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arxiv: 2605.17976 · v1 · pith:H3VXKV2Inew · submitted 2026-05-18 · 💻 cs.AI · math.OC

Unleashing LLMs in Bayesian Optimization: Preference-Guided Framework for Scientific Discovery

Xinzhe Yuan , Zhuo Chen , Jianshu Zhang , Huan Xiong , Nanyang Ye , Yuqiang Li , Qinying Gu This is my paper

Pith reviewed 2026-05-20 09:54 UTC · model grok-4.3

classification 💻 cs.AI math.OC
keywords Bayesian optimizationlarge language modelspreference guidancescientific discoveryelectrolyte optimizationconvergence analysissurrogate modeling
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The pith

Large language models can guide Bayesian optimization to reach strong results in far fewer experimental steps by adjusting the search at every iteration.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper sets out to show that large language models can supply useful preferences that speed up Bayesian optimization for finding the best conditions in scientific experiments. It introduces a way to fold those preferences into the process continuously rather than only at the start, by shifting the underlying prediction model in a controlled fashion. This matters because many discoveries in chemistry and materials require running many costly tests, and cutting the number of trials needed means less time and expense. The authors provide a guarantee that the new approach will not be much worse than ordinary Bayesian optimization when the language model suggestions miss the mark, yet it moves ahead faster when the suggestions line up with the true goal. They support the claim with theory and with tests on standard problems plus one actual laboratory run optimizing battery electrolytes.

Core claim

The authors establish that their LLM-Guided Bayesian Optimization framework, using a region-lifted preference mechanism to incorporate large language model suggestions into the surrogate model at every iteration, does not significantly underperform standard Bayesian optimization in the worst case while converging significantly faster when the preferences are aligned with the objective function. This is demonstrated through theoretical proofs and empirical results on benchmarks in physics, chemistry, biology, and materials science, including a wet-lab optimization of Fe-Cr battery electrolytes where the method reaches 90 percent of the best observed value in 6 iterations compared to more than

What carries the argument

The region-lifted preference mechanism, which adjusts the surrogate mean at each step by lifting LLM preferences into the optimization loop in a stable manner.

If this is right

  • High-dimensional scientific search problems gain quicker early progress because language model knowledge influences the surrogate from the first iteration onward.
  • Optimization tasks keep a safety guarantee that performance will stay close to standard Bayesian optimization even if the language model preferences are imperfect.
  • Laboratory work such as electrolyte design can identify high-performing candidates after roughly half the usual number of physical trials.
  • The same preference guidance produces measurable gains on benchmark problems drawn from physics, chemistry, biology, and materials science.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same continuous preference embedding might let researchers express goals in ordinary language and have those goals steer the search without manual rewriting of the objective.
  • Applying the approach to problems with several competing goals could let language models suggest balanced trade-offs across objectives at each step.
  • The worst-case bound offers a natural way to test noisy or occasionally incorrect language model advice without risking complete failure of the search.

Load-bearing premise

The region-lifted preference mechanism embeds LLM-driven preferences into every iteration in a stable and controllable way that shifts the surrogate mean without introducing instability or bias that would invalidate the convergence claims.

What would settle it

A replication of the Fe-Cr battery electrolyte experiment in which LGBO requires more than 10 iterations to reach 90 percent of the best observed value while standard Bayesian optimization reaches it sooner.

Figures

Figures reproduced from arXiv: 2605.17976 by Huan Xiong, Jianshu Zhang, Nanyang Ye, Qinying Gu, Xinzhe Yuan, Yuqiang Li, Zhuo Chen.

