semvec/pypi-packages huggingface dataset:
repozilla 0.1.0
🦖 Repozilla – explorador de estruturas de diretórios com estilo
Requires Python: <3.13,>=3.12

5️⃣ README.md

\# 🦖 Repozilla



Explorador de estruturas de diretórios com estilo – feito para devs que amam ver seus repositórios bem organizados.



\## 🚀 Como usar



\### Instalar

```bash

pip install repozilla



Usar via terminal

repozilla .



Usar via Python

from repozilla import scan, show\_tree

paths = scan(".", exclude=\[".venv"])

show\_tree(paths, ".")





Feito com 💙 por David Closs.





---



\## ⚖️ \*\*6️⃣ LICENSE (MIT)\*\*



```text

MIT License



Copyright (c) 2025 David Closs



Permission is hereby granted, free of charge, to any person obtaining a copy

of this software and associated documentation files (the "Software"), to deal

in the Software without restriction...
dimos-torch 0.1.1
DIMOS: DIfferentiable MOlecular Simulator
Requires Python: >=3.10
Keywords: simulation, molecular dynamics, sampling methods

<p align="center">
<img src="./dimos-logo.svg" alt="DIMOS" width="600"/>
</p>

DIMOS (Differentiable Molecular Simulator) is a PyTorch-based framework for molecular dynamics (MD) and Monte Carlo (MC) simulations, designed to bridge the gap between traditional simulation engines and modern machine learning (ML) workflows. Built for flexibility, performance, and end-to-end differentiability, DIMOS enables seamless integration of classical force fields, machine learning interatomic potentials (MLIPs), and hybrid ML/MM approaches, empowering researchers to innovate in computational chemistry, physics, and biology.

Documentation available at: https://dimos.henrik-christiansen.net

Please cite our preprint if you are using DIMOS: 
H. Christiansen, T. Maruyama, F. Errica, V. Zaverkin, M. Takamoto, and F. Alesiani, Fast, Modular, and Differentiable Framework for Machine Learning-Enhanced Molecular Simulations, [arXiv:2503.20541](https://arxiv.org/pdf/2503.20541) (2025). 

## Installation

DIMOS can be installed using pip
```bash
pip install dimos-torch
```

Or alternatively by cloning and then installing locally, including optional dependencies, such as the MACE and ORB interatomic potentials or tools used for tests/development:
```bash
git clone https://github.com/nec-research/dimos.git; cd dimos

# install with optional dependencies
python -m pip install -e '.[dev,mmtools,mace,orb]'
```

To run the test cases based on torchMD, also this package needs to be installed. To avoid the installation of the (proprietary) moleculekit dependency, call
```bash
pip install torchmd scipy networkx pandas tqdm pyyaml --no-deps 
```

## Get started

```python
import dimos
system = dimos.AmberForceField("config.prmtop")
integrator = dimos.LangevinDynamics(dt, T, gamma, system)
simulation = dimos.MDSimulation(system, integrator, positions, T)
simulation.step(num_steps)
```