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mat-surface-adsorption

Calculate adsorption energies for adsorbate–crystal combinations using MLIP relaxation and MatCalc workflows.

144|21|Updated Jan 8, 2026
Search Tags:#catalysis#mlip#adsorption energy#surface science#molecular relaxation#miller indices#site screening
One-click install
npx skills add https://github.com/learningmatter-mit/AtomisticSkills --skill mat-surface-adsorption
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: mat-surface-adsorption
Source: https://github.com/learningmatter-mit/AtomisticSkills/tree/main/.agents/skills/mat-surface-adsorption
Command: npx skills add https://github.com/learningmatter-mit/AtomisticSkills --skill mat-surface-adsorption

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

It computes adsorption energies for adsorbate–surface combinations so you can predict binding stability without running full DFT for every configuration.

Core Features & Use Cases

  • Adsorption energy calculation: Evaluates (E_{ads}=E_{adsorbate+slab}-E_{slab}-E_{adsorbate}) from relaxed MLIP energies.
  • Automated surface workflow: Relaxes bulk (optional), generates a slab from Miller indices, relaxes the clean slab, places the adsorbate at candidate sites, and relaxes each adsorbate–slab geometry.
  • Site enumeration and ranking: Identifies multiple adsorption sites (e.g., ontop/bridge/hollow) and reports the most stable site plus per-site energies.
  • Common use cases: Screening adsorbates on catalytic surfaces, comparing facets, and benchmarking model-driven adsorption energetics with reusable inputs.

Quick Start

Use the following command to calculate adsorption energies for an adsorbate on a specified surface facet with an MLIP model: python .agents/skills/mat-surface-adsorption/scripts/calculate_adsorption.py --bulk path/to/bulk_structure.cif --adsorbate path/to/adsorbate.xyz --miller_index '[1,1,1]' --model_type fairchem --model_name EquiformerV2-31M-S2EF-OC20-All+MD --output_dir research/adsorption_run

Frequently Asked Questions about mat-surface-adsorption

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I calculate surface adsorption energies without running full DFT?▼

You can calculate surface adsorption energies without full DFT by using MLIP-driven relaxation to evaluate adsorbate-slab stability. This workflow automates bulk relaxation, slab generation, and adsorbate placement to compute binding energies quickly.

How do I screen adsorption sites on catalytic surfaces using machine learning potentials?▼

Screening adsorption sites on catalytic surfaces using machine learning potentials involves enumerating candidate positions like ontop, bridge, and hollow. The workflow relaxes each adsorbate-slab geometry and ranks the most stable site by per-site energy.

What do I need to calculate adsorption energy for specific crystal facets?▼

To calculate adsorption energy for specific crystal facets you need bulk structure files, adsorbate coordinates, and Miller indices. These inputs drive slab generation and MLIP relaxation to estimate binding stability across different surface orientations.

Can I compare adsorption stability across different Miller index facets?▼

Yes, you can compare adsorption stability across different Miller index facets by specifying multiple Miller indices. The workflow generates slabs for each facet, relaxes the adsorbate-slab geometries, and reports the resulting adsorption energies for comparison.

How does the automated surface workflow handle bulk and slab relaxation?▼

The automated surface workflow handles relaxation by optionally relaxing the bulk structure first, generating a slab from Miller indices, relaxing the clean slab, placing the adsorbate at candidate sites, and relaxing each adsorbate-slab geometry to compute energies.

What is the best way to benchmark model-driven adsorption energetics for materials chemistry?▼

The best way to benchmark model-driven adsorption energetics is using reusable inputs with MLIP-driven relaxation. The workflow computes adsorption energies from relaxed geometries, enabling consistent benchmarking across adsorbate-crystal combinations without full DFT.

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