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What is NVIDIA PhysicsNeMo?

NVIDIA's PhysicsNeMo is an open-source deep-learning framework built in Python that facilitates the design, training, fine-tuning, and inference of AI models that marry physical laws with data, thereby improving simulations, creating precise surrogate models, and enabling near-real-time predictions across a variety of domains such as computational fluid dynamics, structural mechanics, electromagnetics, weather forecasting, climate science, and digital twin technologies. It boasts robust GPU-accelerated performance and offers Python APIs based on the PyTorch framework, all distributed under the Apache 2.0 license, featuring a variety of pre-designed model architectures, including physics-informed neural networks, neural operators, graph neural networks, and generative AI methods, allowing developers to effectively harness the causal relationships present in physics along with empirical data for superior engineering modeling. Furthermore, PhysicsNeMo includes extensive training pipelines that cover all aspects from geometry ingestion to the implementation of differential equations, in addition to providing reference application recipes that assist users in rapidly kickstarting their development processes. This unique integration of powerful features positions PhysicsNeMo as a vital resource for engineers and researchers aiming to push the boundaries of physics-based AI applications. Overall, its capabilities make it a crucial asset for anyone looking to innovate in fields that rely on the intersection of artificial intelligence and physical modeling.

What is ESMC?

ESMC marks the latest innovation in the ESM series of protein language models, advancing the understanding of representation learning in protein biology. By training on an enormous dataset of billions of evolutionary sequences, it effectively captures representations that provide insights into the mechanistic aspects of protein structure and function. Utilizing a transformer architecture, the model prioritizes sequences as its main input and is trained on a dataset that includes up to 6 billion proteins. ESMC is designed for a range of applications within protein science, including structure prediction, functional annotation, protein design, and the investigation of evolutionary relationships among proteins. Furthermore, it has the ability to generate new proteins from partial sequences, structures, or specific functional requirements, which allows researchers to explore novel possibilities in protein design and biological research. The model is readily accessible through the Biohub Platform, enabling users to interact with it via an API and the ESM Python package, which offers quickstart resources for installation, API key generation, and connection to the platform, thus ensuring a user-friendly experience. This ease of access not only promotes wider participation in protein research but also fosters collaborative efforts across the scientific community, ultimately driving further advancements in the field. With its capabilities, ESMC opens new doors for innovation and discovery in protein science.

Media

Media

Integrations Supported

Python
Biohub
PyTorch

Integrations Supported

Python
Biohub
PyTorch

API Availability

Has API

API Availability

Has API

Pricing Information

Free
Free Trial Offered?
Free Version

Pricing Information

Free
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

NVIDIA

Date Founded

1993

Company Location

United States

Company Website

developer.nvidia.com/physicsnemo

Company Facts

Organization Name

Biohub

Date Founded

2016

Company Location

United States

Company Website

biohub.ai/models/esmc

Categories and Features

Categories and Features

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