Figure 1
Figure 1. Figure 1: LGBO framework. The proposed LLM-Guided Bayesian Optimization integrates prior [PITH_FULL_IMAGE:figures/full_fig_p006_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: LNP3 results. Left: LGBO achieves faster convergence and higher final performance than GPBO and LLAMBO. Right: trajectory heatmaps show that LGBO converges more rapidly and consistently across seeds. 4.1 EXPERIMENT SETTING Baselines. Two representative methods are used for comparison: (i) GPBO, the canonical Gaussian-process Bayesian optimization framework with Matern-5/2 kernel; (ii) ´ LLAMBO (Liu et al.)… view at source ↗
Figure 3
Figure 3. Figure 3: Convergence traces on the three dry benchmark tasks. From left to right: [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Wet-lab experiment results. Left: convergence traces across iteration rounds, showing observed values and best objective performance. The hollow markers indicate the historical best so￾lutions observed up to that round. Right: 3D optimization trajectories in the chemical concentration space (Fe, Cr, additive), with color indicating the best- objective [PITH_FULL_IMAGE:figures/full_fig_p010_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Ablation study results. Left: convergence traces comparing LGBO, GPBO, and Random region lifting BO on the HPLC task. Middle and Right: Different LLM backbones ablation exper￾iment, where dots represent individual test runs and bars denote the corresponding means. Random region lifting. We replace LLM suggestions with randomly lifted regions of matched size and confidence. To eliminate warm-start effects, … view at source ↗
Figure 6
Figure 6. Figure 6: Pairplot of sampled points for GPBO on LNP3 (normalized space). [PITH_FULL_IMAGE:figures/full_fig_p020_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Pairplot of sampled points for LLAMBO on LNP3 (normalized space). [PITH_FULL_IMAGE:figures/full_fig_p020_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Pairplot of sampled points for LGBO (ours) on LNP3 (normalized space). [PITH_FULL_IMAGE:figures/full_fig_p021_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: LNP3 results. Left: LGBO achieves faster convergence and higher final performance than [PITH_FULL_IMAGE:figures/full_fig_p021_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Pairplot of sampled points for GPBO on HPLC (normalized space). [PITH_FULL_IMAGE:figures/full_fig_p022_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Pairplot of sampled points for LLAMBO on HPLC (normalized space). [PITH_FULL_IMAGE:figures/full_fig_p022_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Pairplot of sampled points for LGBO (ours) on HPLC (normalized space). [PITH_FULL_IMAGE:figures/full_fig_p023_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: HPLC results. Left: mean performance traces (mean [PITH_FULL_IMAGE:figures/full_fig_p023_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Cross Barrel results. Left: LGBO achieves faster convergence and higher final perfor [PITH_FULL_IMAGE:figures/full_fig_p024_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: Pairplot of sampled points for GPBO on Cross Barrel (normalized space). [PITH_FULL_IMAGE:figures/full_fig_p024_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: Pairplot of sampled points for LLAMBO on Cross Barrel (normalized space). [PITH_FULL_IMAGE:figures/full_fig_p025_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: Pairplot of sampled points for LGBO (ours) on Cross Barrel (normalized space). [PITH_FULL_IMAGE:figures/full_fig_p025_17.png] view at source ↗
Figure 18
Figure 18. Figure 18: Concrete results. Left: LGBO achieves faster convergence and higher final performance [PITH_FULL_IMAGE:figures/full_fig_p026_18.png] view at source ↗
Figure 19
Figure 19. Figure 19: Pairplot of sampled points for GPBO on Concrete (normalized space). [PITH_FULL_IMAGE:figures/full_fig_p026_19.png] view at source ↗
Figure 20
Figure 20. Figure 20: Pairplot of sampled points for LLAMBO on Concrete (normalized space). [PITH_FULL_IMAGE:figures/full_fig_p027_20.png] view at source ↗
Figure 21
Figure 21. Figure 21: Pairplot of sampled points for LGBO (ours) on Concrete (normalized space). [PITH_FULL_IMAGE:figures/full_fig_p027_21.png] view at source ↗
Figure 22
Figure 22. Figure 22: Performance comparison of LGBO against LLAMA, GPBO, ColaLLM and BOPRO on [PITH_FULL_IMAGE:figures/full_fig_p029_22.png] view at source ↗
Figure 23
Figure 23. Figure 23: Performance comparison of LGBO against LLAMA, GPBO, ColaLLM and BOPRO on [PITH_FULL_IMAGE:figures/full_fig_p030_23.png] view at source ↗
Figure 24
Figure 24. Figure 24: Performance comparison of LGBO against LLAMA, GPBO, ColaLLM and BOPRO on [PITH_FULL_IMAGE:figures/full_fig_p030_24.png] view at source ↗
Figure 25
Figure 25. Figure 25: Performance comparison of LGBO against LLAMA, GPBO, ColaLLM and BOPRO on [PITH_FULL_IMAGE:figures/full_fig_p031_25.png] view at source ↗
Figure 26
Figure 26. Figure 26: Performance comparison of LGBO against LLAMA, GPBO, ColaLLM and BOPRO on [PITH_FULL_IMAGE:figures/full_fig_p031_26.png] view at source ↗
read the original abstract

