Innovation China Conference: AI+ Materials/Industrials/Energy

May 14, 2024
The 2024 ‘AI+’ Innovation China Conference was held at Crowne Plaza Shanghai Fudan on 29 April

The highly-anticipated annual Innovation China Conference event took place in Shanghai this year, and was attended by over 150 guests. Themed “AI+”, it focused on 3 areas where AI will drive progress: Materials, Industrials & New Energy. With soaring costs for AI large language models today, China will find it difficult to catch up with the US in the short-term due to a variety of constraints. However, as it is an economy founded on manufacturing, China enjoys great potential for AI to empower its industrialization and productivity. This is why “AI+” was chosen as the theme for the 2024 Innovation China Conference, which is now in its 10th year.

Organized by the SEIF (Strategic Emerging Industries Foundation) and managed by CM Venture, Mitsubishi Corporation (Shanghai) is a lead sponsor. This year’s event was also supported by Shanghai Yangpu Technology & Innovation Group (STI), Chang Yang Campus & Cloud Valley (office property development).

Attended by Fortune 500 companies, LPs, CVCs, startups, policymakers and partners, this year’s MNC speakers included BAT (Matt Hodgson), L’Oréal (Valentin Peuch-Lestrade & Dr Yue Qiao), Nomura, Huawei and more. CVCs attending included Arkema, GE HealthCare, Goodman, Heraeus, Happiness Capital, ORIX Group Investment, SCG Investment, SABIC, Samsung Electronics, Sasol, ST Engineering, Zeiss Group, and more. We’ve picked out a few insights gleaned from the 22 speakers at the conference.

Mitsubishi - Takamasa Wakaki podium
Mr Takamasa Wakaki, Chairman and GM of Mitsubishi Corp (Shanghai)
Key takeaways from the AI+ conference
AI+ Materials

In Dr. Zhou Min’s keynote ‘AI for Materials’, she highlighted the challenges of AI in materials R&D, including:

  • Complexity of modeling, from microscopic to macroscopic levels
  • High-throughput experiments require high standards for equipment and significant investment
  • The biggest challenge remains the lack of data, especially experimental data, which holds the highest value

Materials Genome Engineering in China, by Dr Yi Jin
Dr. Yi Jin of 2Prime Ventures introduced the progress of China’s Materials Genome Initiative from recent years. The US and China launched their respective Materials Genome Initiatives in 2011 and 2015, both aiming to shorten the time for materials R&D while reducing costs. Currently, much of the work focuses on the field of materials data, including:

  • Using dynamic container technology and multi-source heterogeneous material data sources for retrieval, discovery, analysis, and mining
  • Automatic data mining from materials-related literature
  • Establishing a cloud infrastructure and big data platform bases for data sharing and circulation

From a company perspective, AI+ Material Companies often face a choice between becoming a platform company or a product company. Due to different industrial environments and ecosystems, different companies have made different choices:

  • Citrine Informatics (USA), which began exploring AI with materials in 2013, attempted their own product development. They chose to become an SaaS-based company, empowering other materials companies in their R&D and achieving significant commercial progress.
  • Bluepha (蓝晶微生物), a synthetic biology startup, designed and built a synthetic biology R&D platform called ‘Synbio OS’. The platform’s functions include cloud design software, autonomous strain construction, and a high-throughput automated small-scale bioreactor array. Utilizing the capabilities of this R&D platform shortens the R&D cycle and significantly raises R&D standards.
  • ShineHigh Innovation (杉海创新) and InnoAero (云锦特导) have firmly taken the productization route, launching highly-recognized products in the fields of supramolecular biology and high-performance copper alloys, respectively.

Our POV on AI+Materials can be summarized as:

  • AI’s impact on the materials industry is going to accelerate and potentially huge. New materials technologies used to be “technology push” (new materials invented, looking for applications), with AI, it could become “market pull” (market needs identified, AI helps quickly come up with new materials). CM’s first investment in the area, a Chinese company called Innoaero, is a case-in-point.
  • We see west’s advantage in the area as loads of data residing in large companies, like BASF. A good AI platform/tool start-up has good potential in helping large companies to utilizing that data, with SAAS model.
  • We see China’s advantage in the area as quick/low-cost experimentation and rapid re-iteration to test what AI model comes up with – thus quickly identifying new materials if a clear market opportunity exists.
  • In short, we are looking at platform/SAAS investments in the west, and product companies in China.

Overview of AI for Materials, by Dr Min Zhou

AI+ Industry / New Energy

AI computing is very power-intensive. The evolution of AI systems in the last two years has led to higher requirements in power systems to support its operations.

Machine Learning Applications & AI in New Energy, Chen Shengjun
Mr. Chen Shengjun, CTO & VP of Concord New Energy (CNE), shared how AI is used in new energy systems:

  • Wind power forecasting
  • Application research in controlled nuclear fusion
  • Modeling & big data analysis to ensure safety in energy storage cells/systems
  • Lithium battery production control & management
  • Wind power distribution/energy storage transactions
  • Research and development of new energy sources, such as hydrogen fuel and more

A few startups have also explored how AI supports new energy markets and new energy development:

  • TsingRoc Intelligence (清鹏智能), Teddy Li: Their model understands complex system generation strategies and can solve dynamic joint optimization problems in complex systems, achieving comprehensive multi-objective optimization. In practical applications, it has effectively increased the operating revenue of energy storage assets and virtual power plants.
  • TacSense Technology (钛深科技), Wang Xiaoyang: Their compact, high-sensitivity, intelligent tactile sensors can accurately detect internal changes in lithium batteries not visible to the naked eye. Through big data and AI analysis, their systems can predict thermal runaways before actual changes in battery temperature and voltage, allowing safety measures to be taken early.

There is a wide possibility of application scenarios of these new technologies mentioned. With AI and big data behind them, the efficiency and effectiveness levels can only get better, and with that, we believe the future for AI+ Materials/Industrials/Energy is very promising.

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