Reaction Analysis¶

The fr5.reaction_analysis module provides tools for analyzing and predicting chemical reaction outcomes. It uses machine learning models trained on experimental data to predict reaction conversion, selectivity, and other key performance metrics.

Overview¶

Chemical reaction analysis is crucial for:

Process optimization in chemical manufacturing
Catalyst screening and development
Reaction condition optimization (temperature, pressure, time)
Yield prediction for synthetic planning
Selectivity analysis for multi-product reactions
Mechanistic insights through data analysis

Key Features¶

🔬 Reaction Outcome Prediction¶

Predict key reaction metrics:

from fr5.reaction_analysis import predict_reaction_outcome

# Define reaction conditions
reaction_conditions = {
    'catalyst': 'Pd/C',
    'solvent': 'ethanol',
    'temperature': 80,        # °C
    'pressure': 1.0,          # atm
    'time': 4.0,              # hours
    'substrate_concentration': 0.1,  # M
    'catalyst_loading': 5.0   # mol%
}

# Get predictions
outcome = predict_reaction_outcome(
    reaction_type='hydrogenation',
    conditions=reaction_conditions,
    return_uncertainty=True
)

print(f"Predicted conversion: {outcome['conversion']:.1f}% ± {outcome['conversion_uncertainty']:.1f}%")
print(f"Predicted selectivity: {outcome['selectivity']:.1f}% ± {outcome['selectivity_uncertainty']:.1f}%")

⚗️ Reaction Optimization¶

Find optimal conditions using Bayesian optimization:

from fr5.reaction_analysis import optimize_reaction_conditions

# Define optimization space
optimization_space = [
    {'name': 'temperature', 'type': 'continuous', 'bounds': (20, 120)},  # °C
    {'name': 'pressure', 'type': 'continuous', 'bounds': (1, 10)},       # atm
    {'name': 'time', 'type': 'continuous', 'bounds': (0.5, 24)},         # hours
    {'name': 'catalyst', 'type': 'categorical', 'categories': ['Pd/C', 'Pt/C', 'Ru/C']},
    {'name': 'solvent', 'type': 'categorical', 'categories': ['ethanol', 'methanol', 'water']}
]

# Multi-objective optimization (maximize conversion and selectivity)
optimizer = optimize_reaction_conditions(
    reaction_type='hydrogenation',
    optimization_space=optimization_space,
    objectives=['conversion', 'selectivity'],
    initial_experiments=historical_data,
    n_iterations=20
)

# Get next experiments to run
next_experiments = optimizer.suggest_experiments(n_experiments=5)
print(f"Suggested experiments:\n{next_experiments}")

Supported Reaction Types¶

Hydrogenation Reactions¶

Predict outcomes for hydrogenation reactions:

from fr5.reaction_analysis.hydrogenation import HydrogenationPredictor

predictor = HydrogenationPredictor()

# Load pre-trained model
predictor.load_model('models/hydrogenation_catboost.pkl')

# Predict for alkene hydrogenation
result = predictor.predict(
    substrate='alkene',
    catalyst='Pd/C',
    solvent='ethanol',
    temperature=60,
    pressure=1.0,
    time=2.0
)

print(f"Conversion: {result['conversion']:.1f}%")
print(f"Rate constant: {result['rate_constant']:.2e} min⁻¹")

Cross-Coupling Reactions¶

Analyze Suzuki, Heck, and other cross-coupling reactions:

from fr5.reaction_analysis.cross_coupling import CrossCouplingPredictor

cc_predictor = CrossCouplingPredictor(reaction_type='suzuki')

# Predict Suzuki coupling outcome
result = cc_predictor.predict(
    aryl_halide='bromobenzene',
    boronic_acid='phenylboronic_acid',
    catalyst='Pd(PPh3)4',
    base='K2CO3',
    solvent='DME',
    temperature=80,
    time=6.0
)

print(f"Yield: {result['yield']:.1f}%")
print(f"Selectivity: {result['selectivity']:.1f}%")