Scientific discovery is increasingly constrained by costly experiments and limited resources, underscoring the need for efficient optimization in AI for science. Bayesian Optimization (BO), though widely adopted for balancing exploration and exploitation, often exhibits slow cold-start performance and poor scalability in high-dimensional settings, limiting its applicability in real-world scientific problems. To overcome these challenges, we propose LLM-Guided Bayesian Optimization (LGBO), the first LLM preference-guided BO framework that continuously integrates the semantic reasoning of large language models (LLMs) into the optimization loop. Unlike prior works that use LLMs only for warm-start initialization or candidate generation, LGBO introduces a region-lifted preference mechanism that embeds LLM-driven preferences into every iteration, shifting the surrogate mean in a stable and controllable way. Theoretically, we prove that LGBO does not perform significantly worse than standard BO in the worst case, while achieving significantly faster convergence when preferences align with the objective. Empirically, LGBO consistently outperforms existing methods across diverse dry benchmarks in physics, chemistry, biology, and materials science. Most notably, in a new wet-lab optimization of Fe-Cr battery electrolytes, LGBO attains \textbf{90\% of the best observed value within 6 iterations}, whereas standard BO and existing LLM-augmented baselines require more than 10. Together, these results suggest that LGBO offers a promising direction for integrating LLMs into scientific optimization workflows.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript proposes LLM-Guided Bayesian Optimization (LGBO), a framework that continuously integrates LLM semantic reasoning into the BO loop via a novel region-lifted preference mechanism that shifts the surrogate mean at every iteration. It claims a theoretical guarantee that LGBO does not perform significantly worse than standard BO in the worst case while achieving faster convergence when preferences align, supported by empirical results on dry benchmarks across physics, chemistry, biology, and materials science, plus a wet-lab demonstration on Fe-Cr battery electrolyte optimization where LGBO reaches 90% of the best observed value in 6 iterations versus more than 10 for baselines.

Significance. If the central claims hold, the work offers a promising direction for embedding LLMs into scientific discovery pipelines to mitigate BO's cold-start and high-dimensional limitations. The inclusion of a new wet-lab case study and consistent outperformance on diverse benchmarks are notable strengths that could influence practical workflows in AI for science. The theoretical guarantee, if rigorously established, would provide a valuable safety net for adoption.

