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Human-AI Collaborative Autonomous Experimentation With Proxy Modeling for Comparative Observation

arXiv:2603.12618v1 Announce Type: new Abstract: Optimization for different tasks like material characterization, synthesis, and functional properties for desired applications over multi-dimensional control parameters need a rapid strategic search through active learning such as Bayesian optimization (BO). However, such high-dimensional experimental physical descriptors are complex and noisy, from which realization of a low-dimensional mathematical scalar metrics or objective functions can be erroneous. Moreover, in traditional purely data-driven autonomous exploration, such objective functions often ignore the subtle variation and key features of the physical descriptors, thereby can fail to discover unknown phenomenon of the material systems. To address this, here we present a proxy-modelled Bayesian optimization (px-BO) via on-the-fly teaming between human and AI agents. Over the loop of BO, instead of defining a mathematical objective function directly from the experimental data, w

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Arpan Biswas, Hiroshi Funakubo, Yongtao Liu · · 1 min read · 8 views
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arXiv:2603.12618v1 Announce Type: new Abstract: Optimization for different tasks like material characterization, synthesis, and functional properties for desired applications over multi-dimensional control parameters need a rapid strategic search through active learning such as Bayesian optimization (BO). However, such high-dimensional experimental physical descriptors are complex and noisy, from which realization of a low-dimensional mathematical scalar metrics or objective functions can be erroneous. Moreover, in traditional purely data-driven autonomous exploration, such objective functions often ignore the subtle variation and key features of the physical descriptors, thereby can fail to discover unknown phenomenon of the material systems. To address this, here we present a proxy-modelled Bayesian optimization (px-BO) via on-the-fly teaming between human and AI agents. Over the loop of BO, instead of defining a mathematical objective function directly from the experimental data, we introduce a voting system on the fly where the new experimental outcome will be compared with existing experiments, and the human agents will choose the preferred samples. These human-guided comparisons are then transformed into a proxy-based objective function via fitting Bradley-Terry (BT) model. Then, to minimize human interaction, this iteratively trained proxy model also acts as an AI agent for future surrogate human votes. Finally, these surrogate votes are periodically validated by human agents, and the corrections are then learned by the proxy model on-the-fly. We demonstrated the performance of the proposed px-BO framework into simulated and BEPS data generated from PTO sample. We find that our approach provided better control of the domain experts for an improved search over traditional data-driven exploration, thus, signifies the importance of human-AI teaming in an accelerated and meaningful material space exploration.

Executive Summary

This article presents a novel approach to Bayesian optimization, dubbed proxy-modelled Bayesian optimization (px-BO), which leverages human-AI collaboration to accelerate material space exploration. By introducing a voting system and a proxy-based objective function, px-BO enables the discovery of unknown phenomena in material systems. The framework iteratively trains a proxy model to minimize human interaction, which is periodically validated by domain experts. The study demonstrates the performance of px-BO using simulated and BEPS data, showing improved search control compared to traditional data-driven exploration. This approach highlights the importance of human-AI teaming in accelerating material space exploration.

Key Points

  • ▸ Proxy-modelled Bayesian optimization (px-BO) enables human-AI collaboration for accelerated material space exploration.
  • ▸ A voting system and proxy-based objective function facilitate the discovery of unknown phenomena in material systems.
  • ▸ The framework iteratively trains a proxy model to minimize human interaction, which is periodically validated by domain experts.

Merits

Strength in Human-AI Collaboration

The px-BO framework effectively leverages human and AI agents to accelerate material space exploration, addressing the limitations of traditional data-driven approaches.

Improved Search Control

The study demonstrates that px-BO provides better control over the search space compared to traditional data-driven exploration, leading to more efficient discovery of material properties.

Demerits

Limited Scalability

The framework's reliance on human validation may become a bottleneck as the number of experiments increases, limiting its scalability for large-scale material space exploration.

Bias in Human Voting

The voting system may introduce bias if human agents have different opinions or priorities, which could impact the accuracy of the proxy-based objective function.

Expert Commentary

The px-BO framework represents a significant advancement in the field of material space exploration, leveraging human-AI collaboration to accelerate discovery. While the study demonstrates the effectiveness of px-BO, further research is needed to address scalability and bias concerns. Additionally, the framework's potential applications extend beyond material science, making it a valuable tool for other fields where human-AI collaboration is beneficial.

Recommendations

  • ✓ Future studies should investigate the scalability and robustness of the px-BO framework, exploring methods to mitigate the impact of human validation and bias in the voting system.
  • ✓ The px-BO framework should be applied to diverse material systems and applications to demonstrate its generalizability and effectiveness in different domains.

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