Compounds with Low Thermal Conductivity""; ""1.2.2 Prediction of Compounds with Optimal Melting Temperatures and Elastic Properties""; ""1.2.3 Prediction of Interface Structures""; ""1.2.4 Design of Interface Nanostructure""; ""1.3 Bayesian Optimization Requires Good Feature Vectors""; ""References""; ""2 Theory of Bayesian Optimization""
- ""2.1 Bayesian Interpretation of Probability""""2.2 Equilibrium Bond Lengths Via Bayesian Optimization""; ""2.2.1 Prior Probability""; ""2.2.2 Likelihood Function and Posterior Distribution""; ""2.2.3 Example Calculation of the Posterior Distribution""; ""2.2.4 The Expected Improvement""; ""2.2.5 Example Run of Bayesian Optimisation""; ""2.2.6 Training""; ""2.3 Bayesian Optimization in the General Case""; ""2.4 R Code for Bayesian Optimization""; ""Appendix 2.1""; ""Appendix 2.2""; ""Appendix 2.3""; ""References""; ""3 Bayesian Optimization of Molecules Adsorbed to Metal Surfaces""
- ""3.1 Density Functional Theory for Surface Science""""3.2 Bayesian Optimization for Surface Science""; ""3.2.1 Preliminary Computational Study""; ""3.2.2 Statement of Optimization Problem""; ""3.2.3 Data Description""; ""3.2.4 Choice of Feature Vectors""; ""3.2.5 Training of Hyperparameters""; ""3.2.6 Predictive Performance""; ""3.2.7 Discussion""