The development of iterative design of experiments based on a Bayesian approach is gaining interest as shown in these two articles:
- Shields, B. J., Stevens, J., Li, J., Parasram, M., Damani, F., Alvarado, J. I. M., ... & Doyle, A. G. (2021). Bayesian reaction optimization as a tool for chemical synthesis. Nature, 590(7844), 89-96.
- Greenhill, S., Rana, S., Gupta, S., Vellanki, P., & Venkatesh, S. (2020). Bayesian optimization for adaptive experimental design: a review. IEEE access, 8, 13937-13948.
A JMP add-in is available to implement this approach (https://community.jmp.com/t5/JMP-Add-Ins/Bayesian-optimization-add-in/ta-p/496785). Unfortunately, the add-in lacks some important features such as:
- the use of several model types (in particular the bootstrap forest)
- the management of discrete variables
- the management of experimental constraints
- the possibility to run several experiments per iteration
It would be very appreciable and useful to have such a platform available, especially in the fields of exploratory research for the design of new materials.