ascicat 1.0.0

Activity-Stability-Cost Integrated Catalyst Discovery Framework

Activity-Stability-Cost Integrated Catalyst Discovery

A unified multi-objective framework for translating computational catalyst data into reproducible, experimentally-actionable rankings

Installation • Quick Start • Features • Documentation • Contributing

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Overview

The computational catalysis community has generated massive ML/DFT databases (OpenCatalyst, Materials Project, Catalysis-Hub, NOMAD, AFLOW) containing thousands of calculated catalyst properties. However, there exists no standardized framework to translate this wealth of data into actionable experimental priorities.

ASCICat bridges this gap by providing a unified, transparent, and reproducible framework for multi-objective catalyst prioritization.

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The Problem We Solve

Current Challenge	ASCICat Solution
No unified framework for catalyst selection	Standardized ASCI metric applicable to any catalyst dataset
Ad-hoc, non-reproducible selection criteria	Transparent weighting with explicit trade-off documentation
Results cannot be compared across studies	Common metric enables direct cross-study comparison
Hidden assumptions in catalyst ranking	Built-in sensitivity analysis reveals weight dependencies

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Scientific Framework

ASCICat implements a three-pillar scoring system grounded in fundamental catalysis principles:

Activity (Sa) Sabatier Principle Optimal binding energy for reaction kinetics

Stability (Ss) Surface Thermodynamics Dissolution resistance and durability

Cost (Sc) Economic Viability Material pricing and availability

The Unified ASCI Metric:

φ_ASCI = w_a · S_a + w_s · S_s + w_c · S_c

where:  w_a + w_s + w_c = 1
        S_a, S_s, S_c ∈ [0, 1]

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Installation

From Source

# Clone the repository
git clone https://github.com/NabKh/ASCICat.git
cd ASCICat

# Install in development mode
pip install -e .

Optional Dependencies

# For GUI interface
pip install -e ".[gui]"

# For interactive Jupyter visualizations
pip install -e ".[interactive]"

# For full development environment
pip install -e ".[dev]"

Requirements

• Python 3.8+
• NumPy, Pandas, SciPy, Matplotlib, Seaborn
• See setup.py for complete dependency list

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Quick Start

Python API

from ascicat import ASCICalculator

# Initialize calculator for HER reaction
calc = ASCICalculator(reaction='HER')

# Load your DFT data
calc.load_data('data/HER_clean.csv')

# Calculate ASCI scores with custom weights
results = calc.calculate_asci(
    w_a=0.4,  # 40% weight on Activity
    w_s=0.3,  # 30% weight on Stability
    w_c=0.3   # 30% weight on Cost
)

# Get top-ranked catalysts
top_catalysts = calc.get_top_catalysts(n=10)
print(top_catalysts[['symbol', 'ASCI', 'activity_score', 'stability_score', 'cost_score']])

Output:

      symbol     ASCI  activity_score  stability_score  cost_score
0      Fe2Sb4   0.899           0.923            0.851       0.924
1       Cu3Sb   0.887           0.912            0.867       0.883
2      Cu6Sb2   0.876           0.889            0.856       0.882
...

Command-Line Interface

# HER screening with default weights
ascicat --reaction HER --data data/HER_clean.csv --output results/

# CO2RR screening with custom weights
ascicat --reaction CO2RR --pathway CO --weights 0.5 0.3 0.2 --output results/

# Launch graphical interface
ascicat-gui

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Interactive Tutorials

Run the tutorials directly in your browser — no installation required:

Tutorial	Description	Launch
01 - Introduction	ASCICat framework and basic workflow
02 - HER Screening	Complete HER catalyst analysis
03 - CO2RR Analysis	Multi-pathway CO2RR screening
04 - Visualization	Publication-quality figure generation
05 - Sensitivity	Weight sensitivity analysis

Or launch all tutorials in Binder:

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Features

Core Capabilities

• Multi-objective optimization with customizable weight preferences
• Multiple reaction pathways: HER, CO2RR-CO, CO2RR-CHO, CO2RR-COCOH
• Publication-quality visualizations at 600 DPI
• Comprehensive sensitivity analysis for robust screening
• Pareto frontier analysis for trade-off exploration
• Batch processing for high-throughput screening
• Colorblind-friendly visualization palettes

