Modern science and industry generate vast amounts of complex data. Machine learning provides the tools to extract
meaningful insights, moving beyond traditional analysis to guide intelligent, data-driven decisions.
Deep Learning Architectures
Neural Networks (NN)
What it is: A powerful model inspired by the human brain, capable of learning highly complex, non-linear relationships in data.
Lab Application: Predict battery health (SOH) from voltage curves or estimate catalytic activity of new materials.
Convolutional Neural Networks (CNN)
What it is: A deep learning model specialized in processing grid-like data (e.g., images) to automatically extract spatial features.
Lab Application: Classify reaction mechanisms by converting cyclic voltammetry (CV) data into images.
Recurrent Neural Networks (RNN/LSTM)
What it is: An architecture optimized for learning from sequential data, capturing patterns over time.
Lab Application: Forecast battery degradation by learning from charge/discharge cycle data over time.
Graph Neural Networks (GNN)
What it is: A model that directly processes graph data, learning from the relationships between nodes and edges.
Lab Application: Predict the properties of a new electrocatalyst directly from its atomic structure.
Classical Supervised Learning
Support Vector Machine (SVM/SVR)
What it is: A powerful algorithm that finds an optimal boundary to classify data or predict a specific value.
Lab Application: Classify corrosion types from electrochemical noise or predict ion conductivity from impedance data.
Ensemble Methods (RF, GBM)
What it is: A technique that combines multiple simpler models to achieve higher accuracy and stability.
Lab Application: Identify key factors affecting catalyst performance from a large experimental database.
Gaussian Processes (GP)
What it is: A probabilistic model that provides a prediction and a measure of uncertainty, ideal for experiment optimization.
Lab Application: Guide an automated lab by suggesting which experiment to run next for maximum information gain.
Advanced & Unsupervised Learning
Transformers
What it is: Originally from NLP, this architecture excels at finding long-range dependencies in sequential data.
Lab Application: Generate novel molecules with desired properties by treating chemical structures like a language.
Unsupervised Learning (PCA, Clustering)
What it is: A class of algorithms that finds hidden patterns and structures in data without needing pre-defined labels.
Lab Application: Group large datasets of CV curves into clusters of similar mechanisms without prior knowledge.
Physics-Informed ML (PINN/SciML)
What it is: Hybrid models that embed known physical laws (e.g., differential equations) into the neural network architecture.
Lab Application: Model battery degradation more accurately by constraining the NN with known electrochemical equations.