Artificial Intelligence Research

NREL's artificial intelligence (AI) research targets technology advancement through machine learning (ML), reinforcement learning (RL), multidisciplinary deep learning, and an enabling high-performance computing (HPC) infrastructure.

NREL is seeing success with generalized AI, motivated by applications from autonomous vehicles and systems to machine-guided inverse design. Our research using AI and ML allows us to derive insights, make predictions, and explain future system states by learning from data. Our models enable us to address—in a computationally efficient way—issues such as cyberattacks, real-time traffic concerns, protein engineering, and many others.

NREL fuses data streams, modeling and simulation, and ML into its HPC workflows, a paradigm anchored in its data centers. Synthetic data generation through modeling and simulation, computationally heavy ML training, and hyperparameter optimization for HPC suitability are key roles for HPC in the AI workflow. Learn more about NREL's advanced computing operations.

Software, Training, Data Sets, and Collaboration Opportunities

NREL's data, tools, and software drive advancements across energy efficiency, sustainable transportation, renewable power technologies, and the knowledge base to optimize energy systems. For example, BUTTER enables researchers to run high volumes of computational experiments in a highly distributed asynchronous way

We regularly facilitate deep learning training sessions, which allow NREL cohorts to engage in professional development together, often well beyond the initial activity's scope.


Juliane Mueller

Group Manager, Computational Science