The Next Frontier in Climate Tech is Invisible — And It’s AQ: AI + Quantum
by Dr. Ang Xiao, PhD and Jessica Pan, MPH | SandboxAQ
Over the past decade, the world has witnessed a staggering mobilization of capital, talent, and innovation toward solving the climate crisis. From solar to synthetic biology, the climate tech ecosystem has expanded at breakneck speed. But as climate solutions scale from pilot to planet, a new set of barriers is emerging—one that requires us to rethink how we discover and design materials.
Welcome to the invisible layer of climate innovation: simulation through AI- and Quantum-powered materials discovery.
From the Periodic Table to the Planet
Let’s start with a simple premise: every climate solution is, at its core, a materials problem. Whether it’s electrolytes in a battery, catalysts in a hydrogen fuel cell, or membranes in a direct air capture system, our ability to address climate change is bound by what the periodic table offers—and how fast we can navigate it.
The traditional approach to materials discovery is slow, costly, and often based on trial-and-error. Developing a new catalyst can take 10 years. A new electrolyte or battery cathode? Even longer. Meanwhile, the Intergovernmental Panel on Climate Change clock is ticking.
Even when breakthroughs happen, scaling them up from lab bench to commercial deployment poses enormous challenges. Small pilot successes often face steep drop-offs when confronted with the realities of manufacturing, supply chains, and costs.
Why Traditional Discovery Needs to Change
Traditional materials R&D involves long, iterative cycles of synthesis and testing. Even promising new materials can face years of refinement before reaching market readiness. This slow pace limits the climate tech sector’s ability to meet decarbonization goals.
Moreover, as we chase more advanced materials, the cost of exploration rises, creating steep barriers for smaller companies and research teams. Tools that lower the cost and complexity of discovery are urgently needed to broaden participation and accelerate progress.
What AI + Quantum Can Unlock
At SandboxAQ, we believe the next generation of climate breakthroughs won’t just come from the materials we engineer—but from how we compute them. By combining first-principles quantum chemistry with Large Quantitative Models (LQMs), AI and quantum tools can simulate chemical reactions and material properties with unprecedented accuracy—without ever stepping into a lab.
This shift from physical to virtual experimentation doesn’t just accelerate discovery—it democratizes it. Startups, researchers, and corporations can now test thousands of battery chemistries, catalysts, or alloys in silico, dramatically narrowing the search space before committing resources to physical trials.
Recent work in catalyst discovery—long seen as one of the most emissions-intensive challenges in petrochemical processes—has shown how AI simulations can cut timelines from months or years to days, accelerating insights into reaction pathways and optimal configurations.
Accelerating Climate Chemistry
One example of this shift in action: quantum mechanics simulations combined with machine learning are already transforming how we screen materials across sectors—from EV batteries to green hydrogen to carbon capture, enabling rapid advancements.
Simulations that once took months can now deliver insights in days, transforming how we understand catalytic systems and helping companies improve process efficiency. In the battery space, early-stage data paired with machine learning can predict cycle life and performance, slashing time-to-market for next-generation materials.
Perhaps most transformative is the rise of autonomous discovery systems—AI “chemists” capable of exploring millions of chemical pathways and surfacing novel compounds that would have been impractical to uncover using conventional methods.
What’s Needed to Scale Across Climate Sectors
For simulation to realize its full potential, several pieces must come together. Continued investment in computing infrastructure, broader access to high-performance tools, and collaborative ecosystems that bring together startups, labs, and industry are critical.
Equally important is data. High-fidelity experimental data is the foundation for predictive AI models. Improving data quality, building shared repositories, and developing standards for digital experimentation will be essential to scale the impact of simulation technologies.
Importantly, as financial institutions and industrial leaders modernize their computation strategies, the climate sector has an opportunity to lead—not lag—in adopting these advances. In a world where trust and resilience are paramount, investing in scalable, secure computing is no longer optional.
Climate Innovation Needs Its “NVIDIA Moment”
For those familiar with AI’s meteoric rise, the pattern is clear: research breakthroughs → algorithmic advances → hardware acceleration → exponential applications. The tipping point came when general-purpose AI chips enabled scalable inference across industries.
Climate tech is now poised for its own “NVIDIA moment.” Just as GPUs catalyzed the rise of AI, we’re now seeing a similar inflection point in climate tech driven by quantum-inspired simulation and large-scale models. This could enable a leap not only in speed, but in who gets to participate.
The Greenest Computation Prevents Extraction
There’s a paradox in climate tech: the more we try to solve the problem, the more we risk exacerbating it—through rare earth extraction, high-energy processes, and carbon-intensive supply chains.
AI and quantum simulation flips this script. It enables us to optimize before we industrialize, to know whether a material will work before we burn carbon trying to produce it. In this sense, it’s the ultimate form of decarbonization: reducing waste and trial-and-error at the molecular level.
We like to think of it as “climate tech for climate tech”—an invisible but essential layer that ensures every electron, dollar, and data point goes further.
A Call to Climate Founders
If you’re building in climate tech, here’s our invitation: think beyond the bench. Virtual experimentation can help reduce development cycles, lower costs, and improve performance.
Whether you’re a founder designing better electrolyzers, a chemical engineer exploring novel sorbents, or an investor evaluating platform plays, simulation should be in your toolkit.
We also need a shift in mindset across the ecosystem. Investors, policymakers, and corporate leaders must recognize advanced computation as a central pillar of innovation strategy, not just a technical accessory. Broad adoption will require education, incentives, and a willingness to rethink how R&D resources are deployed.
Because ultimately, the climate crisis is the ultimate systems problem. And to solve it, we need systems thinking—not just in policy or finance, but in physics, chemistry, and computation.
🎙️ Inevitable Podcast
🤖 Jonathan Godwin, CEO of Orbital Materials, joins us to talk about designing next-gen materials with AI, starting with tech that cools data centers while capturing CO₂ or water. He shares how Orbital’s open-source model, Orb, and a DeepMind-inspired approach could reshape industrial R&D and climate tech. Listen to the full episode here.
🍿 The Lean Back
Learn more about Orbital Materials in the latest episode of Inevitable.
👩💻 Climate Jobs
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Production Technician at Lightship (Broomfield, CO)
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🗓️ Events
👨💻 MCJ Climate + Product Meetup: Monthly climate and product meetup for folks to connect over product challenges in the climate space. (Thursday, June 5)
🎙️ MCJ Live in Austin - Where AI Meets Energy with Crusoe: MCJ is coming to Austin with Crusoe, the AI infrastructure company powering OpenAI’s $11.6B data center campus in Texas. Join us for an afternoon of startup roundtables and a live recording of the Inevitable podcast with Crusoe’s Co-founder and President, Cully Cavness, on the future of compute, energy, and climate innovation. RSVP here. (Tuesday, June 10)
⚡️ Sometimes Boring, Always Important - What Investors and Founders Should Know About US Electricity Regulation: Join us for a tactical session designed by Greg Geller for early-stage climate tech founders and investors navigating utility-facing business models. We’ll cover how U.S. regulation works and the commercial implications for your business. (Tuesday, June 24)
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A really interesting article, thank you! I may be being narrow-minded as I don’t know much about this sector but it might be worth pointing out in future articles some of the negative environmental aspects of AI, such as it’s water usage.