Combining Hammett $σ$ constants for $Δ$-machine learning and catalyst discovery
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866915124731707392 |
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| author | Rakotonirina, V. Diana Bragato, Marco Heinen, Stefan von Lilienfeld, O. Anatole |
| author_facet | Rakotonirina, V. Diana Bragato, Marco Heinen, Stefan von Lilienfeld, O. Anatole |
| contents | We study the applicability of the Hammett-inspired product (HIP) Ansatz to model relative substrate binding within homogenous organometallic catalysis, assigning $σ$ and $ρ$ to ligands and metals, respectively. Implementing an additive combination (c) rule for obtaining $σ$ constants for any ligand pair combination results in a cHIP model that enhances data efficiency in computational ligand tuning. We show its usefulness (i) as a baseline for $Δ$-machine learning (ML), and (ii) to identify novel catalyst candidates via volcano plots. After testing the combination rule on Hammett constants previously published in the literature, we have generated numerical evidence for the Suzuki-Miyaura (SM) C-C cross-coupling reaction using two synthetic datasets of metallic catalysts (including (10) and (11)-metals Ni, Pd, Pt, and Cu, Ag, Au as well as 96 ligands such as N-heterocyclic carbenes, phosphines, or pyridines). When used as a baseline, $Δ$-ML prediction errors of relative binding decrease systematically with training set size and reach chemical accuracy ($\sim$1 kcal/mol) for 20k training instances. Employing the individual ligand constants obtained from cHIP, we report relative substrate binding for a novel dataset consisting of 720 catalysts (not part of training data), of which 145 fall into the most promising range on the volcano plot accounting for oxidative addition, transmetalation, and reductive elimination steps. Multiple Ni-based catalysts, e.g. Aphos-Ni-P($t$-Bu)$_3$, are included among these promising candidates, potentially offering dramatic cost savings in experimental applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_07747 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Combining Hammett $σ$ constants for $Δ$-machine learning and catalyst discovery Rakotonirina, V. Diana Bragato, Marco Heinen, Stefan von Lilienfeld, O. Anatole Chemical Physics We study the applicability of the Hammett-inspired product (HIP) Ansatz to model relative substrate binding within homogenous organometallic catalysis, assigning $σ$ and $ρ$ to ligands and metals, respectively. Implementing an additive combination (c) rule for obtaining $σ$ constants for any ligand pair combination results in a cHIP model that enhances data efficiency in computational ligand tuning. We show its usefulness (i) as a baseline for $Δ$-machine learning (ML), and (ii) to identify novel catalyst candidates via volcano plots. After testing the combination rule on Hammett constants previously published in the literature, we have generated numerical evidence for the Suzuki-Miyaura (SM) C-C cross-coupling reaction using two synthetic datasets of metallic catalysts (including (10) and (11)-metals Ni, Pd, Pt, and Cu, Ag, Au as well as 96 ligands such as N-heterocyclic carbenes, phosphines, or pyridines). When used as a baseline, $Δ$-ML prediction errors of relative binding decrease systematically with training set size and reach chemical accuracy ($\sim$1 kcal/mol) for 20k training instances. Employing the individual ligand constants obtained from cHIP, we report relative substrate binding for a novel dataset consisting of 720 catalysts (not part of training data), of which 145 fall into the most promising range on the volcano plot accounting for oxidative addition, transmetalation, and reductive elimination steps. Multiple Ni-based catalysts, e.g. Aphos-Ni-P($t$-Bu)$_3$, are included among these promising candidates, potentially offering dramatic cost savings in experimental applications. |
| title | Combining Hammett $σ$ constants for $Δ$-machine learning and catalyst discovery |
| topic | Chemical Physics |
| url | https://arxiv.org/abs/2405.07747 |