Core Insights - The emergence of AI for Science (AI4S) is transforming scientific research by enabling autonomous intelligence in chemistry, moving from "artificial design" to "self-evolving" capabilities [1] - Deep Principles, a startup focused on AI for Chemistry and Materials, has recently completed over 100 million RMB in funding, marking Ant Group's first investment in the AI4S sector [2][14] - The founders of Deep Principles, both MIT PhD graduates, aim to revolutionize the research and development process in materials science by integrating AI with quantum chemistry and high-throughput experimental techniques [4][10] Company Overview - Deep Principles was established in 2024 and focuses on accelerating the entire process of materials innovation by combining AI, quantum chemistry, and high-throughput experiments [4][10] - The company has developed a proprietary Reactive AI platform to address challenges in material chemistry simulations, achieving significant improvements in prediction speed and accuracy [6][10] - Within a year of its establishment, Deep Principles has secured over 10 million RMB in commercial orders, indicating strong market demand for its innovative solutions [10] Technology and Innovation - Deep Principles utilizes diffusion generative models to predict reaction pathways and transition states, significantly reducing computation time from days to mere seconds [6][10] - The company has created a comprehensive product system that integrates various research tools, allowing engineers to automate complex experimental processes and enhance productivity [9][10] - The language model LLM-EO enables researchers to generate new molecular candidates based on natural language inputs, streamlining the hypothesis generation and research design phases [9] Market Context - The AI4S paradigm is gaining traction across multiple scientific disciplines, including chemistry, materials science, and life sciences, driven by the need for interdisciplinary collaboration and innovation [3] - The global investment landscape is increasingly recognizing the potential of AI in scientific research, with significant funding rounds for companies like CuspAI and Periodic Labs, indicating a robust market for AI-driven scientific solutions [13][14] - Deep Principles aims to leverage China's manufacturing strength and large market potential to drive scientific discovery and industrial application, positioning itself strategically in the AI4S landscape [14][15]

《商道创投网》2025年11月24日从官方获悉：深度原理近日完成了由戈壁创投管理的阿里巴巴创业者基 金大湾区基金与蚂蚁集团共同领投，联想创投、Taihill Venture超额加注，BV百度风投继续加注的超亿 元A轮融资。 《商道创投网》创业家会员·单位简介 深度原理是一家由麻省理工学院（MIT）背景团队创立的科技创新公司，创始团队在AI for Science交叉 领域拥有深厚积淀。公司首创的扩散生成模型在《Nature Computational Science》和《Nature Machine Intelligence》等顶级期刊发表封面论文，其研发的Agent Mira™材料发现智能体能够基于实际研发需 求，智能调用自研算法模型和高精度数据集，具备分子结构设计、化学反应预测等能力，推动AI for Science从概念走向可落地、可规模化的产业能力。 《商道创投网》创业家会员·本轮融资用途是什么？ 深度原理创始人贾皓钧博士表示，本轮融资将主要用于加速Agent Mira™的研发与升级，推进L4高通量 自主实验室AI Materials Factory™的建设与布局，以及深化与国际国内头部客户的合作，巩 ...

以下文章来源于Z Potentials ，作者Z Potentials 2025年初，锦秋基金参与对AI for Science（AI4S）赛道明星企业「深度原理 Deep Principle 」 的亿元级Pre-A轮战略融资。 锦秋基金，作为12 年期的 AI Fund，始终以长期主义为核心投资理念，积极寻找那些具有突破性技术和创新商业模式的通用人工智能初创企业。 锦秋基金被投企业—— 「深度原理 Deep Principle」 完成超亿元人民币A轮融资。本轮由戈壁创投管理的阿里巴巴创业者基金大湾区基金(简称AEF大 湾区基金)与蚂蚁集团共同领投，现有股东联想创投、Taihill Venture 超额加注，BV百度风投继续加注，多家机构参与。 本轮融资将主要用于 三大方向 ： 1. 加速 Agentic AI for Materials Discovery 材料发现智能体 Agent Mira ™ 的研发与升级； 2. 推进 L4 高通量自主实验室 AI Materials Factory ™ 与其研发管线的建设与布局； 3. 深化与国际和国内头部客户的合作，巩固技术落地领先优势。 Z Potentials ...