Oxidation Reactions¶

Predict oxidation reaction outcomes:

from fr5.reaction_analysis.oxidation import OxidationPredictor

ox_predictor = OxidationPredictor()

result = ox_predictor.predict(
    substrate='alcohol',
    oxidant='TEMPO',
    solvent='water',
    ph=8.0,
    temperature=25,
    time=12.0
)

print(f"Conversion: {result['conversion']:.1f}%")
print(f"Product distribution: {result['product_distribution']}")

Data Analysis Tools¶

Reaction Database Analysis¶

Analyze reaction databases to extract insights:

from fr5.reaction_analysis import analyze_reaction_database

# Load reaction database
database_analysis = analyze_reaction_database(
    database_path='reaction_database.csv',
    reaction_type='hydrogenation',
    analysis_types=['conversion_trends', 'catalyst_comparison', 'solvent_effects']
)

# Generate analysis report
database_analysis.generate_report(
    output_path='reaction_analysis_report.html',
    include_plots=True
)

# Access specific analyses
catalyst_ranking = database_analysis.catalyst_performance_ranking()
solvent_effects = database_analysis.solvent_effect_analysis()

Kinetic Analysis¶

Analyze reaction kinetics from time-course data:

from fr5.reaction_analysis import analyze_kinetics

# Time-course data
time_points = [0, 0.5, 1, 2, 4, 8, 12, 24]  # hours
conversions = [0, 15, 28, 45, 65, 78, 85, 90]  # %

# Fit kinetic models
kinetic_analysis = analyze_kinetics(
    time=time_points,
    conversion=conversions,
    models=['first_order', 'second_order', 'langmuir_hinshelwood']
)

# Best fitting model
best_model = kinetic_analysis.best_model
print(f"Best model: {best_model['name']}")
print(f"Rate constant: {best_model['rate_constant']:.2e} min⁻¹")
print(f"R²: {best_model['r_squared']:.3f}")

Mechanistic Analysis¶

Extract mechanistic insights from reaction data:

from fr5.reaction_analysis import mechanistic_analysis

# Analyze mechanism from experimental data
mechanism = mechanistic_analysis(
    reaction_data=experimental_results,
    analysis_methods=['hammett_analysis', 'temperature_dependence', 'isotope_effects']
)

# Hammett analysis for electronic effects
hammett_results = mechanism.hammett_analysis(
    substituents=['H', 'CH3', 'CF3', 'NO2'],
    rate_constants=[1.0, 0.8, 2.1, 5.2]
)

print(f"Hammett ρ value: {hammett_results['rho']:.2f}")
print(f"Electronic effect: {hammett_results['interpretation']}")

Machine Learning Models¶

Model Training¶

Train custom models on your reaction data:

from fr5.reaction_analysis import train_reaction_model

# Prepare training data
training_data = prepare_reaction_training_data(
    experiments=experimental_database,
    features=['temperature', 'pressure', 'time', 'catalyst_loading'],
    targets=['conversion', 'selectivity']
)

# Train ensemble model
model = train_reaction_model(
    data=training_data,
    model_type='ensemble',  # 'catboost', 'random_forest', 'neural_network', 'ensemble'
    validation_method='time_series_split',  # Account for temporal dependencies
    hyperparameter_optimization=True
)

# Evaluate model performance
evaluation = model.evaluate(test_data)
print(f"Test RMSE: {evaluation['rmse']:.2f}")
print(f"Test R²: {evaluation['r2']:.3f}")

Feature Importance Analysis¶

Understand which factors most influence reaction outcomes:

from fr5.reaction_analysis import analyze_feature_importance

# Analyze what drives reaction success
importance_analysis = analyze_feature_importance(
    model=trained_model,
    feature_names=['temperature', 'pressure', 'catalyst_loading', 'time'],
    method='permutation_importance',
    n_repeats=10
)

# Plot feature importance
importance_analysis.plot_importance(
    top_n=15,
    title='Factors Affecting Reaction Conversion'
)

