DeepMaterial Raises Tens of Millions of RMB in Series A Funding, Using AI to Compress Metal Materials R&D Cycle from 10 Years to 2 Months | BlueRun Portfolio

DeepMaterial, an AI-powered metal new materials R&D company, recently completed a Series A financing round of tens of millions of RMB, with joint investment from HeShiJia and Chenhui Capital. The proceeds will fund iterative new materials R&D, upgrades to its high-throughput automated laboratory, AI model development, and scaled deployment across vertical industry scenarios.

BlueRun Ventures was DeepMaterial's Pre-A round investor.

Notably, the company's self-developed integrated hardware-software materials intelligence agent (DM Agent) will officially launch on September 10, further accelerating its technology commercialization.

"Materials R&D isn't something you can crack just by building a model — the real challenge lies in closing the data loop and achieving industrial落地," said founder Xuanze Wang. In his view, this explains why AI has already spawned multiple listed companies in pharmaceuticals and other fields, yet few have succeeded in the metal materials space. What attracted investors, he noted, was DeepMaterial's proven industrialization pathway in "AI + materials" and its capacity for rapid scaling.

Since its founding in 2021, DeepMaterial has creatively embedded artificial intelligence throughout the entire metal new materials R&D workflow, building everything in-house: algorithmic models, high-throughput laboratories, and materials data systems. This approach is tightly linked to Wang's background: a Shanghai Jiao Tong University bachelor's and master's in AI, raised in Anshan, Liaoning — a steel industry stronghold — in a family steeped in materials science. In 2015, he first recognized AI's potential to transform materials R&D, but the technology, compute power, and data conditions weren't yet ripe. Five years later, he saw the window opening. "In metal materials, what customers care about most is performance metrics — and that's precisely what AI can optimize with precision."

In metal materials R&D, data is widely acknowledged as the bottleneck: hard to acquire, wrong dimensions, poor consistency. DeepMaterial chose to sidestep these obstacles entirely: using self-developed high-throughput equipment to generate high-consistency experimental data at low cost and high efficiency, then employing large models to orchestrate specialized small models for formulation and process optimization — forming a multimodal-fused "materials intelligence agent" plus high-throughput experimental system. Under this framework, R&D cycles compress from the traditional several years or even more than a decade to as fast as under two months, with costs dropping by one to two orders of magnitude.

This capability has also led DeepMaterial to make distinctive choices on business model. Wang believes future materials companies must build "dual capabilities": boosting R&D efficiency through algorithms while maintaining industrialization and落地 capabilities. "A pure R&D services model has limitations," he emphasized. "If you can't control the scaled production环节, the value of your R&D gets diluted." Accordingly, since 2023, the company has shifted from taking contract R&D orders to proactively selecting high-market-demand, high-process-barrier materials categories for autonomous立项 R&D.

The first product line is high-strength aluminum alloy for 3D printing — exceeding 550MPa strength, meeting aerospace-grade requirements, and costing only one-third of comparable overseas products due to the absence of precious metal components. These materials have already entered validation and procurement processes at aerospace research institutes and top-tier 3C OEMs.

This strategic upgrade stems from DeepMaterial's forward-looking read on the metal additive manufacturing industry landscape. Global penetration of 3D printing metal materials remains in early stages. According to Precedence Research data, the global metal additive manufacturing market reached approximately $5.87 billion in 2024, projected to grow to $6.68 billion in 2025, with potential to surpass $20 billion over the next decade at a CAGR of roughly 13.7%. Yet in terms of specific material categories, there are fewer than thirty metal material grades suitable for stable printing, heavily concentrated in conventional grades and performance levels of aluminum alloys, high-temperature alloys, titanium alloys, and stainless steel — falling short of meeting customized demand for special properties like high strength and lightweighting in aerospace, consumer electronics, and other sectors.

"The biggest opportunity in this industry is using materials breakthroughs to unlock downstream applications — for instance, letting aerospace and consumer electronics shed excess weight from structural components," Wang said. Over the past decade, domestic manufacturing's attitude toward new materials has shifted from cautious to proactive, especially among consumer electronics makers: "They actively ask whether we can make materials that are simultaneously light, strong, and cheap."

DeepMaterial's business isn't limited to materials themselves; its high-throughput laboratory equipment and materials intelligence agent solutions are also entering the R&D systems of top domestic universities, national laboratories, and manufacturing enterprises. This "self-generated data — self-developed models — production line落地" model has opened additional application scenarios.

Over the next two years, they aim to have one or two materials achieve mass production profitability; in five to ten years, cover more industries and complete an IPO. For the longer horizon, Wang summarized in one sentence: "To ensure human progress is no longer constrained by materials."

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