图片来源： 深度原理 据 ZP 获悉， AI for Science 领域的技术先锋企业「深度原理 Deep Principle」完成超亿元人民币A轮融资。 本轮由戈壁创投管理的阿里巴巴创业者基金大 湾区基金(简称AEF大湾区基金)与蚂蚁集团共同领投，现有股东联想创投、Taihill Venture 超额加注，BV百度风投继续加注，多家机构参与。 本轮融资将主要用于三大方向： 1. 加速 Agentic AI for Materials Discovery 材料发现智能体 Agent Mira ™ 的研发与升级； 2. 推进 L4 高通量自主实验室 AI Materials Factory ™ 与其研发管线的建设与布局； 3. 深化与国际和国内头部客户的合作，巩固技术落地领先优势。 技术领军：顶尖团队驱动模型持续突破 公司在扩散生成模型与大语言模型两条生成式 AI 路线同步推进、互补协同，形成"Diffusion + LLM"并进架构，为后续的智能体化交付奠定基础。 这些经 由顶级期刊验证的生成式 AI 模型的系统性进步，正在把 AI for Science 从概念推进为可落地、可规模化的产业能力。 「深度原 ...

摘要 ： 戴纳科技正式宣布完成由中金私募旗下基金领投的B轮融资。此次融资将重点用于戴纳科技AI+黑灯实验室核心技术迭代。 特别提示 微信机制调整，点击顶部"仪器信息网" → 右上方"…" → 设为 ★ 星标，否则很可能无法看到我们的推送。 近日，北京戴纳实验科技股份有限公司（以下简称 "戴纳科技"）近期正式宣布完成由中金私募旗下基金领投的B轮融资。此次融资将重点用于 戴纳科技AI+黑灯实验室核心技术迭代，标志着戴纳科技在AI+黑灯实验室领域的行业引领地位获得资本深度认可，也为公司未来发展注入强劲 动力。 作为AI for Science领域的创新标杆，戴纳科技打造的 "AI + 黑灯实验室" 解决方案实现了颠覆性突破 —— 系统可24小时自主完成实验规 划、样品处理、仪器操作、试剂运送、数据分析全流程，单日实验效率大幅提升。 关于戴纳科技 北京戴纳实验科技股份有限公司成立于2005年，由原赛默飞世尔团队创立，是国内 "AI+黑灯实验室"解决方案的先行者和领先者。公司以自 主算法及自研自动化系统为核心，提供从黑灯实验室设计、实施、交付的全生命周期服务，服务客户涵盖石化、新能源、日化等领域的领军企 业及各高校、科 ...

Core Insights - The future business blueprint consists of two main components: frontier organizations and super individuals [4][5][6] - The transition to a new operational paradigm is driven by the digital transformation of organizations, enabling AI to empower every process and individual within [4][6] - The concept of "Intelligence on Tap" signifies the ability to access intelligence anytime and anywhere, fundamentally changing organizational operations and business models [4][6] Summary by Sections Frontier Organizations - Frontier organizations emerge from successful digital transformation, where knowledge is converted into comprehensive data reserves [4][6] - These organizations will exhibit enhanced intelligence through human-machine collaboration, leading to a new operational phase [4][6] Super Individuals - The evolution of individuals will focus on becoming super individuals who can command knowledgeable machines to assist in tasks [5][6] - The ratio of humans to machines is expected to shift from 1:1 to 1:100, emphasizing the importance of human capability in this new landscape [6][8] Key Capabilities for Super Individuals - Super individuals must develop the ability to learn and adapt, moving from a mindset of "I can't" to "I can direct machines" [11][13] - The core competency of super individuals lies in leveraging human common sense and insights to guide machines effectively [14][16] Communication and Personal Branding - Effective communication, persuasion, and personal brand building are essential skills for super individuals in the information age [16][24] - The ability to manage one's digital footprint is crucial for distinguishing oneself in a competitive landscape [24][26] The Future of Work - The traditional age-related career milestones are shifting, with older individuals becoming increasingly valuable due to their retained agency and experience [35][36] - The focus is on providing exceptional value and exploring unknowns rather than merely fulfilling tasks [39][41] Philosophical Considerations - The ultimate goal transcends mere productivity; it involves exploring the meaning of existence and human purpose in the age of AI [31][39] - The challenge lies in maintaining human agency and avoiding the pitfalls of over-reliance on machines [30][31]

