Materials Discovery Using Uncertainty-Aware Constrained Bayesian Optimization With Representation Learning of High-Dimensional Inputs
Bibliographic record
Abstract
Abstract High-dimensional structure and composition spaces pose a fundamental challenge in materials discovery due to the lack of efficient approaches for navigating the vast and complex design space. Although machine learning (ML) has aided materials discovery, most existing ML models lack the ability to quantify epistemic uncertainty arising from limited data. Developing this capability is particularly challenging for tasks involving high-dimensional design representations, such as atomic structures. In this study, building on the Bayesian optimization (BO) framework, we propose an uncertainty-aware atomistic machine learning model, uncertainty-aware PointNet, which enables automated representation learning directly from high-dimensional design inputs, such as atomic structures, and achieves principled uncertainty quantification through the use of spectral-normalized neural Gaussian process. By utilizing a constrained expected improvement acquisition function, our BO framework simultaneously considers multiple design criteria. We demonstrate the effectiveness of our approach in two materials discovery case studies: (1) identifying catalysts for the carbon dioxide reduction reaction and (2) designing transparent conducting materials. The results show that our approach achieves high prediction accuracy, facilitates interpretable feature extraction, and enables multicriteria material design using constrained BO, leading to a significant reduction of computing power and time (a 10× reduction in required simulation calculations). Beyond the demonstration examples, the developed method can accelerate materials discovery for various other applications with high-dimensional design inputs and expensive physics-based simulations.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".