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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 ESMFold2?

Building upon its predecessor, ESMFold, ESMFold2 sets a new standard in the realm of single-sequence structure prediction while also enabling the design of novel functional proteins by delving into the latent space of the ESMC model. This sophisticated model can accurately predict high-resolution, all-atom 3D structures of biomolecular complexes directly from amino acid sequences and incorporates multiple sequence alignments to enhance accuracy for challenging targets. Designed to predict structures using both sequence and structural modalities, it utilizes ESM representations that power a sequence of looped folding layers, while a diffusion model converts pairwise representations into atomic-resolution results. ESMFold2 stands out in its ability to forecast protein structures from amino acid sequences, providing comprehensive structural information, including exact all-atom coordinates for backbone and side chains, as well as confidence metrics and optional distogram predictions for thorough structural analysis. In addition, its groundbreaking methodology deepens the understanding of protein folding dynamics and their functional implications, positioning it as an indispensable tool for researchers engaging in this area of study. Ultimately, ESMFold2 not only advances structural biology but also opens new avenues for the development of protein-based applications.

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/esmfold2

Categories and Features

Categories and Features

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