How can Machine Learning be integrated into traditional Catalysis research?
Integrating machine learning into traditional catalysis research involves several steps:
1. Data Collection and Curation: Compile high-quality datasets from experimental and computational sources. 2. Feature Engineering: Identify and create relevant features that can be used as inputs to ML models. 3. Model Training and Validation: Train machine learning models using historical data and validate their performance using test datasets. 4. Experimental Verification: Use model predictions to guide experimental work, validating the model's accuracy and refining it as necessary. 5. Iterative Feedback Loop: Continuously update the models with new experimental data to improve their predictive power.
