Insider Brief
- Meissner raised $2.6 million in pre-seed financing to develop superconducting materials for quantum computing, fusion energy and other applications.
- The startup combines machine learning, quantum simulations and laboratory testing to identify materials that could operate at higher temperatures and with fewer performance problems.
- Meissner plans to begin testing its leading material candidates at the University of Waterloo’s Quantum-Nano Fabrication and Characterization Facility.
Toronto-based materials startup Meissner has raised $2.6 million in pre-seed financing to search for superconductors that could support advances in quantum computing, fusion energy and other emerging industries.
The $2.6 million U.S. round, equivalent to about $3.6 million Canadian, included backing from BDC Capital’s Thrive Venture Fund and a group of Canadian technology entrepreneurs and investors. Participants included Andrew Talpash, Anthony Lacavera, Christian Weedbrook, Daniel Debow, Dennis Bennie, Eliot Pence, Greg Twinney, and Michael and Richard Hyatt, according to BetaKit.
Meissner plans to use machine learning, computer simulations and laboratory testing to identify and develop materials with improved superconducting properties. The four-person company describes the system it is building as a “discovery engine” for superconductors.
Superconductors can carry electricity without resistance or the resulting loss of energy. They are already used in technologies such as magnetic resonance imaging machines and magnetic-levitation trains. Companies are also exploring them as components for quantum computers and fusion-energy systems, both of which require precise control of electrical currents and magnetic fields.
“They’re definitely the picks and shovels to unlocking high-growth, high-tech industries,” Meissner founder and CEO Olivia Leng told BetaKit.
The company’s goal is to produce and sell superconducting materials tailored to particular commercial applications. Meissner is initially focused on finding materials that can operate at higher temperatures and avoid some of the performance problems associated with current superconductors.
Making Superconductors More Practical
Many existing superconductors work only when cooled to extremely low temperatures. Maintaining those conditions requires specialized refrigeration equipment that increases the cost, power use and complexity of deploying the materials.
Superconducting systems can also experience sudden localized losses of superconductivity, known as quenches. When part of a material becomes electrically resistant, it can generate intense heat that damages nearby components.
Meissner aims to develop materials that are less expensive and more reliable to operate, potentially making superconducting technology practical for a wider range of industries. The company’s business model calls for selling optimized materials for specialized applications rather than building complete quantum computers or energy systems.
That approach appealed to investor Michael Hyatt, who compared Meissner with his early investment in Toronto quantum-computing company Xanadu. Several of Meissner’s investors have also backed Xanadu, while Weedbrook is the quantum company’s founder and chief executive.
“If you believe quantum is going to be a reality by 2030, companies like Meissner will be really important in that process,” Hyatt told BetaKit. “It’s a derivative bet on quantum.”
Hyatt also said the technical and physical nature of the business creates a barrier to competition. Unlike a software product that can be developed quickly with AI-assisted coding tools, new superconductors require scientific expertise, laboratory equipment and experimental testing.
From Computer Models to the Laboratory
Leng formed Meissner about a year ago after studying materials science chemistry at the University of Toronto. Her undergraduate work included laboratory research with superconductors and chemical and electrical simulations, according to BetaKit. She paused her studies to concentrate on building the startup.
The company takes its name from the Meissner effect, in which a superconductor expels a magnetic field when it enters its superconducting state. That behavior is one of the defining properties of superconducting materials.
The company has concentrated its early work on computation. Leng said its proprietary machine-learning model identifies new metal-based compounds that might become superconductors. Meissner then uses quantum simulations, which model the behavior of electrons and atoms, to assess the most promising candidates before attempting to manufacture and test them.
This screening process is intended to reduce the time and expense of laboratory experimentation. Materials development has traditionally involved testing large numbers of possible chemical combinations, many of which fail to produce useful results.
Meissner plans to begin testing its leading candidates this month at the University of Waterloo’s Quantum-Nano Fabrication and Characterization Facility. The experiments will provide an early indication of whether the company’s computer predictions hold up under laboratory conditions.
“We finally get to take our materials that we’ve run very high-fidelity quantum simulations on, that have shown very promising results, out into the lab to see how well those lab results correlate,” Leng told BetaKit.
The laboratory results will represent an important technical test for the young company. A model that successfully predicts useful superconducting behavior could help Meissner build a pipeline of proprietary materials. Poor correlation between the simulations and experiments would require the startup to refine its system before moving toward commercial production.
