# What are some examples of active learning methods used in atomistic machine learning?

Many machine learning attempts in atomistic applications (see this answer) seem to parameterize models on calculated data (i.e., CCSD(T), DFT, etc.). This approach suggests some automatic procedure for active machine learning in this context.

So the question is: what is the current status of active machine learning methods for atomistic applications? Specifically, I want to know which algorithms are more appropriate for chemical problems.

• My edit should not only bump up this unanswered question, but should also help make it more answerable. – Nike Dattani Jun 12 at 22:38
• It has also been posted on Twitter 3 times (once, and then 2 re-tweets with new comments, and new tags). You can re-tweet all three of the Twitter posts to get more attention. – Nike Dattani Jun 12 at 23:02

To formulate a simple example, imagine you want to discover new stable quaternary compound in the quaternary system $$\ce{Ba - Sr - Mn - O}$$. One would start the experiment from $$\ce{BaSrMn2O6}$$, and the active learning algorithm learns from the existing data and the success/failure of this experiment to guide the next point to experiment in the quaternary system, such that smaller number of steps will be needed to reach convergence, which is to find a new stable compound. The same example can be reiterated to find new quaternary compound with target dielectric constant etc. It is specifically very useful when the compound search space is huge, but only very few experimental data is available on such compounds.