近日，就锂电池设计自动化BDA软件的前景，以及我国材料领域工业仿真软件的现状和未来，郑家新 接受了证券时报记者专访。 证券时报记者：屹艮科技联合北大研发的BDA电池设计自动化软件，是AI赋能工业的核心成果，当初 为何选择锂电池研发作为切入点？ 北京大学联合屹艮科技成功研发出下一代锂电池设计自动化BDA软件，标志着在全球锂电行业，我国 率先构建起以人工智能结合跨尺度物理仿真为核心的电池研发新范式，为全球新能源产业升级注入强劲 的"中国动能"。 成立于2020年的屹艮科技，是一家专注于工业仿真软件研发的国家高新技术企业，总部位于深圳市南山 区。公司创始人兼 CEO、北京大学深圳研究生院新材料学院长聘副教授郑家新，已主持 2 项国家重大 项目，发表 SCI 论文160余篇，曾获深圳市自然科学一等奖、深圳市青年科技奖。 郑家新：新能源是数万亿规模的产业，电池作为核心部件，迫切需要先进的数字化工具来赋能研发，就 像EDA软件对芯片行业的作用一样。现在绝大多数新能源企业的研发模式还是靠"手搓试错"，靠调配方 反复试验，效率极低，和早期芯片研发很相似。针对这个行业痛点，行业近几年发展出BDA（电池设 计自动化）的概念，希望 ...

Core Viewpoint - Siemens is at the forefront of integrating AI into industrial processes, exemplified by its Industrial Copilot and Industrial Foundation Model, which enhance automation and efficiency in manufacturing environments [9][30][65]. Group 1: Historical Context and Development - The journey of Siemens in industrial AI began in 1964 with the creation of the Zuse Graphomat Z64, marking the start of computer-generated art and the long evolution towards AI in industry [2][4]. - Over the past 60 years, Siemens has transformed its Erlangen factory into a hub for over 100 AI applications, utilizing digital twin technology to mirror real-world processes [6][9]. Group 2: Industrial Copilot and AI Integration - The Industrial Copilot acts as a bridge between human language and machine operations, allowing users to issue natural language commands that the system translates into actionable tasks [10][18]. - This system significantly improves efficiency, enabling engineers to generate automation code quickly, reducing development time by nearly 50% and deployment time by 30% [14][15]. Group 3: Industrial Foundation Model (IFM) - The Industrial Foundation Model is a collection of models rooted in 150PB of validated industrial data, designed to understand and operate within the constraints of industrial environments [24][28]. - Unlike general-purpose AI models, the IFM is tailored to comprehend machine language and industrial logic, making it suitable for complex manufacturing processes [25][28]. Group 4: Data and Knowledge as Competitive Advantages - Siemens possesses a unique data asset of 150PB, which spans various stages of product design and manufacturing, providing a competitive edge in AI model training [34][36]. - The company’s extensive experience and industry know-how are critical in navigating the complexities of data collection, cleaning, and model deployment in industrial settings [40][41]. Group 5: Strategic Moves and Future Outlook - Recent strategic actions include the acquisition of Altair for over $10 billion, enhancing Siemens' capabilities in industrial simulation and AI-driven optimization [67]. - Siemens is also focusing on reskilling its workforce to ensure that employees can effectively collaborate with AI technologies, emphasizing the importance of cultural acceptance of AI in industrial environments [62][65].