# SHAP analysis for interpretability
shap_values = importance_analysis.shap_analysis(
    test_samples=representative_experiments
)

Model Uncertainty¶

Quantify prediction uncertainty:

from fr5.reaction_analysis import uncertainty_quantification

# Quantify model uncertainty
uncertainty_analysis = uncertainty_quantification(
    model=trained_ensemble,
    test_data=new_experiments,
    methods=['ensemble_variance', 'bootstrap', 'conformal_prediction']
)

# High uncertainty predictions (may need experimental validation)
high_uncertainty_cases = uncertainty_analysis.get_high_uncertainty_predictions(
    threshold=0.1  # 10% uncertainty threshold
)

print(f"Found {len(high_uncertainty_cases)} high-uncertainty predictions")

Optimization Strategies¶

Design of Experiments (DoE)¶

Generate optimal experimental designs:

from fr5.reaction_analysis import design_experiments

# Create optimal experimental design
doe = design_experiments(
    variables=[
        {'name': 'temperature', 'type': 'continuous', 'bounds': (20, 100)},
        {'name': 'time', 'type': 'continuous', 'bounds': (1, 24)},
        {'name': 'catalyst', 'type': 'categorical', 'categories': ['A', 'B', 'C']}
    ],
    design_type='optimal',  # 'factorial', 'central_composite', 'optimal'
    n_experiments=20,
    objectives=['conversion', 'selectivity'],
    constraints={'temperature': 'minimize_energy_cost'}
)

experimental_plan = doe.generate_experiments()
print(f"Generated {len(experimental_plan)} optimized experiments")

Multi-Objective Optimization¶

Balance multiple competing objectives:

from fr5.reaction_analysis import multi_objective_optimization

# Optimize for yield, selectivity, and cost simultaneously
pareto_optimization = multi_objective_optimization(
    reaction_data=historical_experiments,
    objectives=[
        {'name': 'conversion', 'direction': 'maximize', 'weight': 0.4},
        {'name': 'selectivity', 'direction': 'maximize', 'weight': 0.4},
        {'name': 'cost', 'direction': 'minimize', 'weight': 0.2}
    ],
    constraints={
        'temperature': {'max': 150},  # Equipment limitation
        'pressure': {'max': 5}        # Safety constraint
    }
)

# Get Pareto front
pareto_front = pareto_optimization.get_pareto_front()
pareto_optimization.plot_pareto_front(save_path='pareto_analysis.png')

Reaction Screening¶

High-Throughput Analysis¶

Analyze high-throughput screening data:

from fr5.reaction_analysis import analyze_hts_data

# Analyze 96-well plate screening results
hts_analysis = analyze_hts_data(
    plate_data=screening_results,
    plate_layout=plate_map,
    analysis_methods=['hit_identification', 'dose_response', 'structure_activity']
)

# Identify hit compounds
hits = hts_analysis.identify_hits(
    success_criteria={'conversion': '>70%', 'selectivity': '>90%'},
    statistical_significance=0.05
)

print(f"Identified {len(hits)} hit conditions")

Parallel Reaction Analysis¶

Analyze parallel reaction arrays:

from fr5.reaction_analysis import analyze_parallel_reactions

# Process parallel reaction data
parallel_analysis = analyze_parallel_reactions(
    reaction_array=reaction_grid,
    variables=['catalyst', 'temperature', 'solvent'],
    responses=['yield', 'ee'],  # Enantiomeric excess
    visualization='heatmap'
)

# Find optimal conditions
optimal_conditions = parallel_analysis.find_optimal(
    optimization_criteria={'yield': 'max', 'ee': '>95%'}
)

Data Integration¶

Literature Data Mining¶

Extract reaction data from literature:

from fr5.reaction_analysis import mine_literature_data

# Extract reaction data from papers
literature_data = mine_literature_data(
    search_terms=['suzuki coupling', 'palladium catalyst'],
    databases=['reaxys', 'scifinder', 'patents'],
    extraction_methods=['table_parsing', 'text_mining']
)