major comments (2)
  1. [Theoretical Analysis / Regret Proof] The worst-case guarantee (stated in the abstract and presumably derived in the theoretical section) asserts that LGBO does not perform significantly worse than standard BO. This rests on the region-lifted preference mechanism producing only a bounded perturbation to the surrogate mean that preserves sublinear regret. No derivation or explicit bound is given on the variance of LLM preference outputs (across prompt variations or temperature settings), which could violate the Lipschitz or concentration assumptions if variance grows with iteration count or dimension. This directly affects the load-bearing claim that the method 'does not perform significantly worse.'
  2. [Experimental Results (wet-lab case study)] Table or figure reporting the wet-lab Fe-Cr electrolyte results: the claim of attaining 90% of the best observed value within 6 iterations is presented without error bars, multiple independent runs, or an ablation on preference misalignment cases. This leaves the empirical support for both the faster-convergence claim and the 'does not perform significantly worse' guarantee incomplete.
minor comments (2)
  1. [Framework Description] The abstract and framework description refer to the preference mechanism as 'stable and controllable' without providing the explicit equation for the lift parameter or mean-shift update rule.
  2. [Related Work] Missing references to recent LLM-augmented BO baselines that also use preference or semantic guidance; ensure the related-work section distinguishes the region-lifted approach clearly.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback and for recognizing the potential of LGBO for integrating LLMs into scientific discovery pipelines. We address each major comment below and will revise the manuscript to incorporate the suggested clarifications and additions.

read point-by-point responses

  1. Referee: [Theoretical Analysis / Regret Proof] The worst-case guarantee (stated in the abstract and presumably derived in the theoretical section) asserts that LGBO does not perform significantly worse than standard BO. This rests on the region-lifted preference mechanism producing only a bounded perturbation to the surrogate mean that preserves sublinear regret. No derivation or explicit bound is given on the variance of LLM preference outputs (across prompt variations or temperature settings), which could violate the Lipschitz or concentration assumptions if variance grows with iteration count or dimension. This directly affects the load-bearing claim that the method 'does not perform significantly worse.'

    Authors: We appreciate the referee's scrutiny of the theoretical guarantee. The proof in Section 4 establishes that the region-lifted preference introduces a perturbation whose magnitude is controlled by a fixed bound on the deviation between LLM preferences and the true objective, which preserves the sublinear regret of standard BO. We agree, however, that an explicit treatment of variance across LLM prompt variations and temperature settings is not provided. In the revision we will add a dedicated paragraph deriving a concentration bound (via Hoeffding's inequality) on the LLM output variance under standard temperature ranges and prompt stability assumptions, showing that the variance remains independent of iteration count and dimension. This addition will make the worst-case claim fully rigorous while leaving the main result unchanged. revision: yes

  2. Referee: [Experimental Results (wet-lab case study)] Table or figure reporting the wet-lab Fe-Cr electrolyte results: the claim of attaining 90% of the best observed value within 6 iterations is presented without error bars, multiple independent runs, or an ablation on preference misalignment cases. This leaves the empirical support for both the faster-convergence claim and the 'does not perform significantly worse' guarantee incomplete.

    Authors: We thank the referee for identifying the gaps in the wet-lab presentation. The current manuscript reports results from a single experimental trajectory. In the revised version we will augment the relevant figure and table with error bars computed from five independent runs of the Fe-Cr electrolyte optimization. We will also add a new ablation subsection that systematically varies the degree of preference misalignment (by injecting controlled noise into the LLM outputs) and shows that performance remains no worse than standard BO. These changes will strengthen the empirical support for both faster convergence when preferences align and the worst-case guarantee. revision: yes

Circularity Check

0 steps flagged

No significant circularity in the derivation chain

full rationale

The paper introduces the LGBO framework via a region-lifted preference mechanism that embeds LLM preferences by shifting the surrogate mean in a described stable and controllable manner. It separately claims a theoretical proof establishing that LGBO does not perform significantly worse than standard BO in the worst case (with faster convergence conditional on alignment). This structure presents the performance guarantee as derived from the mechanism's properties rather than reducing to inputs by construction, fitted parameters renamed as predictions, or load-bearing self-citations. No equations or steps in the provided description exhibit self-definitional equivalence or imported uniqueness from prior author work. The derivation remains self-contained with independent theoretical content against external benchmarks.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 1 invented entities

The framework rests on the assumption that LLM preferences can be translated into a stable shift of the surrogate without violating BO regret bounds, plus standard BO assumptions on the objective function and kernel.

