Combinatorial Steiner Methodology for Efficiently Exploring Chemical Space in CO ₂ Hydrogenation to Fischer-Tropsch Reactivity via High-throughput Experimentation
- 1NCCR Catalysis
- 2Department of Chemistry and Applied Biosciences, ETH Zürich, Vladimir Prelog 2, Zurich, CH-8039
- 3Swiss Cat+ East, ETH Zürich
Abstract
High-throughput experimentation (HTE) and machine learning (ML) have accelerated discovery in molecular chemistry. Meanwhile, such advances in heterogeneous catalysis are hindered by the complexity of materials and the vast chemical space. We present a mathematically optimized methodology using Steiner coverings to explore multi-component catalysts and access lower-dimension interactions through higher-dimension blocks. From 21 elements, we designed a library of 70 trimetallic mixed-metal-oxide catalysts, covering all 210 possible bimetallic pairs. The experimental workflow was automated by a robotic platform and applied to thermocatalytic CO 2 hydrogenation, critical for achieving a circular carbon economy. In just 70 materials we successfully recovered and uncovered trends in methanation, reverse water–gas shift (RWGS), and methanol synthesis, while discovering a highly active FeGaRu catalyst for CO 2 –Fischer–Tropsch synthesis that retains this reactivity when simplified to binary FeRu, Ga providing improved performances. Our combinatorial optimization methodology replaces thousands of manual experiments with strategic exploration, improving the discovery rate for complex, multi-component heterogeneous catalysts.
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BibTeX
@misc{iwanojko2026combinatorial,
title = {{Combinatorial Steiner Methodology for Efficiently Exploring Chemical Space in CO ₂ Hydrogenation to Fischer-Tropsch Reactivity via High-throughput Experimentation}},
author = {Nestor Iwanojko and Erwin Lam and Christophe Copéret},
howpublished = {ChemRxiv},
year = {2026},
month = oct,
doi = {10.26434/chemrxiv.15007166/v2},
url = {https://chemrxiv.org/doi/full/10.26434/chemrxiv.15007166/v2},
note = {CC BY},
}
RIS
TY - UNPB
TI - Combinatorial Steiner Methodology for Efficiently Exploring Chemical Space in CO ₂ Hydrogenation to Fischer-Tropsch Reactivity via High-throughput Experimentation
AU - Nestor Iwanojko
AU - Erwin Lam
AU - Christophe Copéret
PY - 2026
DA - 2026/10/19
PB - ChemRxiv
DO - 10.26434/chemrxiv.15007166/v2
UR - https://chemrxiv.org/doi/full/10.26434/chemrxiv.15007166/v2
AB - High-throughput experimentation (HTE) and machine learning (ML) have accelerated discovery in molecular chemistry. Meanwhile, such advances in heterogeneous catalysis are hindered by the complexity of materials and the vast chemical space. We present a mathematically optimized methodology using Steiner coverings to explore multi-component catalysts and access lower-dimension interactions through higher-dimension blocks. From 21 elements, we designed a library of 70 trimetallic mixed-metal-oxide catalysts, covering all 210 possible bimetallic pairs. The experimental workflow was automated by a robotic platform and applied to thermocatalytic CO 2 hydrogenation, critical for achieving a circular carbon economy. In just 70 materials we successfully recovered and uncovered trends in methanation, reverse water–gas shift (RWGS), and methanol synthesis, while discovering a highly active FeGaRu catalyst for CO 2 –Fischer–Tropsch synthesis that retains this reactivity when simplified to binary FeRu, Ga providing improved performances. Our combinatorial optimization methodology replaces thousands of manual experiments with strategic exploration, improving the discovery rate for complex, multi-component heterogeneous catalysts.
ER -