So3krates-torch

v0.2.0 suspicious
4.0
Medium Risk

PyTorch implementation of So3krates neural network potential for atomistic simulations

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows limited signs of potential risk with no direct evidence of malicious intent. However, the sparse metadata and the warning about breaking changes raise concerns.

  • Sparse author information and possibly inactive account
  • Warning about potential breaking changes
Per-check LLM notes
  • Network: No network calls detected, which is normal and does not indicate any risk.
  • Shell: Shell execution patterns are limited to running Python modules, suggesting it may be part of the package's functionality rather than malicious activity.
  • Obfuscation: The observed patterns seem to be code snippets that have been obfuscated for readability purposes rather than malicious intent.
  • Credentials: No patterns indicative of credential harvesting were detected.
  • Metadata: The author's information is sparse and the account seems new or inactive, raising some suspicion but not conclusive evidence of malice.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 8.0

Found 4 obfuscation pattern(s)

  • t_model_config) model.eval() batch = make_batch(h2o_atoms, r_max=5.0) o
  • t_model_config) model.eval() atoms_list = [molecule("H2O"), molecule("NH3"), mo
  • t_model_config) model.eval() atoms_list = [molecule("H2O"), molecule("NH3")]
  • t_model_config) model.eval() result = evaluate_model( atoms_list=[m
⚠ Shell / Subprocess Execution score 4.0

Found 2 shell execution pattern(s)

  • # Run CLI result = subprocess.run( [ "python", "-m
  • h validation result = subprocess.run( [ "python", "-m
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: uni.lu>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository tohenkes/So3krates-torch appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" appears to have only 1 package on PyPI (new or inactive account)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with So3krates-torch 
Create a mini-application that simulates molecular dynamics using the 'So3krates-torch' package, which implements a neural network potential for atomistic simulations based on PyTorch. This application will serve as a basic tool for researchers and students interested in understanding the behavior of materials at the atomic level without the need for complex quantum mechanical calculations. Here’s a detailed plan for your project:

1. **Project Setup**: Initialize a new Python project. Ensure you have PyTorch installed alongside 'So3krates-torch'. If not already available, install it via pip.
2. **Data Preparation**: Use pre-defined datasets or generate simple configurations of atoms (e.g., a small molecule like water or a simple crystal structure). These datasets should include initial positions and possibly velocities for the atoms.
3. **Simulation Initialization**: Write a function that initializes the simulation environment using 'So3krates-torch'. This involves loading the neural network model that has been trained on atomistic interactions.
4. **Force Calculation**: Implement a method to calculate forces between atoms using the neural network potential provided by 'So3krates-torch'. This is crucial for performing molecular dynamics simulations.
5. **Integration Step**: Integrate the forces over time to update the positions and velocities of atoms. Use a simple integration method like Verlet integration to keep things straightforward.
6. **Visualization**: Include a feature to visualize the simulation. You could use libraries such as Matplotlib or even more specialized ones like ASE (Atomic Simulation Environment) for plotting trajectories and snapshots of the molecular dynamics.
7. **Analysis Tools**: Provide basic tools to analyze the simulation data. For example, calculate and plot the total energy of the system over time, or visualize the distribution of bond lengths or angles.
8. **User Interface**: Although not mandatory, consider adding a simple command-line interface (CLI) that allows users to specify parameters like the type of molecule, initial conditions, and simulation duration.
9. **Documentation**: Write clear documentation explaining how to run the simulation, interpret the results, and customize the application for different scenarios.

This mini-application will not only demonstrate the power of 'So3krates-torch' in simulating atomistic systems but also provide a practical learning tool for those interested in computational materials science.