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arXiv · 2602.03319

Information-Theoretic Multi-Model Fusion for Target-Oriented Adaptive Sampling in Materials Design

Yixuan Zhang·Zhiyuan Li·Weijia He·Mian Dai·Chen Shen·Teng Long·Hongbin Zhang
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Abstract

Target-oriented discovery under limited evaluation budgets requires making reliable progress in high-dimensional, heterogeneous design spaces where each new measurement is costly, whether experimental or high-fidelity simulation. We present an information-theoretic framework for target-oriented adaptive sampling that reframes optimization as trajectory discovery: instead of approximating the full response surface, the method maintains and refines a low-entropy information state that concentrates search on target-relevant directions. The approach couples data, model beliefs, and physics/structure priors through dimension-aware information budgeting, adaptive bootstrapped distillation over a heterogeneous surrogate reservoir, and structure-aware candidate manifold analysis with Kalman-inspired multi-model fusion to balance consensus-driven exploitation and disagreement-driven exploration. Evaluated under a single unified protocol without dataset-specific tuning, the framework improves sample efficiency and reliability across 14 single- and multi-objective materials design tasks spanning candidate pools from $600$ to $4 \times 10^6$ and feature dimensions from $10$ to $10^3$, typically reaching top-performing regions within 100 evaluations. Complementary 20-dimensional synthetic benchmarks (Ackley, Rastrigin, Schwefel) further demonstrate robustness to rugged and multimodal landscapes.

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Yixuan Zhang, Zhiyuan Li, Weijia He, Mian Dai, Chen Shen, Teng Long, Hongbin Zhang. 2026-02-03. Information-Theoretic Multi-Model Fusion for Target-Oriented Adaptive Sampling in Materials Design. https://arxiv.org/abs/2602.03319

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