free parameters (1)
  • preference strength or lift parameter
    Controls how strongly LLM preference shifts the surrogate mean; value not specified in abstract but required for the mechanism.
axioms (1)
  • domain assumption LLM preferences can be mapped to regions that meaningfully align with or at least do not contradict the true objective in a controllable manner
    Invoked to obtain the faster-convergence result when preferences align.
invented entities (1)
  • region-lifted preference mechanism no independent evidence
    purpose: Embeds LLM semantic reasoning into every optimization iteration by shifting surrogate mean
    New construct introduced to achieve continuous integration rather than one-time use.

pith-pipeline@v0.9.0 · 5802 in / 1390 out tokens · 34095 ms · 2026-05-20T09:54:10.966527+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

35 extracted references · 35 canonical work pages · 2 internal anchors

  1. [1]

    International Conference on Machine Learning , pages=

    On kernelized multi-armed bandits , author=. International Conference on Machine Learning , pages=. 2017 , organization=

    work page 2017

  2. [2]

    Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design

    Gaussian process optimization in the bandit setting: No regret and experimental design , author=. arXiv preprint arXiv:0912.3995 , year=

    work page internal anchor Pith review Pith/arXiv arXiv

  3. [3]

    Progress in Materials Science , volume=

    Toward autonomous laboratories: Convergence of artificial intelligence and experimental automation , author=. Progress in Materials Science , volume=

    work page

  4. [4]

    Chemical Reviews , volume=

    Self-driving laboratories for chemistry and materials science , author=. Chemical Reviews , volume=

    work page

  5. [5]

    Accounts of Chemical Research , volume=

    Autonomous chemical experiments: Challenges and perspectives on establishing a self-driving lab , author=. Accounts of Chemical Research , volume=

    work page

  6. [6]

    Chemical Engineering Journal , volume=

    Robot-assisted optimized array design for accurate multi-component gas quantification , author=. Chemical Engineering Journal , volume=

    work page

  7. [7]

    ACS Sensors , volume=

    Customizable colorimetric sensor array via a high-throughput robot for mitigation of humidity interference in gas sensing , author=. ACS Sensors , volume=

    work page

  8. [8]

    Chimia , volume=

    Bayesian optimization for chemical reactions , author=. Chimia , volume=

    work page

  9. [9]

    Nature , volume=

    Bayesian reaction optimization as a tool for chemical synthesis , author=. Nature , volume=

    work page

  10. [10]

    Proceedings of the IEEE , volume=

    Taking the human out of the loop: A review of Bayesian optimization , author=. Proceedings of the IEEE , volume=

    work page

  11. [11]

    International Conference on Artificial Intelligence and Statistics , pages=

    No-regret algorithms for multi-task bayesian optimization , author=. International Conference on Artificial Intelligence and Statistics , pages=. 2021 , organization=

    work page 2021

  12. [12]

    International Conference on Machine Learning , pages=

    A framework for Bayesian optimization in embedded subspaces , author=. International Conference on Machine Learning , pages=

    work page

  13. [13]

    Machine Learning , volume=

    High-dimensional Bayesian optimization using low-dimensional feature spaces , author=. Machine Learning , volume=

    work page

  14. [14]

    Advances in Neural Information Processing Systems , volume=

    Learning search space partition for black-box optimization using Monte Carlo tree search , author=. Advances in Neural Information Processing Systems , volume=

    work page

  15. [15]

    arXiv preprint arXiv:2305.02614 , year=

    High-dimensional Bayesian optimization via semi-supervised learning with optimized unlabeled data sampling , author=. arXiv preprint arXiv:2305.02614 , year=

    work page arXiv

  16. [16]

    International Conference on Machine Learning , pages=

    Preferential bayesian optimization , author=. International Conference on Machine Learning , pages=. 2017 , organization=

    work page 2017

  17. [17]

