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[論文レビュー] Open Catalyst Experiments 2024 (OCx24): Bridging Experiments and Computational Models

Jehad Abed, Jiheon Kim|arXiv (Cornell University)|Nov 18, 2024
Catalytic Processes in Materials Science被引用数 17
ひとこと要約

OCx24 は多様な実験データセットを大規模に作成し、金属間ナノ粒子を対象とするAI加速型DFT吸着エネルギースクリーニングと結合させることで、HERとCO2RRの実験と計算触媒モデルを橋渡しする。

ABSTRACT

The search for low-cost, durable, and effective catalysts is essential for green hydrogen production and carbon dioxide upcycling to help in the mitigation of climate change. Discovery of new catalysts is currently limited by the gap between what AI-accelerated computational models predict and what experimental studies produce. To make progress, large and diverse experimental datasets are needed that are reproducible and tested at industrially-relevant conditions. We address these needs by utilizing a comprehensive high-throughput characterization and experimental pipeline to create the Open Catalyst Experiments 2024 (OCX24) dataset. The dataset contains 572 samples synthesized using both wet and dry methods with X-ray fluorescence and X-ray diffraction characterization. We prepared 441 gas diffusion electrodes, including replicates, and evaluated them using zero-gap electrolysis for carbon dioxide reduction (CO$_2$RR) and hydrogen evolution reactions (HER) at current densities up to $300$ mA/cm$^2$. To find correlations with experimental outcomes and to perform computational screens, DFT-verified adsorption energies for six adsorbates were calculated on $\sim$20,000 inorganic materials requiring 685 million AI-accelerated relaxations. Remarkably from this large set of materials, a data driven Sabatier volcano independently identified Pt as being a top candidate for HER without having any experimental measurements on Pt or Pt-alloy samples. We anticipate the availability of experimental data generated specifically for AI training, such as OCX24, will significantly improve the utility of computational models in selecting materials for experimental screening.

研究の動機と目的

  • AIが予測する性能と実験で観察される性能のギャップに対処することで、グリーン水素製造とCO2利用の触媒発見を促進する。
  • 再現性が高く、産業応用にも適した実験パイプラインを、多様な材料サンプルとともに提供する。
  • ハイスループット実験データと大規模計算スクリーニングを組み合わせて、実験結果の予測モデルを実現する。
  • 実験結果と計算記述子を結びつける前処理およびマッチング手法を開発する。

提案手法

  • 組成・構造の多様性を最大化するため、2つの手法（化学還元法とスパークアブレーション）を用いて572個の金属間ナノ粒子サンプルを合成する。
  • X線蛍光法(XRF)およびX線回折法(XRD)でサンプルを特徴づけ、単一相・標的構造材料を抽出する。
  • 改良型MEA系を用いて、CO2還元(CO2RR)と水素発生(HER)の電気化学性能を、産業上 relevant な電流密度300 mA/cm2までの範囲で評価する。
  • AdsorbMLパイプラインを用いて、AIとDFTリラクゼーションを組み合わせ、約19,406材料に渡る6つの中間種（OH, CO, CHO, C, COCOH, H）の吸着エネルギーを計算する（685 million relaxations; ~20 million DFT points）。
  • Build predictive models linking computational descriptors to experimental outcomes using linear and random forest regressions, with both surface-level and bulk descriptors; evaluate with LOOC and LOCO cross-validation.
Figure 1 : A summary of the computational and experimental screening efforts, and the resulting outcomes. Computationally, six adsorbates were used as descriptors and their adsorption energies were calculated across a wide swath of materials. Experimentally, two synthesis techniques were used to pre
Figure 1 : A summary of the computational and experimental screening efforts, and the resulting outcomes. Computationally, six adsorbates were used as descriptors and their adsorption energies were calculated across a wide swath of materials. Experimentally, two synthesis techniques were used to pre

実験結果

リサーチクエスチョン

  • RQ1Can AI-accelerated computed descriptors predict experimental HER and CO2RR performance across a diverse set of intermetallics?
  • RQ2How well do matched (XRD/XRF-confirmed) versus unmatched samples generalize to predict experimental outcomes?
  • RQ3What is the incremental predictive value of including adsorption-energy descriptors vs. bulk Matminer features for material-level performance?
  • RQ4Can a Sabatier-type volcano emerge from data without any Pt-containing training samples, and what does this imply for discovering low-cost catalysts?
  • RQ5How does dataset size affect the predictivity of models linking computation to experiment?

主な発見

  • A data-driven Sabatier volcano appears for HER with Pt at the apex, despite Pt not appearing in the training data.
  • Linear models using adsorption energies as features achieve R2≈0.59 (LOO) and ~0.61 (including more energies) for HER predictions, outperforming Matminer-only models in some cases.
  • For CO2RR, LOCO indicates weaker generalization (H2 and liquids show modest correlations; CO shows near-zero correlation).
  • Joint training on matched and unmatched samples improves predictive performance over using only matched samples.
  • Predicted HER catalysis candidates include low-cost, non-Pt compositions; a substantial set of materials (including Se-containing and Mo-based alloys) emerge as potential catalysts.
  • Model performance improves with larger training datasets, with projections suggesting substantial gains at 10^4–10^5 samples.
Figure 2 : This figure illustrates the experimental pipeline, detailing the process from synthesis to characterization and testing. (right) Cu baselines for the two synthesis techniques and a reference technique using spray coated nanoparticles. Similar trends across current densities are seen for a
Figure 2 : This figure illustrates the experimental pipeline, detailing the process from synthesis to characterization and testing. (right) Cu baselines for the two synthesis techniques and a reference technique using spray coated nanoparticles. Similar trends across current densities are seen for a

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