（原标题：让研发告别"手搓试错" 国产BDA软件赋能智造万亿锂电产业｜人工智能Al瞭望台） 通过自主研发的电池设计自动化（BDA）软件，原本耗时数月的材料实验仅需数日即可完成性能预 测；而锂电池企业在使用该软件后，研发成本大幅下降。这一幕正在宁德时代等新能源头部企业的研发 中心上演。当人工智能（AI）与锂电池这一新能源核心产业相遇，一场颠覆传统研发模式的产业革新 正悄然到来。 让研发告别"手搓试错" 我国已是全球锂离子电池生产与应用第一大国。EVTank数据显示，2024年中国锂离子电池出货量达到 1214.6GWh，同比增长36.9%，在全球锂离子电池总体出货量的占比达到78%，行业市值超过1万亿元。 但光鲜的产业规模背后，研发环节却长期受制于低效的传统模式。 "绝大多数锂电池企业的研发模式还是'手搓试错'，靠调配方反复做实验，效率较低。"屹艮科技创始人 兼首席科学家郑家新在接受证券时报记者采访时表示。锂电池研发堪称工业领域的"复杂系统工程"，其 核心挑战集中在"跨尺度、长流程、多因素"三大特性。 更严峻的是，当前商业化锂电池能量密度已接近极限，而具有超高能量密度潜力的新一代锂金属电池和 全固态电池，仍面临 ...

Core Viewpoint - The event "Intelligent New Materials Leading the Future" focused on the integration of artificial intelligence (AI) in the development and application of new materials, highlighting the importance of this intersection for industry advancement and collaboration among experts and enterprises [1][17]. Group 1: Event Overview - The event was co-hosted by multiple institutions including Beijing University of Chemical Technology Alumni Association and Fudan Technology Park Development Research Institute, attracting nearly a hundred industry elites [2]. - Key leaders and guests included representatives from various universities and companies, emphasizing the strong lineup and collaborative spirit of the event [2]. Group 2: Opening Remarks - Opening speeches highlighted the critical role of the new materials industry in global technological competition and economic development, addressing challenges in traditional R&D models and opportunities presented by AI [4]. - The introduction of the investment environment in Suqian High-tech Zone showcased local policies and support for the new materials industry, providing insights for collaboration and project implementation [4]. Group 3: Keynote Presentations - Experts shared cutting-edge research and industry insights, including applications of deep learning in predicting molecular properties and catalytic reactions, demonstrating AI's potential in foundational materials research [6]. - The concept of "AI + Dark Room Laboratory" was introduced, showcasing its advantages in enhancing R&D efficiency and the future trend of intelligent development in new materials [8]. - Machine learning methods for studying complex systems were discussed, providing theoretical support for new materials development and industrial catalysis optimization [10]. - The transformative impact of AI on the chemical industry was analyzed, focusing on its role in accelerating R&D, optimizing production processes, and reducing costs [12]. - Research on AI and big data platforms in energy materials design was presented, covering applications in various battery technologies and the establishment of relevant databases [14]. - The application of AI in simulating and designing key materials for lithium batteries was discussed, highlighting the importance of lithium-ion batteries and current R&D challenges [16]. Group 4: Roundtable Forum - A roundtable forum featured discussions on the opportunities and challenges in the new materials industry under the AI wave, emphasizing the need for collaboration between academia and industry to overcome technological bottlenecks [16]. - Participants shared insights on the value of AI in practical applications and investment perspectives, aiming to accelerate the industrialization process of new materials [16]. Group 5: Conclusion - The event successfully facilitated communication and collaboration in the new materials field, promoting the integration of AI technologies in R&D and production, contributing to the intelligent development of the new materials industry [17].