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Materials are foundational resources for human civilization and have long been central to many disruptive technological revolutions. Their development plays a critical role in driving industrial upgrading and social progress.

However, traditional materials R&D faces high technical barriers, lengthy timelines, and massive engineering demands — often requiring 10 to 20 years of development, making it difficult to meet current industrial demand for diverse materials.

According to data from the Ministry of Industry and Information Technology and Choice, China's self-sufficiency rate for high-end new materials remains low, with imported materials accounting for 84% of supply. The import dependency rate for key materials stands at 52%, while that for high-temperature alloys reaches 50%, creating strong demand for domestic substitution.

In recent years, as AI, big data, and IoT technologies have been applied in the materials sector, data-driven approaches marked by "big data + AI" have become the fourth paradigm of materials science development. Materials R&D, production, sales, and application are entering an intelligent development stage.

Through large-scale data collection and training, many companies are attempting to introduce AI into materials design and R&D to improve efficiency and reduce costs.

Hard Kraken recently spoke with DeepMaterial (Suzhou) Technology Co., Ltd. (hereinafter referred to as "DeepMaterial"), a company focused on the industrialization of AI + high-end metal materials.

Founded in February 2021, DeepMaterial leverages materials computing, materials informatics, machine learning, and deep neural networks to accelerate the R&D and industrialization of high-end metal materials. The company also provides one-stop services including independent product R&D, sample design, metal powder materials, production processing, quality inspection, and performance analysis.

Unlike most AI + new materials companies, whose business models focus on software sales and R&D contract work, DeepMaterial concentrates on the materials themselves — dedicated to developing metal material products for production and sale.

Currently, DeepMaterial has developed multiple high-end metal 3D printing materials and high-performance die-cast aluminum materials, and has entered mass production.

R&D personnel account for 80% of the team, comprising senior algorithm scientists and materials scientists with extensive technical expertise and product implementation experience in materials R&D, process optimization, algorithm design, mechanical design, metal die casting, and finished product quality inspection. The team has mass production and delivery capabilities.

To address the long R&D cycles in materials development, DeepMaterial has developed proprietary high-throughput equipment that enables standardized, streamlined, and automated collection of experimental data, ensuring high-quality data production. The company has also deployed high-throughput laboratories and industrial-grade big data platforms to support fully digitalized development workflows.

Take high-temperature alloys as an example. The high-throughput laboratory collects extensive experimental data through machine learning and deep learning technologies. Team members then build a generative model algorithm suitable for high-temperature alloy materials, followed by materials testing and performance optimization.

DeepMaterial's AI-driven R&D model offers clear advantages: faster development speed and shorter cycles. Compared to the minimum five-year R&D period of traditional laboratory models, DeepMaterial can complete development of three to five new materials in just six months, substantially reducing R&D costs.

High-throughput platform workflow diagram

Additionally, AI technology demonstrates greater consistency in data processing and process parameters, with strong algorithm transferability, anti-disturbance capability, and multi-element joint tuning — better handling real-world perturbations. Materials developed through the platform meet performance standards required by aerospace and other demanding fields.

DeepMaterial founder Zexuan Wang told Hard Kraken, "The feasibility of the company's AI R&D platform has been validated in practical applications." By the end of 2023, the company completed R&D on three advanced alloy material pipelines: high-temperature alloys, high-strength aluminum alloys, and die-cast aluminum alloys.

"Our materials have fully independent intellectual property rights. Compared to imported materials, they offer certain advantages in performance and cost, enabling domestic substitution and effectively solving bottleneck problems. These materials can be widely applied in aerospace, military, new energy vehicles, consumer electronics, and other fields with enormous market demand," Wang said.

Metal material product images

On the domestic customer front, DeepMaterial has established partnerships with benchmark clients in aerospace and military industries, with products entering small-batch trial production. The company has also signed new energy vehicle material orders exceeding 10 million yuan with a leading automotive brand.

Currently, many high-end metal materials remain in a "bottleneck" stage, with expanding demand for domestic substitution. AI technology has shown tremendous potential in materials design, promising to further develop China's metal materials market, drive intelligent transformation of the industry, and achieve autonomous control of key materials.

As of early 2022, DeepMaterial completed angel and Pre-A funding rounds totaling tens of millions of RMB, with investors including BlueRun Ventures, Linear Capital, and Source Code Capital.

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