Insider Brief
- Matlantis released a new report showing that materials R&D has reached a mainstream AI phase, with nearly half of simulation workloads now running on AI or machine-learning methods.
- Surveyed teams reported widespread project abandonment due to time and compute limits, underscoring an urgent need for faster and more efficient simulation capabilities.
- Despite clear cost savings and demand for speed, concerns over accuracy and data security remain high, with only 14% of respondents expressing strong confidence in AI-driven simulations.
PRESS RELEASE — Matlantis, the materials discovery arm of Preferred Networks (PFN), today announced the release of a new research report titled “The State of AI‑Accelerated Materials R&D,” based on a survey of 300 materials science and engineering professionals across the United States. The report reveals a field at an inflection point: materials R&D has entered its mainstream AI era, with nearly half (46%) of all simulation workloads now running on AI or machine-learning methods.
Yet this acceleration comes with significant growing pains. An overwhelming 94% of R&D teams reported abandoning at least one project in the past year because the simulations ran out of time or computing resources, leaving potential discoveries unrealized. The findings underscore the industry’s urgent need for faster simulation capabilities to keep pace with surging innovation demands.
At the same time, the economic stakes for accelerating R&D have never been clearer. According to the survey, organizations are saving roughly $100,000 per project on average by leveraging computational simulation in place of purely physical experiments. This proven ROI is driving heavy investment in new tools and workflows. Researchers are also eager to speed up their work even if it means accepting minor trade-offs in precision—73% of respondents said they would trade a small amount of accuracy for a 100× increase in simulation speed.
“This is the quiet crisis of modern R&D: the experiments that never happen,” said Daisuke Okanohara, CEO of Matlantis. “Our study shows nearly every materials research team has had to leave promising projects on the shelf, not for lack of ideas, but because their tools couldn’t keep up. It’s a stark wake-up call that traditional trial-and-error methods are too slow and costly for today’s pace of innovation. We’re at a turning point where the industry must embrace faster, smarter approaches to ensure crucial discoveries don’t get left behind.”
The report also highlights that trust and security remain paramount. Every team surveyed expressed concerns about protecting intellectual property when using external or cloud-based tools, and only 14% felt “very confident” in the accuracy of AI-driven simulations. In sum, materials scientists are hungry for speed and efficiency, but solutions must also earn their confidence.
“These findings echo what we’re seeing at Empa,” said Vladyslav Turlo, PhD, Team Leader – Modeling & Simulations, Empa, a Matlantis partner organization and customer. “The need for computational speed is real if we want to effectively guide experimental materials design, but we need to ensure the simulation data is trustworthy and the sensitive parts are well-protected. Industry leaders are asking how we can accelerate discovery while maintaining control over data and validation. Initiatives like this one help clarify both the challenges and the path forward.”
For Matlantis, the survey’s findings validate the urgency of its mission. The company’s cloud-native Matlantis platform was built to accelerate materials discovery through AI-accelerated, high-speed simulations without compromising on scientific fidelity or data security. By integrating advanced neural-network potentials with proven physics, Matlantis enables researchers to iterate rapidly while maintaining confidence in the results. The new report positions Matlantis at the heart of this transformation, highlighting how AI-powered simulation can help bridge the gap between researchers’ ambitions and their current technical limits.
“At Matlantis, we anticipated this inflection point and designed our platform to deliver acceleration and efficiency without sacrificing accuracy or security,” Okanohara said. “Our AI technology lets scientists run high-fidelity simulations in hours instead of months, with the assurance that those results can be trusted. As R&D teams race to develop sustainable batteries, catalysts, semiconductors and more, Matlantis is here to ensure they can carry those projects through to breakthrough results rather than abandoning them mid-stream.”
The full findings and analysis are detailed in the Accelerating Discovery: AI Trends in Materials R&D report, which is available for download today on Matlantis’s website. By shining a light on both the progress and pain points in current R&D practices, Matlantis aims to spark industry-wide dialogue on how to fully realize the potential of AI in materials development. The company will continue working with partners across academia and industry to drive that evolution forward, so that scientific discovery keeps pace with global challenges and opportunities.
