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ARXIV:2603.12618 · MATERIAL OPTIMIZATION · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.12618MATERIAL OPTIMIZATIONSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
A human-AI collaborative framework for optimizing material properties through proxy modeling and Bayesian optimization.
Opportunity summary
Pain A human-AI collaborative framework for optimizing material properties through proxy modeling and Bayesian optimization.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
A human-AI collaborative framework for optimizing material properties through proxy modeling and Bayesian optimization. However, such high-dimensional experimental physical descriptors are complex and noisy, from which realization of a low-dimensional mathematical scalar metrics or…
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…
ScienceToStartup currently rates this 4.0/10 on the public viability pass. 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…
Material Optimization moved forward this cycle; last verified April 2026. Public score 4.0/10.
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A human-AI collaborative framework for optimizing material properties through proxy modeling and Bayesian optimization.
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10.48550/arXiv.2603.12618A human-AI collaborative framework for optimizing material properties through proxy modeling and Bayesian optimization.
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.
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PROBLEM
A human-AI collaborative framework for optimizing material properties through proxy modeling and Bayesian optimization. However, such high-dimensional experimental physical descriptors are complex and noisy, from which realization of a low-dimensional mathematical scalar metrics...
METHOD
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-d...
RESULT
ScienceToStartup currently rates this 4.0/10 on the public viability pass. 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 accelerate...
WHY NOW
Material Optimization moved forward this cycle; last verified April 2026. Public score 4.0/10.
Abstract-backed public claims while anchored extraction refreshes.
A human-AI collaborative framework for optimizing material properties through proxy modeling and Bayesian optimization. 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.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 4.0/10 on the public viability pass. 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.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Material Optimization moved forward this cycle; last verified April 2026. Public score 4.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A human-AI collaborative framework for optimizing material properties through proxy modeling and Bayesian optimization.
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Material Optimization
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