Critical Minerals

AI/ML Approaches for Research and Development Application

Approaches
Use machine learning approaches based on known labels/expectations, usually for
  • Classification (e.g., Logistic, SVM)
  • Regression (e.g., Linear, Kernel Ridge)
  • Predict subsurface properties
  • Predict properties of fly ash-based zeolites.
  • Structured numeric data, text, image, maps, sequences, time-series data
  • Predictive and prescriptive critical mineral design tool
  • Predictive and decision support
  • Operation optimization
Approaches
Use machine learning approaches without known labels/expectations, usually for
  • Clustering
  • Dimension reduction
  • Identify similarities and characteristics within the data to group data into clusters
  • Reduce the dimensions and complexities of the data
  • Structured or unstructured data
  • Identify knowledge and information gaps
Approaches
Use complex neural network architecture for
  • Image and spatial classification
  • Time series regression
  • Natural language processing
  • Predict subsurface properties
  • Structured numeric data, text, image, maps, sequences, time-series data
  • Supply chain optimization
Approaches
Combine more than one machine learning models to handle the tasks
  • Random Forest
  • XGBoost
  • Committee Machine
  • Evaluate variable importance and influence
  • Predict potential document
  • Structured numeric data, text, image, maps, sequences, time-series data
  • Predictive and prescriptive operation optimization
  • Predictive decision support
Approaches

Integrate scientific equations and knowledge into machine learning algorithms (usually using Deep Learning), such as Physics-Informed Machine Learning

  • Predict upstream product behaviors
  • Forecast processing problems
  • Solve partial differential equations as generalized solver
  • Structured numeric data, text, image, maps, sequences, time-series data
  • Scientific equations, field equations, partial differential equations
  • Predictive and prescriptive operation optimization
  • Multi-scale and resolution prediction
  • Predictive decision support
Large Language Models for UNSTRUCTURED DATA MINING Large Language Models for GENERATIVE DISCOVERY Transfer Learning Science-based Learning Edge Computing Advance Sensor Reinforcement Learning Operator Learning
Characterize Materials
Develop Structural/Functional Materials
Enable Smart Components w/embedded sensors
Optimize regional Planning and Operations
Modeling/Simulation Bridging Scales
Accelerate Flow Sheet Initialization
Optimize Turbine Operations
Assess/Control H2 Combustion Characteristics