# Clean and standardize data
standardized_data = literature_data.standardize_conditions()
validated_data = literature_data.validate_chemistry()

Patent Analysis¶

Analyze reaction data from patents:

from fr5.reaction_analysis import analyze_patent_data

# Extract and analyze patent reaction data
patent_analysis = analyze_patent_data(
    patent_numbers=['US1234567', 'EP9876543'],
    extraction_focus=['reaction_conditions', 'yields', 'catalysts']
)

# Identify trends and opportunities
trend_analysis = patent_analysis.identify_trends(
    time_window='2015-2024',
    technology_areas=['catalysis', 'green_chemistry']
)

Visualization Tools¶

Reaction Mapping¶

Visualize reaction networks and pathways:

from fr5.reaction_analysis.visualization import plot_reaction_network

# Create reaction network diagram
plot_reaction_network(
    reactions=reaction_database,
    layout='hierarchical',
    node_size_by='frequency',
    edge_weight_by='yield',
    highlight_pathways=['high_yield', 'green_chemistry']
)

Condition-Performance Maps¶

Create heat maps of reaction performance:

from fr5.reaction_analysis.visualization import plot_condition_heatmap

# Temperature vs. time performance map
plot_condition_heatmap(
    data=experimental_results,
    x_axis='temperature',
    y_axis='time', 
    z_axis='conversion',
    contour_levels=10,
    title='Conversion Heat Map'
)

Time-Course Analysis¶

Visualize reaction progress over time:

from fr5.reaction_analysis.visualization import plot_time_course

# Plot multiple reaction profiles
plot_time_course(
    time_course_data=kinetic_experiments,
    group_by='catalyst',
    show_fits=True,
    confidence_intervals=True
)

Best Practices¶

1. Data Quality¶

Validate experimental data before analysis
Check for outliers and inconsistencies
Standardize reaction conditions and nomenclature

2. Model Selection¶

Use appropriate models for your data size and type
Validate models on held-out test sets
Consider ensemble methods for robust predictions

3. Uncertainty Quantification¶

Always report prediction uncertainties
Use uncertainty to guide experimental planning
Validate high-uncertainty predictions experimentally

4. Interpretability¶

Use feature importance analysis to understand models
Apply SHAP or LIME for local explanations
Connect model insights to chemical intuition

5. Experimental Design¶

Use DoE principles for efficient experiments
Balance exploration and exploitation in optimization
Consider practical constraints in experimental planning

Example: Complete Reaction Optimization¶

import pandas as pd
from fr5.reaction_analysis import *

# 1. Load and analyze historical data
historical_data = pd.read_csv('hydrogenation_database.csv')

# 2. Train predictive model
model = train_reaction_model(
    data=historical_data,
    model_type='ensemble',
    targets=['conversion', 'selectivity']
)

# 3. Set up optimization
optimization_space = [
    {'name': 'temperature', 'type': 'continuous', 'bounds': (20, 100)},
    {'name': 'pressure', 'type': 'continuous', 'bounds': (1, 10)},
    {'name': 'catalyst_loading', 'type': 'continuous', 'bounds': (0.1, 10)},
    {'name': 'catalyst', 'type': 'categorical', 'categories': ['Pd/C', 'Pt/C', 'Ru/C']}
]

# 4. Run Bayesian optimization
optimizer = optimize_reaction_conditions(
    reaction_type='hydrogenation',
    optimization_space=optimization_space,
    objectives=['conversion', 'selectivity'],
    initial_experiments=historical_data,
    n_iterations=15
)

# 5. Get final recommendations
best_conditions = optimizer.get_best_conditions(
    criteria={'conversion': '>95%', 'selectivity': '>90%'}
)

print("Optimal reaction conditions:")
for param, value in best_conditions.items():
    print(f"  {param}: {value}")

# 6. Visualize optimization progress
optimizer.plot_optimization_progress(save_path='optimization_results.png')

This comprehensive reaction analysis module enables data-driven optimization of chemical reactions, from initial screening to process development.