Datavault AI and Brookhaven Lab use digital twins to boost canola biofuel efficiency

Datavault AI Inc. (Nasdaq: DVLT), a U.S.-based artificial intelligence data platform developer, has partnered with the U.S. Department of Energy’s Brookhaven National Laboratory to launch a high-performance computing initiative focused on biofuel crop optimization. The project uses AI-driven digital twins to simulate and refine the fatty acid metabolism of Brassica napus (canola), a key biofuel feedstock, to increase its oil yield and support the U.S. Environmental Protection Agency’s target of replacing up to 140,000 barrels of crude oil per day with biofuels.

This strategic move positions Datavault AI to expand from its core business of data experience and monetization into the clean energy and computational biology domains. The collaboration aims to shorten the traditional multi-year crop optimization timeline through in silico simulations powered by Datavault AI’s machine learning infrastructure, in combination with Brookhaven’s advanced genomic and computational capabilities.

The project also aligns with the U.S. government’s broader energy transition goals and rising federal investment in renewable diesel and advanced biofuel technologies.

How will Datavault AI and Brookhaven National Laboratory collaborate on high-performance computing for canola biofuel optimization?

The initiative is anchored in Brookhaven National Laboratory’s Computing and Data Sciences Directorate, which will provide genomic research and simulation validation infrastructure. Datavault AI will contribute its multi-modal machine learning systems to create dynamic digital twins of canola metabolic pathways. These models aim to identify high-yield genetic traits by simulating and analyzing changes to fatty acid biosynthesis in silico.

Sonia Choi, Chief Marketing Officer at Datavault AI and the project’s Lead Principal Investigator, stated that the collaboration will ensure that scientific insights are converted into structured, scalable outputs ready for deployment across agricultural biotech and energy sectors. By pairing Brookhaven’s scientific resources with Datavault AI’s model training and validation expertise, the team intends to bring commercial readiness to the forefront of plant genomics innovation.

What computational methods are used to accelerate fatty acid metabolism optimization in Brassica napus for biofuel production?

Datavault AI’s solution leverages comparative genomics, multi-omics integration, and evolutionary biology algorithms to simulate oil biosynthesis pathways under variable genetic scenarios. These computationally validated digital twin models simulate the physiological responses of canola plants to genetic enhancements aimed at boosting oil content.

Nathaniel Bradley, CEO of Datavault AI, explained that this digital-first approach significantly reduces dependency on traditional greenhouse trials. By using high-performance computing to rapidly iterate gene pathway designs, the project intends to deliver a commercially viable, high-oil-yield canola variant in a fraction of the time normally required.

The digital twin infrastructure also incorporates Web 3.0 architecture to support traceable and monetizable datasets, allowing Datavault AI to create structured outputs for licensing, institutional use, and broader commercialization beyond just seed products.

How does the project align with U.S. federal biofuel targets and recent renewable diesel investment trends?

The project directly supports the EPA’s initiative to displace crude oil with advanced biofuels, reinforcing U.S. energy independence goals outlined in the Inflation Reduction Act and Department of Energy clean energy strategy. Datavault AI’s contribution to upstream feedstock enhancement complements the wave of downstream investment in renewable diesel refining capacity.

According to industry reports, global biofuel demand is projected to grow by more than 38 billion liters between 2023 and 2028—an increase of nearly 30%—driven by decarbonization mandates and rising demand for sustainable transport fuels. In North America alone, more than USD 1.9 billion was committed to renewable diesel capacity expansions in 2022, with multiple large-scale refineries now operational or in development.

In this context, the optimization of oil-rich crops like canola represents a critical input for supply-side efficiency and cost competitiveness. Datavault AI’s work is expected to bridge the gap between lab-stage discoveries and scalable industrial adoption.

What is the anticipated financial structure and monetization model behind Datavault AI’s biofuel initiative?

While specific funding terms have not been disclosed, the project likely falls within the scope of Department of Energy research programs, where grants for early-stage innovation typically range from USD 5 million to USD 25 million. In-kind contributions from Brookhaven National Laboratory are expected to include computational infrastructure and genomic analysis support.

Datavault AI’s core monetization strategy involves transforming structured simulation outputs into commercially licensable datasets. These may be marketed to seed companies, biofuel producers, or energy investors interested in proprietary high-yield crop profiles.

In addition, the company plans to tokenize certain research data assets using blockchain infrastructure, allowing traceability, licensing control, and downstream monetization. This opens an entirely new revenue stream within the data economy of the bioenergy sector.

What is the investor sentiment around Datavault AI following this clean energy expansion?

Following the announcement, shares of Datavault AI (Nasdaq: DVLT) rose by approximately 3% during intraday trading. While modest, the gain reflects growing investor interest in AI applications that intersect with federally funded clean tech initiatives.

Institutional sentiment remains cautiously optimistic, with analysts noting that the Brookhaven partnership lends credibility to Datavault AI’s claims. However, sustained investor confidence will depend on tangible milestones such as model validation, licensing agreements, and early signs of commercial adoption.

Some market watchers have emphasized the importance of execution in proving the scalability of this platform. If Datavault AI can demonstrate measurable yield gains and bring simulation-enhanced crops to pilot-stage trials or licensing deals, investor confidence could accelerate.

What are the upcoming project milestones and what implications do they have for AI-enabled clean energy solutions?

Over the next 12–18 months, the team expects to complete the first round of digital twin validation for fatty acid metabolic pathways. This will involve aligning simulation predictions with experimental data, refining AI models for accuracy, and preparing output structures for industry testing.

Should the validation phase prove successful, Datavault AI is expected to transition to early-stage greenhouse trials of enhanced canola variants, in partnership with agricultural institutions or commercial growers.

Experts in the renewable energy and agri-biotech sectors are watching closely, as this initiative could serve as a blueprint for the deployment of AI in bioenergy R&D pipelines. If commercial uptake materializes, Datavault AI’s model could be adapted to other crops such as soybean, camelina, or even algae.

The company’s strategy to integrate AI, Web 3.0 monetization, and DOE-backed R&D illustrates how startups can navigate multiple high-growth ecosystems simultaneously—offering data-driven solutions to complex environmental and economic challenges.