Electrochemical Reaction Optimization

How can machine learning algorithms be used to optimize electrochemical reactions?

In the realm of chemical research and development, finding efficient and sustainable methods to optimize electrochemical reactions is a crucial task. These reactions play a vital role in various industries, such as energy storage, materials synthesis, and pharmaceuticals.

With the advent of machine learning algorithms, a new frontier in electrochemical optimization has emerged, enabling scientists to enhance reaction outcomes and unearth novel possibilities in this field. There are various machine learning algorithms that can be used for electrochemical reaction optimization, including Bayesian optimization method, artificial neural networks (ANN), Gaussian process regression (GPR), random forests (RF), support vector machines (SVM), and extremely randomized tree (ERT), etc.

ERT + AdaBoost algorithms predict corresponding electrochemical properties. [1]

What are our machine learning algorithms?

At Alfa Chemistry, our machine learning algorithms serve as powerful tools to unravel the intricate dynamics involved in electrochemical reactions. By leveraging vast repositories of data, including experimental results, analytical reports, and computational simulations, our algorithms can identify patterns, correlations, and potential optimization strategies that were previously elusive to traditional methods. Here is a brief introduction to several of our featured algorithms:

• Bayesian Optimization Algorithm (BOA)

Our BOA platform is designed to efficiently optimize electrochemical reactions by iteratively exploring the reaction parameter space to identify the optimal conditions. It employs a combination of statistical models, optimization techniques, and machine learning algorithms to effectively guide the search, minimizing the number of experiments required while maximizing the information gained from each trial.

Technical Features

• Data-Driven Approach
• Efficient Exploration
• Parallelization
• Iterative Improvement
• Cost Reduction-fewer experiments reduce time and labor costs.
• Easily visualize and explore complex reaction trends.
• Start your analysis with as few as 5 data points.

• ANN Simulation

Our ANNs are trained on vast datasets comprising multiple reaction parameters, such as electrode potentials, catalyst properties, electrolyte compositions, and reaction conditions. This allows our algorithms to capture intricate relationships between variables and model reaction mechanisms accurately.

Application Case

In the organic electrosynthesis process, a 2-layer feedforward ANN consisting of 9 hidden neurons can be constructed. Trained using 16 experimental data points and 70/15/15% data split for training, testing and validation respectively. In this study, the Levenberg-Marquardt learning algorithm was used as a training function to optimize performance in terms of root mean square error and overall data fit. Using experimentally obtained data to train the ANN increased selectivity to 325% and adiponitrile (ADN) electrosynthesis productivity to 30%.

Prediction of ADN production rates. [2]

Advantages of Our ML Algorithm Platform

• Efficient and Fast Optimization
  Alfa Chemistry leverages proprietary machine-learning algorithms to learn from your data and guide you through the optimization path.
• Accurate Predictions
  The ML algorithms used by Alfa Chemistry are designed to autocorrect themselves, learning from initial data and suggesting subsequent experiments to confirm, adapt, and correct predicted trends.
• Wider Applicable Types
  In addition to electrochemical reaction optimization, our algorithm platform can also be expanded to a wider range of reaction types and applications, such as catalytic processes, formulation optimization, material property prediction, etc.
• Data Security
  Alfa Chemistry implements best practices to protect your data, ensuring its safety and privacy.

References

• Kyoungmin Min, et al. Scientific reports, 2018, 8(1): 15778.
• Daniela E. Blanco, et al. Proceedings of the National Academy of Sciences, 2019, 116(36): 17683-17689.