Anubis: Bayesian optimization with unknown feasibility constraints for scientific experimentation
Bibliographic record
Abstract
Model-based optimization strategies, such as Bayesian optimization (BO), have been deployed across the natural sciences in design and discovery campaigns due to their sample efficiency and flexibility. The combination of such strategies with automated laboratory equipment and/or high-performance computing in a suggest-make-measure closed-loop constitutes a self-driving laboratory (SDL), which have been endorsed as a next-generation technology for autonomous scientific experimentation. Despite the promise of early SDL prototypes, a lack of flexible experiment planning algorithms prevents certain prevalent optimization problem types from being addressed. For instance, many experiment planning algorithms are unable to intelligently deal with failed measurements resulting from a priori unknown constraints on the parameter space. Such constraint functions are pervasive in chemistry and materials science research, stemming from unexpected equipment failures, failed/abandoned syntheses, or unstable molecules or materials. In this work, we provide a comprehensive discussion and benchmark of BO strategies to deal with a priori unknown constraints, characterized by learning the constraint function on-the-fly using a variational Gaussian process classifier and combining its predictions with the typical BO regression surrogate to parameterize feasibility-aware acquisition functions. These acquisition functions balance sampling parameter space regions deemed to be promising in terms of optimization objectives with avoidance of regions predicted to be infeasible. In addition to benchmarking feasibility-aware acquisition functions on analytic optimization benchmark surfaces, we conduct two realistic optimization benchmarks derived from previously reported studies: inverse design of hybrid organic-inorganic halide perovskite materials with unknown stability constraints, and the design of BCR-Abl kinase inhibitors with unknown synthetic accessibility constraints. We deliver intuitive recommendations to readers on which strategies work best for various scenarios. Overall, this work contributes to advancing the practicality and efficiency of autonomous experimentation in SDLs. All strategies introduced in this work are implemented as part of the open-source Atlas Python library.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".