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ASCICat

Activity-Stability-Cost Integrated Catalyst Discovery

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

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The Challenge

The computational catalysis community has generated massive ML/DFT databases containing thousands of calculated catalyst properties. However, there exists no standardized framework to translate this wealth of data into actionable experimental priorities.

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

The ASCI Framework

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

  • Activity Score (Sa)

    Based on the Sabatier Principle: optimal binding energy for reaction kinetics. Too weak binding prevents activation; too strong binding prevents desorption.

    \[S_a = \max\left(0, 1 - \frac{|\Delta E - \Delta E_{opt}|}{\sigma_a}\right)\]

  • Stability Score (Ss)

    Based on Surface Thermodynamics: lower surface energy indicates stronger metal-metal bonding and enhanced resistance to dissolution.

    \[S_s = \frac{\gamma_{max} - \gamma}{\gamma_{max} - \gamma_{min}}\]

  • Cost Score (Sc)

    Based on Economic Viability: logarithmic normalization handles the enormous range in material costs while maintaining discrimination.

    \[S_c = \frac{\log C_{max} - \log C}{\log C_{max} - \log C_{min}}\]

The Unified ASCI Metric

\[\phi_{ASCI} = w_a \cdot S_a + w_s \cdot S_s + w_c \cdot S_c\]

where \(w_a + w_s + w_c = 1\) and all scores \(S_i \in [0, 1]\)

Quick Example

from ascicat import ASCICalculator

# Initialize 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% Activity
    w_s=0.3,  # 30% Stability
    w_c=0.3   # 30% Cost
)

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

Output: 

      symbol     ASCI  activity_score
0      Fe2Sb4   0.899           0.923
1       Cu3Sb   0.887           0.912
2      Cu6Sb2   0.876           0.889
...

Supported Reactions

Reaction Pathway Optimal \(\Delta E\) 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

Key Features

  • Customizable Weights

    Balance activity, stability, and cost according to your application requirements

  • High-Quality Figures

    Generate 600 DPI figures including 3D Pareto spaces, volcano plots, and sensitivity diagrams

  • Sensitivity Analysis

    Ternary diagrams, bootstrap confidence intervals, and variance-based sensitivity indices

  • High-Throughput Ready

    Process datasets with 50,000+ catalysts with automatic stratified sampling

  • Multiple Interfaces

    Python API, command-line interface, and graphical user interface

  • Pareto Complementarity

    Works alongside Pareto frontier methods for comprehensive analysis

Scientific Foundation

ASCICat is built on established theoretical foundations:

References

  • 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)

Installation

pip install ascicat

git clone https://github.com/NabKh/ASCICat.git
cd ASCICat
pip install -e .

pip install ascicat[gui]

Ready to start screening catalysts?

Get Started

Developed at the Dutch Institute for Fundamental Energy Research (DIFFER)

Author: N. Khossossi | Contact: n.khossossi@differ.nl