Combining Hammett σ Constants for Improved Machine Learning and Catalyst Discovery in Homogeneous Organometallic Catalysis

Core Concepts

This research demonstrates a novel approach to catalyst discovery by combining Hammett σ constants with machine learning, specifically for the Suzuki-Miyaura cross-coupling reaction, leading to the identification of promising, cost-effective catalyst candidates.

Abstract

Bibliographic Information:

Rakotonirina, V.D., Bragato, M., Heinen, S., & von Lilienfeld, O.A. (2024). Combining Hammett σ constants for ∆-machine learning and catalyst discovery. [Journal Name Not Provided].

Research Objective:

This study investigates the effectiveness of combining Hammett σ constants with machine learning (∆-ML) to predict relative substrate binding energies in homogeneous organometallic catalysis, specifically for the Suzuki-Miyaura (SM) cross-coupling reaction. The goal is to develop a computationally efficient method for catalyst discovery and ligand tuning.

Methodology:

The researchers employed a combination rule-enhanced Hammett-inspired product model (cHIP) to partition the contributions of metals and ligands in organometallic catalysts. They utilized two datasets: DB1, containing relative binding energies for the oxidative addition step in the SM reaction, and DB2, containing relative binding free energies for all three intermediate steps. The cHIP model was used as a baseline for ∆-ML with Kernel Ridge Regression (KRR) to improve prediction accuracy.

Key Findings:

• The cHIP model, incorporating an additive combination rule for ligand effects, demonstrated promising predictive power for relative binding energies, comparable to density functional approximations.
• Combining cHIP with ∆-ML significantly improved prediction accuracy, reaching chemical accuracy (∼1 kcal/mol) with approximately 20,000 training instances.
• Applying cHIP to a smaller dataset (DB2) enabled the prediction of relative binding free energy changes for 720 new catalysts (DB3).
• This combinatorial approach identified 145 promising catalyst candidates, including several cost-effective Ni-based catalysts, such as Aphos-Ni-P(t-Bu)3.

Main Conclusions:

The study highlights the efficacy of combining Hammett σ constants with machine learning for catalyst discovery. The proposed cHIP model, particularly when used as a baseline for ∆-ML, offers a computationally efficient and accurate method for predicting relative binding energies and identifying promising catalyst candidates.

Significance:

This research contributes to the field of computational catalysis by providing a novel approach for catalyst design and optimization. The ability to predict catalyst performance based on readily available parameters like Hammett σ constants has the potential to accelerate the discovery of new and improved catalysts for various chemical reactions.

Limitations and Future Research:

Further research is needed to investigate the effect of steric hindrance and specific ligand environments on the accuracy of the cHIP model. Additionally, extending this approach to other catalytic reactions and complexes with more than two ligands would broaden its applicability.

Stats

The cHIP model achieved a mean absolute error (MAE) of ~3.4 kcal/mol for DB1.
The naive HIP model, using global σs, had an MAE of ~2.5 kcal/mol.
∆-ML with cHIP as a baseline reached chemical accuracy (MAE ~1 kcal/mol) with ~20k training instances.
cHIP predicted ligand effects for 120 new ligand combinations from 16 single ligand effects.
145 new catalyst candidates displayed oxidative addition relative binding free energies ranging from -34.0 to 17.0 kcal/mol, an optimal range identified in previous research.
Aphos-Ni-P(t-Bu)3, the most cost-effective catalyst identified, represents about 67% of the cost of the least expensive catalyst in DB2.

Quotes

"This method facilitates computational ligand tuning through binding energy predictions and their implementation into volcano plots."
"Despite the advances, these models often require extensive computations for each catalyst, highlighting the need for a combinatorial strategy that can efficiently explore the catalyst space by integrating the contributions of various building blocks, such as ligands and metals, to optimize performance."
"This combinatorial approach revealed several Ni-based catalysts approaching the top of the volcano after ligand tuning, despite the initially strong-binding nature of Ni."

Key Insights Distilled From

Combining Hammett $\sigma$ constants for $\Delta$-machine learning and catalyst discovery

by V. Diana Rak... at arxiv.org 10-08-2024

https://arxiv.org/pdf/2405.07747.pdf

Deeper Inquiries

How might this approach be adapted for other types of chemical reactions beyond cross-coupling reactions?

This approach, centered around combining Hammett σ constants with ∆-machine learning, holds promising potential for adaptation to various chemical reactions beyond cross-coupling reactions. The core principle lies in leveraging the ability of Hammett parameters to capture the electronic effects of substituents on a reaction center. Here's a breakdown of how this adaptability can be achieved:

Reaction Types: The method can be extended to reactions where substituent electronic effects play a significant role. Prime candidates include:

Nucleophilic substitutions (SN1, SN2): Similar to the paper's example of predicting activation energies in SN2 reactions, the approach can be applied to other substitution reactions where electronic effects influence reaction rates.
Electrophilic aromatic substitutions: The original application of Hammett parameters focused on benzene derivatives. This method could be readily applied to predict reactivity in various electrophilic substitutions on aromatic systems.
Addition reactions: Reactions involving carbonyls or other electron-deficient groups could be analyzed using this approach, as substituents can significantly impact the electrophilicity of these centers.

