NVIDIA Academic Grant Program: Simulation and Modeling

NVIDIA solicits proposals for innovative projects related to simulation and modeling that incorporate NVIDIA technologies including PhysicsNeMo, ALCHEMI™, BioNeMo™, Digital Biology Research, Warp, cuEquivariance, cuLitho, Omniverse™, HPC SDK, cuPyNumeric, cuQuantum, or CUDA-Q™.

Overview

• Compute resources: Up to 30,000 H100 80 GB GPU hours or equivalent (maximum of eight concurrent GPUs)
• Hardware option: Up to eight NVIDIA RTX PRO 6000 GPUs (Max-Q workstation or Server Edition)
• GPU hours expire six months after award; unused hours will be forfeited
• Physical hardware will be shipped to PI
• Award amount determined by NVIDIA awards panel
• Required technology integration: Must incorporate one or more specified NVIDIA technologies (PhysicsNeMo, ALCHEMI, BioNeMo, Digital Biology Research, Warp, cuEquivariance, cuLitho, Omniverse, HPC SDK, cuPyNumeric, cuQuantum, or CUDA-Q)

Priority Areas

Scientific Simulation:

• Use numerical algorithms to solve PDEs, use machine learning to augment simulation, or use reinforcement learning with simulation in the loop
• Materials science, life sciences, robotics, climate simulations
• Computational chemistry: quantum chemistry, molecular dynamics, and hybrid AI methods
• Cell simulations: coarse-grained molecular dynamics or protein conformational ensembles
• Earth system science: weather predictions, climate prediction and projection, air quality modeling, urban scale prediction, and seasonal-to-decadal modeling
• Astrophysics: N-body simulations
• Multi-physics for high-energy physics
• Fluid dynamics: The physics of fluid dynamics phenomena through computational methods
• Digital twins: Simulation techniques and digital twins for designing complex or large systems (e.g., hardware design)
• Integration with experimental and theoretical approaches

Quantum Computing:

• GPU-QPU integration: Demonstrations of tightly integrated GPU-QPU workloads using NVIDIA NVQLink (NVIDIA RTX PRO 6000 only), coupling the RTX to supported and novel quantum control systems to develop methods for fast calibration, hybrid quantum/classical applications, and real-time error correction decoding
• Qubit design: GPU-accelerated methods for qubit electronic design automation (EDA), including EM field simulation for quantum chip design
• Algorithm development: Quantum algorithm development and advanced simulation methods
• Error correction: Quantum error correction codes and decoding techniques with algorithmic or AI methods
• System simulation: Simulation of noisy quantum systems, time dynamics, and error mitigation techniques

Physics-Informed Machine Learning:

• Develop or apply machine learning techniques that are informed or constrained by physical laws, principles, and models
• ML for physical phenomena: Machine learning systems for modeling physical phenomena, including physics-informed methods, neural operators, and innovative methods to leverage physical knowledge with data-driven techniques
• Hybrid methods: Hybrid methods that combine solving for physical equations and machine learning techniques on the fly
• Control systems: Reinforcement learning to control physical systems

Eligibility & Requirements

Eligible Applicants:

• Full-time faculty at accredited academic institutions that award research degrees to Ph.D. students are eligible
• Postdocs and graduate students must work with a full-time faculty member to submit on their behalf

Submission Limits:

• Each person can submit one proposal per quarter, a maximum of four proposals annually
• Each individual applicant is eligible to receive one award per calendar year

Proposal Requirements:

• Proposals must follow the proposal template and should not exceed four pages, not including appendices
• Must incorporate required NVIDIA technologies as specified in Overview section
• Proposal should align with one or more of the three main themes

Selection Process:

• Not all projects that meet eligibility requirements will be selected for an award
• The final award amount will be determined by the NVIDIA awards panel

Recipient Expectations:

• Award recipients should make reasonable efforts to acknowledge the support of NVIDIA Corporation and reference how specific hardware and software contributed to project results
• Recipients will inform NVIDIA of publications, presentations, open-source code and data releases, and speaking engagements that reference the supported project via the NVIDIA academic grant portal
• Failure to report in the portal will influence future award selection
• Must review NVIDIA Academic Grant Program terms and conditions

Timeline

• Submission window: Quarterly cycle 
• Award duration: GPU hours expire six months after award date
• Reporting: Ongoing obligation to report publications and presentations via NVIDIA academic grant portal

How to Submit:

• Follow NVIDIA proposal template (not exceeding four pages, excluding appendices)
• Submit proposal through NVIDIA academic grant portal
• Ensure proposal aligns with one or more of the three main research themes
• Include plan for incorporating required NVIDIA technologies

Please note: Full RFP is attached in the "More Information" section of this page. Faculty and researchers interested in applying for these opportunities based on technologies developed or disclosed at Vanderbilt must submit their proposals through the CTTC.