ALCHEMI Batched Geometry Relaxation NIM Overview

Description:

ALCHEMI NIM for Batch Geometry Relaxation (BGR) performs high-throughput geometry optimization of atomic structures using machine learning interatomic potentials (MLIPs). This NIM uses the FIRE2 optimizer with optional cell optimization. This container houses the MACE-MPA-O model. The container components are ready for commercial/non-commercial use.

Third-Party Community Consideration

The model embedded in the container is not owned or developed by NVIDIA. This model has been developed and built to a third-party’s requirements for this application and use case; see link to Non-NVIDIA MACE-MPA-0 Model Card.

License/Terms of Use:

The NIM container is governed by the NVIDIA AI Product Agreement; and the use of this model is governed by the model's original MIT License.

Deployment Geography:

Global

Release Date:

03/12/2026 via NGC

Program Classes

The following models are housed within the ALCHEMI Batched Geometry Relaxation Container.

Deployment Details

NVIDIA NIM, part of NVIDIA AI Enterprise, is a set of easy-to-use microservices designed to accelerate deployment of generative AI across cloud, data center, and workstations.

Benefits of self-hosted NIMs:

• Deploy anywhere and maintain control of generative AI applications and data
• Streamline AI application development with industry standard APIs and tools tailored for enterprise environments
• Prebuilt containers for the latest generative AI models, offering a diverse range of options and flexibility right out of the gate
• Industry-leading latency and throughput for cost-effective scaling
• Support for custom models out of the box so models can be trained on domain specific data
• Enterprise-grade software with dedicated feature branches, rigorous validation processes, and robust support structures

Getting Started with ALCHEMI Batched Geometry Relaxation NIM

Please visit the ALCHEMI Batched Geometry Relaxation NIM Documentation on how to get started.

Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA’s hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.

Reference(s):

MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields
MACE Github Repository
FIRE2 Optimizer

Container Version(s):

v0.1.0 - Initial ALCHEMI Batched Geometry Relxation NIM release.

Security Common Vulnerabilities and Exposures (CVEs)

Please review the Security Scanning tab on NGC to view the latest security scan results. For certain open-source vulnerabilities listed in the scan results, NVIDIA provides a response in the form of a Vulnerability Exploitability eXchange (VEX) document. The VEX information can be reviewed and downloaded from the Security Scanning tab.

Ethical Considerations:

NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal developer team to ensure these software components meet requirements for the relevant industry and use case and address unforeseen product misuse.

Users are responsible for ensuring the physical properties of model-generated molecules are appropriately evaluated and comply with applicable safety regulations and ethical standards.

Please report quality, risk, security vulnerabilities or NVIDIA AI Concerns here.

Get Help

Getting started with the NIM

Deploying and integrating the NIM is straightforward thanks to our industry standard APIs. Visit the NIM Container page for release documentation, deployment guides and more.

Enterprise Support

Get access to knowledge base articles and support cases or submit a ticket.