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Intelligent route to design efficient CO2 reduction electrocatalysts using ANFIS optimized by GA and PSO
Majedeh Gheytanzadeh1, Alireza Baghban2, Sajjad Habibzadeh3
1Surface Reaction and Advanced Energy Materials Laboratory, Chemical Engineering Department, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran.
Scientific Reports
|December 2, 2022
Summary
Machine learning models accurately predict CO2 electroreduction catalyst performance using d-band theory. This approach accelerates the discovery of efficient electrocatalysts for converting CO2 into valuable fuels, reducing emissions.
Area of Science:
- Electrochemistry
- Materials Science
- Computational Chemistry
Background:
- Electrochemical reduction of carbon dioxide (CO2) to fuels is a key strategy for mitigating CO2 emissions.
- Developing efficient electrocatalysts is crucial for this process, but experimental screening is costly and time-consuming.
- Computational methods, especially machine learning, offer a faster alternative for catalyst design.
Purpose of the Study:
- To develop and validate machine learning models for predicting electrocatalyst performance in CO2 reduction.
- To identify key descriptors for tuning electrocatalyst surface properties.
- To accelerate the discovery of novel alloy electrocatalysts for CO2 electroreduction.
Main Methods:
- Utilized Density Functional Theory (DFT) to calculate electronic features and intrinsic properties of electrocatalysts.
- Employed machine learning algorithms, specifically Adaptive Neuro-Fuzzy Inference System (ANFIS), combined with Particle Swarm Optimization (PSO) and Genetic Algorithm (GA).
- Used a dataset of 258 data points derived from DFT calculations as descriptors for the machine learning models.
Main Results:
- Developed ANFIS-PSO and ANFIS-GA models that accurately predict CO adsorption energy, a key metric for CO2 reduction efficiency.
- Achieved excellent performance with root-mean-square errors (RMSE) of 0.0411 for ANFIS-PSO and 0.0383 for ANFIS-GA, representing state-of-the-art accuracy.
- Sensitivity analysis identified d-band center and filling as the most influential parameters governing electrocatalyst surface reactivity.
Conclusions:
- Machine learning models, particularly ANFIS-PSO and ANFIS-GA, demonstrate high predictive power for electrocatalyst performance.
- The study highlights the potential of computational approaches to guide experimental efforts in designing efficient CO2 reduction electrocatalysts.
- d-band theory descriptors are effective for representing electrocatalyst surface properties and predicting reactivity.
