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User Guide

This comprehensive guide covers all aspects of using ASCICat for multi-objective catalyst screening.

Overview

ASCICat provides a complete framework for:

  • Scoring catalysts on activity, stability, and cost
  • Ranking using customizable weight combinations
  • Visualizing results with high-quality figures
  • Analyzing sensitivity to weight choices

Guide Structure

  • Core Concepts

    Understand the theoretical foundation and key principles

    Core Concepts

  • Scoring System

    Detailed explanation of activity, stability, and cost scoring

    Scoring Overview

  • Reactions

    Configure HER, CO2RR, and custom reaction pathways

    Reactions

  • Visualization

    Generate high-quality figures and interactive plots

    Visualization

  • Sensitivity Analysis

    Analyze weight dependencies and ranking robustness

    Sensitivity

  • Data Format

    Prepare and validate your catalyst datasets

    Data Format

Quick Reference

The ASCI Formula

\[\phi_{ASCI} = w_a \cdot S_a(\Delta E) + w_s \cdot S_s(\gamma) + w_c \cdot S_c(C)\]

Score Definitions

Score Formula Interpretation
Activity \(S_a = \max(0, 1 - \|\Delta E - \Delta E_{opt}\| / \sigma_a)\) Proximity to Sabatier optimum
Stability \(S_s = (\gamma_{max} - \gamma) / (\gamma_{max} - \gamma_{min})\) Inverse surface energy
Cost \(S_c = (\log C_{max} - \log C) / (\log C_{max} - \log C_{min})\) Logarithmic cost penalty

Typical Weight Scenarios

Scenario \((w_a, w_s, w_c)\) Use Case
Equal (Default) (0.33, 0.33, 0.34) Unbiased exploratory screening
Activity-Focused (0.50, 0.30, 0.20) Performance-critical applications
Stability-Focused (0.30, 0.50, 0.20) Long-term durability required
Cost-Focused (0.30, 0.20, 0.50) Large-scale deployment

Supported Reactions

Reaction Pathway \(\Delta E_{opt}\) \(\sigma_a\)
HER H adsorption -0.27 eV 0.15 eV
CO2RR CO -0.67 eV 0.15 eV
CO2RR CHO -0.48 eV 0.15 eV
CO2RR COCOH -0.32 eV 0.15 eV

Key Classes

from ascicat import (
    ASCICalculator,    # Main calculation engine
    Visualizer,        # Figure generation
    Analyzer,          # Statistical analysis
    SensitivityAnalyzer,  # Weight sensitivity
    ReactionConfig,    # Reaction configuration
)

Workflow Summary

graph TD
    A[Initialize Calculator] --> B[Load Data]
    B --> C[Calculate ASCI]
    C --> D[Analyze Results]
    D --> E[Generate Figures]
    E --> F[Export Data]

    C --> G[Sensitivity Analysis]
    G --> E

Best Practices

Recommended Workflow

  1. Start with equal weights for unbiased initial screening
  2. Run sensitivity analysis to understand weight dependencies
  3. Identify robust candidates that rank well across weight ranges
  4. Document your weights and rationale for reproducibility

Common Pitfalls

  • Don't choose weights to favor a predetermined outcome
  • Don't ignore sensitivity analysis results
  • Don't compare rankings from different weight scenarios directly
  • Don't skip data validation

Getting Help