"Chuangcai Shenzao" Raises Tens of Millions of RMB in Series A Funding to Accelerate Metal New Material R&D with AI | Linear Portfolio

AI-powered metal new materials R&D company Deep Material (创材深造) recently completed a Series A financing round totaling tens of millions of RMB. On September 10, the company will officially release its self-developed software-hardware integrated materials intelligence agent (DM Agent), further accelerating its technology commercialization.

Deep Material was founded in 2021. The company creatively embeds artificial intelligence into the entire metal new materials R&D workflow, building everything in-house from algorithmic models to high-throughput laboratories to materials data systems. It has formed a multimodal-integrated "materials intelligence agent" plus high-throughput experimental system. Through this approach, Deep Material has compressed the R&D cycle from the traditional several years or even more than a decade to as fast as under two months.

Linear Capital was the lead investor in Deep Material's angel round and has continued to increase its stake in subsequent rounds.

AI-powered metal new materials R&D company Deep Material (创材深造) recently completed a Series A financing round totaling tens of millions of RMB, with Hesijia Capital and Chenhui Capital participating. The proceeds will be used for iterative new materials R&D, upgrades to its high-throughput automated laboratory, AI model development, and scaled application in vertical industry scenarios.

According to the company, its self-developed software-hardware integrated materials intelligence agent (DM Agent) will be officially released on September 10, further accelerating its technology commercialization.

"Materials R&D isn't something you can nail just by building a model. The difficulty lies in closing the data loop and achieving industrial落地," said founder Xuanze Wang. In his view, this explains why AI has already produced multiple listed companies in pharmaceuticals and other fields, yet few have succeeded in the metal materials space. What investors value is precisely that Deep Material has already proven out an industrialization path in the "AI + materials" domain and has the conditions for rapid scaling.

Founded in 2021, Deep Material creatively embeds artificial intelligence into the entire metal new materials R&D workflow, building everything in-house from algorithmic models to high-throughput laboratories to materials data systems. This path is closely tied to Wang's background: bachelor's and master's degrees from Shanghai Jiao Tong University in artificial intelligence, raised in Anshan, Liaoning — a steel industry stronghold — in a family steeped in materials science. In 2015, he first realized AI could transform materials R&D, but at the time the technological, computing, and data conditions were not yet mature. Five years later, he saw the opportunity approaching. "In the metal materials field, what customers care about most is performance metrics — precisely the direction AI can optimize with precision."

In metal materials R&D, data is a widely acknowledged bottleneck: hard to obtain, wrong dimensions, poor consistency. Deep Material chose to bypass these obstacles: using self-developed high-throughput equipment to generate high-consistency experimental data at low cost and high efficiency, then having large models orchestrate specialized small models to complete formulation and process optimization, forming a multimodal-integrated "materials intelligence agent" plus high-throughput experimental system. Under this system, the R&D cycle has been compressed 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 Deep Material to make a different choice on business model. Wang believes future materials companies must build "dual capabilities": improving R&D efficiency through algorithms while also possessing industrialization落地 capabilities. "A business model purely providing R&D services has limitations," he emphasized. "If you can't control the scaled production环节, the value of R&D gets diluted." Therefore, starting in 2023, the company shifted from taking contract R&D orders to proactively selecting materials categories with large market demand and high process barriers for self-directed立项 R&D.

The first products are high-strength aluminum alloys for 3D printing — strength exceeding 550MPa, meeting aerospace-grade requirements, and with no precious metal content, their cost is only one-third of comparable overseas products, offering significant cost advantages. These materials have already entered validation and procurement processes at aerospace research institutes and leading 3C OEM manufacturers.

This strategic upgrade stems from Deep Material's forward-looking assessment of 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 was approximately $5.87 billion in 2024, expected to grow to $6.68 billion in 2025, with potential to surpass $20 billion over the next decade at a CAGR of approximately 13.7%. However, in terms of specific materials categories, there are fewer than thirty metal material grades available for stable printing, and they are heavily concentrated in conventional grades and properties of aluminum alloys, high-temperature alloys, titanium alloys, and stainless steel — struggling to meet the customized demand for special properties like high strength and lightweighting in aerospace, consumer electronics, and other fields.

"The biggest opportunity in this industry is using materials breakthroughs to unlock downstream application constraints — for example, letting aerospace and consumer electronics eliminate 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 manufacturers, "who now actively ask whether we can make new materials that are light, strong, and cheap."

Deep Material's business is not 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-generate data — self-develop models — production line落地" model has opened up additional application scenarios.

Over the next two years, they hope to have one or two materials achieve mass production profitability; in five to ten years, cover more industries and complete an IPO. On longer-term goals, Wang hopes to "free human progress from materials limitations."