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Governing Terms: Use of the NIM container is governed by the NVIDIA Software License Agreement and Product-Specific Terms for NVIDIA AI Products. Use of the model is governed by the MIT License.

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ALCHEMI Batched Molecular Dynamics NIM Overview

Description:

ALCHEMI NIM for Batched Molecular Dynamics (BMD) performs molecular dynamics simulations using machine learning interatomic potentials (MLIPs). This NIM can simulate NVE, NVT, and NPT ensembles. The default MACE-MPA-0 model is downloaded from Hugging Face when the container starts. 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;\nand the use of this model is governed by the model's original MIT License.

Deployment Geography:

Global

Release Date:

Current release: 1.1.0 on NGC
Initial release: 1.0.0 on NGC, released March 13, 2026

Program Classes

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

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

Model Name & Link	Use Case	How to Pull the Model
MACE-MP-0	Predicting many-body atomic interactions and generating force fields	Automatic

Deployment Details

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

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\ntailored for enterprise environments
Prebuilt containers for the latest generative AI models, offering a diverse\nrange 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\nspecific data
Enterprise-grade software with dedicated feature branches, rigorous validation\nprocesses, and robust support structures

Getting Started with ALCHEMI Batched Geometry Relaxation NIM

Please visit the ALCHEMI Batched Molecular Dynamics 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
\nMACE Github Repository
\nFIRE2 Optimizer

Container Version(s):

v1.1.0 - Current ALCHEMI Batched Molecular Dynamics NIM release.
\nv1.0.0 - Initial ALCHEMI Batched Molecular Dynamics NIM release.

Security Common Vulnerabilities and Exposures (CVEs)

Please review the Security Scanning tab on NGC to view the latest security scan results.\nFor 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.