在全球科技竞争日益激烈的背景下，人工智能（AI）作为引领产业变革的颠覆性技术，正深刻重构化 工行业的研发范式。日前，在2025石化化工数字化转型发展大会"AI+化工研发创新"论坛上，产学研各 界专家分享AI与化工研发深度融合的前沿成果与实践经验，共同探讨AI赋能科学研究（AI for Science， AI4S）推动行业智能化转型的路径与方向。 中国科学技术大学江俊教授团队开发出的"机器化学家"系统，则通过融合AI算法与自动化实验平台，能 够将55万种催化剂配方的筛选范围通过AI快速收敛，仅用几周时间就能完成传统方法需要数年的研发 工作。 而在国际上，AlphaFold2已能高精度预测2.14亿种蛋白质三维结构，分析速度达每秒9种；金风科技 （002202）利用AI优化风电场布局，通过高精度风速预测与机组协同控制，年发电量提升8%，运维成 本降低20%；沙特阿美推出Aramco Metabrain大模型，规模达2500亿参数，覆盖90余年积累的工程、地 质和运营数据，支撑核心流程智能化升级……一批创新成果的出现，证明着AI正从辅助工具升级为科 研核心驱动力，显著提升成果转化效率 ，成为发展新质生产力的生动实践 ...

国际知识产权学院落地于粤港澳大湾区，旨在服务国家知识产权强国战略，助力大湾区打造高水平知识 产权人才高地，为全球知识产权治理与创新发展提供智力支持与人才保障。未来，该学院将面向未来科 技与国际知识产权治理需求，开展跨学科、跨领域的高层次人才培养与创新研究，推动知识产权规则与 科技、产业发展的深度融合。 新京报讯 据北京大学国际法学院消息，11月15日，以"AI for Science"为主题的2025西丽湖论坛开幕式暨 主论坛在深圳大学城国际会议中心举行。作为本届论坛的重要环节，由国家知识产权局与北京大学共同 建设的国际知识产权学院正式成立。 该学院由北京大学国际法学院具体承建，充分发挥北大法学的深厚基础和深圳的独特区位功能，通过南 北联动，实现优势互补，努力打造集教育、科研、智库和国际合作于一体的、具有国际影响力的知识产 权人才培养高端平台。 ...

Core Viewpoint - The future development of artificial intelligence (AI) is centered around achieving satisfactory general artificial intelligence (AGI), which will significantly impact various sectors including science, strategy, and economic competition [2][3]. Group 1: Directions Towards AGI - The journey towards AGI will inevitably focus on four key directions: continuous evolution of large models, embodied general intelligence, AI for science, and AI safety governance [5][8]. - In the past five years, China has made remarkable progress in large model development, reaching a competitive level internationally [7]. - Embodied intelligence is crucial for enhancing robots' capabilities, allowing them to perform tasks that were previously difficult due to their rigid nature [8]. - AI for science is expected to revolutionize scientific research methodologies within the next 5 to 10 years, making collaboration between scientists and AI essential for competitive advantage [9]. Group 2: Risks and Governance - The development of AI poses significant safety risks, as it can potentially lead to loss of control and conflict with human intentions [10][11]. - AI algorithms inherently possess characteristics such as lack of robustness, uncertainty, and non-interpretability, which can impact societal values and ethics [11]. - Addressing the "survival risk" associated with AI requires the development of provably safe AI systems, leveraging theories from cryptography and game theory [12]. Group 3: Future of Robotics - The next decade is anticipated to transform robots from mere tools into life partners, capable of understanding the world and performing various tasks [14][17]. - Robots will increasingly collaborate with humans in industrial settings and provide assistance in community services, such as elderly care [17]. - The robotics industry will benefit from open-source collaboration to accelerate technological advancements and reduce innovation costs [17]. Group 4: Market Potential - The AI market is projected to reach a trillion-dollar scale as it empowers various industries, with open-source initiatives playing a crucial role in fostering commercial growth [19][20]. - The focus on intelligent terminals as potential AI entry points highlights the importance of integrating AI into everyday life, particularly in the automotive sector [22].

Core Insights - The rapid rise of AI for Science in scientific research is a significant topic, emphasizing the integration of AI with traditional technologies [1] - AI can assist quantum physicists in constructing quantum error correction decoders, enhancing quantum computing research [1] - The Google quantum chip Willow has achieved a significant reduction in quantum error correction errors, improving the large-scale usability of quantum computing [1] - Future applications of AI are expected to greatly benefit the accuracy and speed of quantum computing [1]