friederbluemle / NanoX

A Transformer GAN with Reinforcement Learning on generating novel ready to mass manufacture nanomachines

NanoX

NanoX is a transformer model designed to create bio-compatible, high-performance, and ready-to-manufacture nanomachines for optimizing human-bio feedback processes.

Installation

To use the NanoX model, you need to have PyTorch installed in your environment. If you haven't installed it yet, you can do so with the following command:

pip install nanox

Usage

Here is a basic example of how to use the NanoX model:

import torch
from nanox.model.encoder import NanoXGraphEncoder
from nanox.model import NanoXModel, NanoX

# Initialize the encoder
encoder = NanoXGraphEncoder()

# Initialize the model
model = NanoXModel(encoder)

# Define the batched data
batched_data = torch.rand(10, 512)  # Example data

# Forward pass through the model
output = model(batched_data)

Classes

The module contains two main classes:

• NanoXModel: This is the main model class which uses an encoder to process the input data.
• NanoX: This class is used to create an instance of the NanoX model with specific parameters.

Hiring

We're hiring Engineers, Researchers, Interns, and Salespeople to work on democratizing SOTA Multi-Modality Foundation Models. If you're interested, please email your accomplishments to [e-mail].

Datasets

To train this model, we need extensive and diverse data. Here are some readily available datasets that can be beneficial:

• Material Sciences Datasets: Datasets like the Materials Project, OQMD (Open Quantum Materials Database), AFLOWLIB.org etc. can provide insight into various materials and their properties. This data can inform the AI about feasible and efficient materials for nanomachine construction.

• Nanodevice Datasets: Existing databases from research in the field of nanodevices and nanotechnology can provide a good starting point for our AI model.

• Biomedical Datasets: Datasets like The Cancer Imaging Archive (TCIA), Human Protein Atlas, GenBank (NIH genetic sequence database), etc. can provide useful information about human physiology and how nanomachines can interact with it.

• Physics Datasets: Datasets such as PhysioNet (containing a wealth of biomedical research data) and CERN's Open Data Portal could provide crucial insights into the laws that govern the behaviors of nanomachines.

This approach ensures that we not only create efficient designs but also consider the complexities of the real world, such as the laws of physics and material limitations. The final goal is to have an AI system that can generate practical, viable, and manufacturable designs of nanomachines.

Resources:

• Swarms of Nanomachines Could Improve the Efficiency of Any Machine
• Ultra-tiny nanomachines are redefining how we think of robots
• Towards Predicting Equilibrium Distributions
• Distributional Graphormer: Toward Equilibrium Distribution Prediction for Molecular Systems
• Graphormer

Vision

At NanoX, our vision is to democratize and revolutionize the world of nanotechnology. We aim to use our advanced AI system to create efficient, feasible, and ready-to-manufacture designs of nanomachines. By doing so, we aspire to significantly optimize human-bio feedback processes, enhancing the quality of life and pushing the boundaries of technological innovation. We firmly believe in the power of AI to transform the field of nanotechnology and contribute significantly to scientific and medical advancements.