Supported Reactions

Reaction	Pathway	Optimal ΔE	Description
HER	H adsorption	-0.27 eV	Hydrogen Evolution Reaction
CO2RR	CO	-0.67 eV	Carbon monoxide production
CO2RR	CHO	-0.48 eV	Methanol pathway
CO2RR	COCOH	-0.32 eV	Formic acid pathway

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Sensitivity Analysis

ASCICat includes built-in tools to address the critical "weight selection problem":

from ascicat import SensitivityAnalyzer, SensitivityVisualizer

# Analyze sensitivity across weight space
analyzer = SensitivityAnalyzer(calc)
results = analyzer.analyze_full_weight_space(resolution=20)

# Generate visualization suite
visualizer = SensitivityVisualizer(results)
visualizer.plot_ternary_heatmap()
visualizer.plot_catalyst_dominance()
visualizer.plot_rank_trajectories()

This enables researchers to:

• Understand how rankings depend on weight choices
• Identify robust candidates that rank well regardless of weights
• Document the sensitivity of conclusions transparently

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Examples

The examples/ directory contains complete, documented workflows:

Example	Description
example_1_HER_screening.py	Complete HER catalyst screening workflow
example_2_CO2RR_screening.py	Multi-pathway CO2RR analysis
example_3_visualization.py	Publication figure generation
example_4_sensitivity_analysis.py	Weight sensitivity study
example_5_ascicat_vs_pareto.py	Comparison with Pareto methods

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Data Format

ASCICat accepts CSV files with the following structure:

Column	Type	Description	Unit
DFT_ads_E	float	Adsorption energy	eV
surface_energy	float	Surface energy	J/m²
Cost	float	Material cost	$/kg
symbol	str	Catalyst identifier	-
optimal_energy	float	Sabatier optimum	eV

See data/README_DATA.md for detailed specifications.

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Complementarity with Pareto Analysis

ASCICat and Pareto frontier methods are complementary approaches:

Pareto Analysis	ASCICat
Identifies non-dominated trade-off set	Provides deterministic ranking within the set
Multiple equivalent solutions	Single prioritized list
No weight specification needed	Explicit, documented weights
Difficult cross-study comparison	Reproducible comparison metric

Key insight: Top ASCI-ranked catalysts are predominantly Pareto-optimal, validating both methodologies.

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Project Structure

ASCICat/
├── ascicat/                 # Core package
│   ├── calculator.py        # Main ASCICalculator class
│   ├── scoring.py           # Score computation
│   ├── visualizer.py        # Visualization tools
│   ├── analyzer.py          # Statistical analysis
│   └── sensitivity.py       # Sensitivity analysis
├── scripts/                 # CLI and GUI tools
├── examples/                # Usage examples
├── tutorials/               # Jupyter notebook tutorials
├── data/                    # Catalyst datasets
├── tests/                   # Test suite
└── logo/                    # Branding assets

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Contributing

We welcome contributions! Please see our Contributing Guidelines for:

• Development setup instructions
• Coding standards (NumPy docstrings, PEP 8)
• Testing requirements
• Pull request process

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References

ASCICat is built on established theoretical foundations:

• Nørskov, J. K. et al. Towards the computational design of solid catalysts. Nat. Chem. 1, 37 (2009)
• Greeley, J. et al. Computational high-throughput screening of electrocatalytic materials. Nat. Mater. 5, 909 (2006)
• Sabatier, P. Hydrogénations et déshydrogénations par catalyse. Ber. Dtsch. Chem. Ges. 44, 1984 (1911)
• Oguz, I. C., Khossossi, N., Brunacci, M., Bucak, H. & Er, S. ACS Catal. 15, 19461–19474 (2025)

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License

This project is licensed under the MIT License - see the LICENSE file for details.

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Contact

Author	Nabil Khossossi
Email	[e-mail]
Institution	Dutch Institute for Fundamental Energy Research (DIFFER)
Issues	GitHub Issues

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Acknowledgments

This work was supported by the Dutch Institute for Fundamental Energy Research (DIFFER).

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ASCICat — Bridging computational databases and experimental priorities for accelerated catalyst discovery

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