NEXTorch¶

NEXTorch is an open-source software package in Python/PyTorch to faciliate experimental design using Bayesian Optimization (BO).

NEXTorch stands for Next EXperiment toolkit in PyTorch/BoTorch. It is also a library for learning the theory and implementation of Bayesian Optimization.

Documentation¶

See our documentation page for examples, equations used, and docstrings.

Developers¶

Yifan Wang (wangyf@udel.edu)
Tai-Ying (Chris) Chen

Dependencies¶

Python >= 3.7
PyTorch >= 1.8: Used for tensor operations with GPU and autograd support
GPyTorch >= 1.4: Used for training Gaussian Processes
BoTorch >= 0.4.0: Used for providing Bayesian Optimization framework
Matplotlib: Used for generating plots
PyDOE2: Used for constructing experimental designs
Numpy: Used for vector and matrix operations
Scipy: Used for curve fitting
Pandas: Used to import data from Excel or CSV files
openpyxl: Used by Pandas to import Excel files
pytest: Used for unit tests

Getting Started¶

Install using pip (see documentation for full instructions):
```
pip install nextorch
```
Run the unit tests.
Read the documentation for tutorials and examples.

License¶

This project is licensed under the MIT License - see the LICENSE.md. file for details.

Contributing¶

If you have a suggestion or find a bug, please post to our Issues page on GitHub.

Questions¶

If you are having issues, please post to our Issues page on GitHub.

Funding¶

This material is based upon work supported by the Department of Energy’s Office of Energy Efficient and Renewable Energy’s Advanced Manufacturing Office under Award Number DE-EE0007888-9.5.

Acknowledgements¶

Jaynell Keely (Logo design)

Publications¶

Y. Wang, T.-Y. Chen, and D.G. Vlachos, NEXTorch: A Design and Bayesian Optimization Toolkit for Chemical Sciences and Engineering, J. Chem. Inf. Model. 2021, 61, 11, 5312–5319.