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\n\nALCHEMI NIM for Batched Molecular Dynamics (BMD) performs molecular dynamics simulations using machine learning interatomic potentials (MLIPs). This NIM can simulate NVE, NVT, and NPT ensembles. The default [MACE-MPA-0 model](https://huggingface.co/mace-foundations/mace-mpa-0/tree/main) is downloaded from Hugging Face when the container starts. The container components are ready for commercial/non-commercial use.
\n\n## Third-Party Community Consideration
\n\nThe 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](https://github.com/ACEsuit/mace-foundations) Model Card.\n\n### License/Terms of Use:\n\nThe NIM container is governed by the [NVIDIA AI Product Agreement](\n https://www.nvidia.com/en-us/agreements/enterprise-software/product-specific-terms-for-ai-products/);\n and the use of this model is governed by the model's original [MIT License](https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/mit.md).\n\n\n### Deployment Geography:
\n\nGlobal
\n\n### Release Date:
\n\n- Current release: [1.1.0 on NGC](https://catalog.ngc.nvidia.com/orgs/nim/nvidia/containers/alchemi-bmd/1.1.0)\n- Initial release: [1.0.0 on NGC](https://catalog.ngc.nvidia.com/orgs/nim/nvidia/containers/alchemi-bmd/1.0.0), released March 13, 2026\n\n## Program Classes\n\nThe following models are housed within the ALCHEMI Batched Geometry Relaxation Container.\n\n| Model Name & Link | Use Case | How to Pull the Model |\n|:------------------|:---------|:----------------------|\n| [MACE-MP-0](https://huggingface.co/mace-foundations/mace-mpa-0/tree/main) | Predicting many-body atomic interactions and generating force fields| Automatic |\n\n## Deployment Details\n\nNVIDIA NIM, part of NVIDIA AI Enterprise, is a set of easy-to-use microservices\ndesigned to accelerate deployment of generative AI across cloud, data center, and\nworkstations.\n\nBenefits of self-hosted NIMs:\n\n- Deploy anywhere and maintain control of generative AI applications and data\n- Streamline AI application development with industry standard APIs and tools\n tailored for enterprise environments\n- Prebuilt containers for the latest generative AI models, offering a diverse\n range of options and flexibility right out of the gate\n- Industry-leading latency and throughput for cost-effective scaling\n- Support for custom models out of the box so models can be trained on domain\n specific data\n- Enterprise-grade software with dedicated feature branches, rigorous validation\n processes, and robust support structures\n\n## Getting Started with ALCHEMI Batched Geometry Relaxation NIM\n\nPlease visit the [ALCHEMI Batched Molecular Dynamics NIM Documentation](https://docs.nvidia.com/nim/alchemi/alchemi-bmd/latest/overview.html) on how to get started.\n\nOur 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.
\n\n## Reference(s):\n[MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields](https://openreview.net/forum?id=YPpSngE-ZU)
\n[MACE Github Repository](https://github.com/ACEsuit/mace)
\n[FIRE2 Optimizer](https://doi.org/10.1016/j.commatsci.2020.109584)
\n\n## Container Version(s): \n\nv1.1.0 - Current ALCHEMI Batched Molecular Dynamics NIM release.
\nv1.0.0 - Initial ALCHEMI Batched Molecular Dynamics NIM release.
\n\n## Security Common Vulnerabilities and Exposures (CVEs)\n\nPlease review the Security Scanning tab on NGC to view the latest security scan results.\nFor 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.\n\n## Ethical Considerations:\n\nNVIDIA 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. \n\nUsers are responsible for ensuring the physical properties of model-generated molecules are appropriately evaluated and comply with applicable safety regulations and ethical standards.
\n\nPlease report quality, risk, security vulnerabilities or NVIDIA AI Concerns [here](https://app.intigriti.com/programs/nvidia/nvidiavdp/detail).\n\n# Get Help\n\n## Getting started with the NIM\n\nDeploying and integrating the NIM is straightforward thanks to our industry standard APIs. Visit the [NIM Container page](https://docs.nvidia.com/nim/alchemi/alchemi-bmd/latest/overview.html) for release documentation, deployment guides and more.\n\n## Enterprise Support\nGet access to knowledge base articles and support cases or [submit a ticket](https://www.nvidia.com/en-us/data-center/products/ai-enterprise-suite/support/).","displayName","ALCHEMI Batched Molecular Dynamics",["D",1791219105483],"publisher","NVIDIA","latestVersion","versions",[192,130],"imageUrl","/api/image/nim/nvidia/containers/alchemi-bmd","rawImageUrl","canGuestDownload","latestVersionSizeInBytes",11200263741,"licenseTerms",{"_294":295,"_296":16,"_297":298,"_299":20,"_300":301},"isMultinodeEnabled","isMultiArchitecture","hasSignedTag","featureLabels",[285,286],"displayLabels",[243,244,245,246,247,248,249,250,251,252],"policyLabels",[],"systemLabels",[236],"products",[],"hasNimMetadata","computedResourceType",{"_205":237,"_238":237,"_239":240,"_73":241},"signed images","value","filter",{"_242":241,"_238":237},"system","field",{"_205":283,"_238":283,"_239":284,"_73":77},{"_205":280,"_238":281,"_239":282,"_73":77},{"_205":277,"_238":278,"_239":279,"_73":77},{"_205":274,"_238":275,"_239":276,"_73":77},{"_205":270,"_238":271,"_239":272,"_73":77},{"_205":268,"_238":268,"_239":269,"_73":77},{"_205":264,"_238":265,"_239":266,"_73":77},{"_205":261,"_238":261,"_239":262,"_73":77},{"_205":257,"_238":258,"_239":259,"_73":77},{"_205":253,"_238":254,"_239":255,"_73":77},"Triton Inference Server","pltfm_triton_inference_server",{"_242":256,"_238":254},"platform","Simulation and Modeling","uscs_simulation_and_modeling",{"_242":260,"_238":258},"application","PyTorch",{"_242":263,"_238":261},"framework","Physics and Dynamics Simulation","soln_physics_and_dynamics_simulation",{"_242":267,"_238":265},"technology","NSPECT-N69H-LHGL",{"_242":77,"_238":268},"HPC / Supercomputing","indus_hpc_supercomputing",{"_242":273,"_238":271},"industry","High Performance Computing","uscs_high_performance_computing",{"_242":260,"_238":275},"Graph Neural Networks","uscs_graph_neural_networks",{"_242":260,"_238":278},"Drug Discovery","uscs_drug_discovery",{"_242":260,"_238":281},"ALCHEMI",{"_242":77,"_238":283},{"_205":292,"_238":292,"_239":293,"_73":290},{"_205":287,"_238":288,"_239":289,"_73":290},"NVIDIA NIM","nimmcro_nvidia_nim",{"_242":291,"_238":288},"feature","nvidia_nim","NVIDIA AI Enterprise Supported",{"_242":77,"_238":292},"governingTerms","

Governing Terms: Use of the NIM container is governed by the NVIDIA Software License Agreement and Product-Specific Terms for NVIDIA AI Products. Use of the model is governed by the MIT License.

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This enables site owners to prevent cookies in each category from being set in the users browser, when consent is not given. The cookie has a normal lifespan of one year, so that returning visitors to the site will have their preferences remembered. It contains no information that can identify the site visitor.","_rdt_em","This cookie name is associated with Reddit which is used to manage session state and user interactions.","events.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. 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It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","nvidiaCart","This domain is owned by NVIDIA Corporation which provides information about the company, its products, solutions, industries, news, events, and career opportunities.","nv_co","__uxq412__ses.88c1","AWS ELB application load balancerUsed to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","learn.learn.nvidia.com","pctrk","Used to count page views by unauthenticated users against license usage.","bm_s","This cookie is associated with Akamai. 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