Proceedings

Adaptive Experimental Design to Accelerate Scientific Discovery and Engineering Design

Aryan Deshwal

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Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence. Early Career Spotlight. Pages 8150-8154.

https://doi.org/10.24963/ijcai.2026/904

Keywords

  • AI: Planning and Scheduling
  • AI: Search
  • AI: Uncertainty in AI
  • AI: Machine Learning

Abstract

Artificial Intelligence (AI) and Machine Learning hold immense potential to accelerate scientific discovery and engineering design. A fundamental challenge in these domains involves efficiently exploring a large space of designs or hypotheses using expensive experiments in a resource-efficient manner. This paper surveys novel adaptive experimental design methods to address this broad challenge. Specifically, we discuss new probabilistic modeling and decision-making techniques that are applicable in small data settings. These approaches have shown substantial improvements in sample-efficiency, particularly for black-box optimization over high-dimensional combinatorial spaces (e.g., sequences and graphs) and a variety of goals ranging from multiobjective to multi-fidelity optimization. This paper outlines key methods and their real-world sustainability applications in areas such as nanoporous materials discovery, hardware design, surfactant design, and additive manufacturing.

Aryan Deshwal. 2026. Adaptive Experimental Design to Accelerate Scientific Discovery and Engineering Design. In Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-26. IJCAI, 8150–8154. https://doi.org/10.24963/ijcai.2026/904
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