    Large Language Models to Enhance Bayesian Optimization , author=

    work page

  18. [18]

    arXiv preprint arXiv:2410.10190 , year =

    Predicting from strings: Language model embeddings for Bayesian optimization , author=. arXiv preprint arXiv:2410.10190 , year=

    work page arXiv

  19. [19]

    arXiv preprint arXiv:2304.05341 , year=

    Bayesian optimization of catalysts with in-context learning , author=. arXiv preprint arXiv:2304.05341 , year=

    work page arXiv

  20. [20]

    Proceedings of the 18th ACM Conference on Recommender Systems , pages=

    Bayesian optimization with LLM-based acquisition functions for natural language preference elicitation , author=. Proceedings of the 18th ACM Conference on Recommender Systems , pages=. 2024 , doi=

    work page 2024

  21. [21]

    Chang, Chih-Yu and Azvar, Milad and Okwudire, Chinedum and Kontar, Raed Al , journal=

    work page

  22. [22]

    Proceedings of the 43rd IEEE/ACM International Conference on Computer-Aided Design , pages=

    Ado-llm: Analog design bayesian optimization with in-context learning of large language models , author=. Proceedings of the 43rd IEEE/ACM International Conference on Computer-Aided Design , pages=

    work page

  23. [23]

    arXiv preprint arXiv:2505.12833 , year=

    Reasoning BO: Enhancing Bayesian optimization with long-context reasoning power of LLMs , author=. arXiv preprint arXiv:2505.12833 , year=

    work page arXiv

  24. [24]

    Advances in Neural Information Processing Systems (NeurIPS) , year =

    Principled Bayesian Optimization in Collaboration with Human Experts , author =. Advances in Neural Information Processing Systems (NeurIPS) , year =

    work page

  25. [25]

    International Conference on Learning Representations (ICLR) , year =

    A General Framework for User-Guided Bayesian Optimization , author =. International Conference on Learning Representations (ICLR) , year =

    work page

  26. [26]

    arXiv preprint arXiv:2208.08742 , year =

    Bayesian Optimization Augmented with Actively Elicited Expert Knowledge , author =. arXiv preprint arXiv:2208.08742 , year =

    work page arXiv

  27. [27]

    A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning

    A tutorial on Bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning , author=. arXiv preprint arXiv:1012.2599 , year=

    work page internal anchor Pith review Pith/arXiv arXiv

  28. [28]

    Intern-s1: A scientific multimodal foundation model.arXiv preprint arXiv:2508.15763, 2025

    Intern-S1: A Scientific Multimodal Foundation Model , author =. arXiv preprint arXiv:2508.15763 , year =

    work page arXiv

  29. [29]

    Science advances , volume=

    A Bayesian experimental autonomous researcher for mechanical design , author=. Science advances , volume=. 2020 , publisher=

    work page 2020

  30. [30]

    Optimization of lipid nanoformulations for effective m

    Chen, Huiling and Ren, Xuan and Xu, Shi and Zhang, Dekui and Han, TiYun , journal=. Optimization of lipid nanoformulations for effective m. 2022 , publisher=

    work page 2022

  31. [31]

    Machine Learning: Science and Technology , volume=

    Olympus: a benchmarking framework for noisy optimization and experiment planning , author=. Machine Learning: Science and Technology , volume=. 2021 , publisher=

    work page 2021

  32. [32]

    2020 , howpublished =

    Pratham Tripathi , title =. 2020 , howpublished =

    work page 2020

  33. [33]

    The Thirteenth International Conference on Learning Representations , year=

    Searching for optimal solutions with LLMs via bayesian optimization , author=. The Thirteenth International Conference on Learning Representations , year=

    work page

  34. [34]

    arXiv preprint arXiv:2509.17998 , year=

    Adaptive Kernel Design for Bayesian Optimization Is a Piece of CAKE with LLMs , author=. arXiv preprint arXiv:2509.17998 , year=

    work page arXiv

  35. [35]

    Digital Discovery , year=

    Multi-fidelity Bayesian Optimization of Covalent Organic Frameworks for Xenon/Krypton Separations , author=. Digital Discovery , year=

    work page