可将10年研发周期压缩到2个月，AI金属新材料公司获数千万融资｜硬氪首发

Core Insights - Deep Material, an AI and metal new materials R&D company, recently completed a multi-million A round financing led by He Shijia Capital and Chenhui Capital, with funds allocated for new material R&D, high-throughput automated laboratory upgrades, AI model development, and large-scale applications in vertical industries [1][2] Company Overview - Founded in 2021, Deep Material integrates AI throughout the metal new materials R&D process, including algorithm models, high-throughput laboratories, and material data systems, which is closely linked to the founder's background in AI and materials science [2][6] - The company aims to overcome data bottlenecks in metal materials R&D by using self-developed high-throughput equipment to generate consistent experimental data efficiently, reducing the R&D cycle from years to as little as two months and cutting costs significantly [2][6] Business Model and Strategy - Deep Material has shifted its business model from contract R&D to proactively selecting high-demand materials for independent R&D, focusing on high-strength aluminum alloys for 3D printing that meet aerospace standards at a significantly lower cost compared to overseas products [6][7] - The company anticipates that the global metal additive manufacturing market will grow from approximately $5.87 billion in 2024 to $6.68 billion in 2025, with a projected compound annual growth rate of about 13.7% over the next decade [7] Future Goals - In the next two years, Deep Material aims to achieve mass production and profitability for one or two materials, with a longer-term goal of expanding into more industries and completing an IPO within five to ten years [8] Investor Perspectives - He Shijia Capital views the investment as a recognition of Deep Material's ability to combine AI with intelligent additive manufacturing, expecting the DM Agent platform to accelerate low-cost material research and innovation [9] - Chenhui Capital is optimistic about the "AI + new materials" sector and believes that Deep Material's approach can transform traditional material R&D from trial-and-error to predictive methods, showcasing strong industrialization potential [9]