Dataset Requirements: A key requirement is the availability of datasets containing:

Relative binding energies or reaction rates: These serve as the target properties for the model.
Structural information of catalysts/reactants: This is crucial for calculating descriptors or representations used in machine learning.

Model Adaptation:

Redefining σ and ρ: The interpretation of σ and ρ might need adjustments depending on the reaction. For instance, in reactions involving Lewis acidity, ρ might reflect the metal center's Lewis acidity rather than just its electronic effect.
Incorporating Other Descriptors: While Hammett parameters capture electronic effects, other descriptors might be necessary to account for steric effects, solvent effects, or specific interactions. These could be integrated into the machine learning model alongside the cHIP predictions.

Beyond Catalysis: This approach could extend beyond catalysis to areas like:

Materials science: Predicting properties of materials based on the electronic effects of their constituent components.
Drug design: Analyzing the relationship between substituents on drug molecules and their binding affinities to target proteins.

In essence, the adaptability hinges on identifying reactions where electronic effects are significant and having suitable datasets. The combination of cHIP with ∆-machine learning offers a powerful framework for data-efficient exploration of chemical space.

Could the reliance on Hammett parameters limit the applicability of this method to systems where steric effects are dominant?

Yes, the reliance on Hammett parameters, which primarily capture electronic effects, can indeed limit the applicability of this method to systems where steric effects are dominant.
Here's why:

Nature of Hammett Parameters: Hammett σ constants are derived from the electronic influence of substituents on the equilibrium or rate of a reaction. They do not inherently account for the physical size or shape of the substituents.

Steric Hindrance: In reactions where the spatial arrangement of atoms around the reaction center is crucial, steric hindrance can significantly impact the reaction outcome. Hammett parameters alone would fail to capture these effects.

Limitations Highlighted in the Paper: The paper itself acknowledges this limitation, stating that the outliers in their model (phosphorus-containing ligands) might be due to the Hammett equation's inadequacy in describing sterically hindered systems.
Addressing Steric Effects:
To extend this method to systems with significant steric effects, several strategies can be employed:

Incorporating Steric Descriptors:

Taft Parameters: These parameters (Es) are specifically designed to quantify steric effects.
Charton Parameters: These parameters offer a more sophisticated approach to separate steric and electronic effects.
Sterimol Parameters: These parameters, based on geometric measurements of substituents, provide a detailed description of their shape and size.

Hybrid Descriptors: Combining Hammett parameters with steric descriptors within the machine learning model can create a more comprehensive representation of the system.

Advanced Machine Learning Techniques: Employing more sophisticated machine learning algorithms, such as graph neural networks, can potentially learn complex relationships between structure and properties, implicitly capturing steric effects.

In conclusion, while the current method relying solely on Hammett parameters might not be suitable for sterically dominated systems, incorporating steric descriptors and advanced machine learning techniques can broaden its applicability.

What are the broader implications of using machine learning and data-driven approaches for scientific discovery in chemistry and beyond?

The use of machine learning and data-driven approaches signifies a paradigm shift in scientific discovery, extending far beyond chemistry. Here are some broader implications:

Accelerated Discovery:

Efficient Data Analysis: ML excels at analyzing vast datasets, identifying patterns and correlations that might elude human researchers. This accelerates the analysis of experimental results and the identification of promising leads.
Predictive Power: ML models, trained on existing data, can predict properties of new compounds, materials, or systems, guiding experimental efforts and reducing reliance on trial-and-error approaches.

Breaking Complexity Barriers:

Handling Complex Systems: Traditional methods often struggle with complex systems involving numerous variables and intricate interactions. ML can unravel these complexities, leading to a deeper understanding of the underlying principles.
Multidisciplinary Research: ML facilitates the integration of data from various disciplines, fostering collaborations and enabling holistic approaches to scientific problems.

Data-Driven Experimentation:

Optimizing Experiments: ML can guide experimental design, suggesting the most informative experiments to conduct, thus maximizing resource utilization and accelerating the scientific process.
Autonomous Labs: The integration of ML with automated experimental platforms paves the way for self-driving labs, where experiments are designed, executed, and analyzed with minimal human intervention.

Beyond Chemistry:

Materials Science: Discovering new materials with tailored properties for applications in energy, electronics, and beyond.
Drug Discovery: Accelerating the identification and development of new drugs by predicting drug-target interactions and optimizing drug candidates.
Medicine: Improving disease diagnosis, personalized medicine, and drug development through analysis of patient data and medical images.
Environmental Science: Modeling climate change, predicting natural disasters, and developing sustainable solutions.

Challenges and Considerations:

Data Quality and Bias: ML models are only as good as the data they are trained on. Ensuring data quality and addressing potential biases is crucial.
Interpretability and Explainability: Understanding the reasoning behind ML predictions is essential for building trust and gaining scientific insights.
Ethical Considerations: As with any powerful technology, ethical considerations regarding data privacy, algorithmic bias, and responsible use of ML need careful attention.

In conclusion, machine learning and data-driven approaches are transforming scientific discovery by accelerating research, breaking complexity barriers, and fostering data-driven experimentation. This paradigm shift holds immense potential to address global challenges and advance our understanding of the world around us.