Find and acquire computational science resources autonomously. Use when you need force field parameters, pseudopotentials, crystal structures, or any other scientific data...
You are a researcher. You have tools, you have a goal, you figure out the rest.
When you need something (parameters, structures, files):
Never use a value without knowing where it came from.
Every MD simulation needs force field parameters (LJ epsilon/sigma, bond constants, etc.). These are NOT universal - they depend on:
Step 1: Identify what you need
"I need Lennard-Jones parameters for liquid argon at 94.4 K"
"I need TIP4P water model parameters"
"I need EAM potential for copper"
Step 2: Search literature
Search queries that work:
- "[material] lennard-jones parameters molecular dynamics"
- "[material] force field parameters"
- "[model name] original paper" (e.g., "TIP4P original paper")
- "[material] interatomic potential"
Step 3: Find the authoritative source For common systems, there are seminal papers:
Step 4: Extract parameters
Step 5: Convert units if necessary Common conversions:
Step 6: Document your source In your input file:
# Lennard-Jones parameters for argon
# Source: Rahman, Phys. Rev. 136, A405 (1964)
# ε = 0.238 kcal/mol, Ļ = 3.405 Ć
pair_coeff 1 1 0.238 3.405
1. Search: "argon lennard-jones parameters molecular dynamics"
2. Find: Rahman 1964 is the seminal paper for liquid Ar MD
3. Also find: Allen & Tildesley give ε/kB = 119.8 K, Ļ = 3.405 Ć
4. Convert: ε = 119.8 K à 0.001987 kcal/mol/K = 0.238 kcal/mol
5. Use: pair_coeff 1 1 0.238 3.405
QE needs pseudopotential files (.UPF) for each element. These depend on:
Primary Sources (in order of preference):
SSSP (Standard Solid State Pseudopotentials)
PseudoDojo
QE Pseudopotential Library
Materials Cloud
Step 1: Determine what you need
Element: Si
Functional: PBE (most common for solids)
Type: Usually US or PAW for efficiency
Step 2: Search and navigate
Use WebSearch: "silicon PBE pseudopotential SSSP"
Or navigate directly to SSSP table
Step 3: Download the file Use Playwright or WebFetch to download:
The file will be something like:
Si.pbe-n-rrkjus_psl.1.0.0.UPF
Step 4: Save to resources directory
Save to: workspaces/resources/pseudopotentials/
Or to your project workspace
Step 5: Reference in input
ATOMIC_SPECIES
Si 28.0855 Si.pbe-n-rrkjus_psl.1.0.0.UPF
When you download a pseudopotential, also note the recommended cutoffs:
SSSP provides these explicitly. If not available, test convergence.
Materials Project (API available)
Crystallography Open Database (COD)
ICSD (subscription required)
Paper Supplementary Information
From Materials Project:
from mp_api.client import MPRester
import os
api_key = os.environ.get("MP_API_KEY")
with MPRester(api_key) as mpr:
structure = mpr.get_structure_by_material_id("mp-149") # Silicon
structure.to("poscar", "POSCAR") # Save as VASP format
From COD or papers:
from pymatgen.core import Structure
struct = Structure.from_file("structure.cif")
The main paper often says "parameters in SI" or "see Supporting Information". You need to get these files.
Step 1: Find the paper DOI From Semantic Scholar, Google Scholar, or the paper itself.
Step 2: Navigate to publisher page Use Playwright to:
Step 3: Parse the SI
1. Search Semantic Scholar for "TIP4P water Jorgensen 1983"
2. Get DOI: 10.1063/1.445869
3. Navigate to: https://doi.org/10.1063/1.445869
4. Find paper, check if SI exists
5. For this classic paper, parameters are in Table I of main text
6. Extract: ε = 0.1550 kcal/mol, Ļ = 3.1536 Ć
, etc.
When you find parameters:
Check that parameters make sense:
workspaces/resources/
āāā pseudopotentials/
ā āāā pbe/
ā ā āāā Si.pbe-n-rrkjus_psl.1.0.0.UPF
ā ā āāā ...
ā āāā lda/
āāā potentials/
ā āāā eam/
ā āāā tersoff/
āāā structures/
ā āāā cif/
ā āāā poscar/
āāā parameters/
āāā force_fields.json # Cache of found parameters
When you find parameters, save them:
{
"argon_lj": {
"epsilon_kcal_mol": 0.238,
"sigma_angstrom": 3.405,
"source": "Rahman 1964, Phys. Rev. 136, A405",
"notes": "For liquid argon near triple point"
}
}
You are a researcher, not a script executor.
The goal is: given only a scientific question, you acquire everything needed to answer it.