思朗科技十年磨一剑，用 MaPU 与 " 天穹 " 超算重构全球科学智能科研新范式 人工智能、工业智能已迈入深耕落地的成熟期，科学智能（AI for Science，AI4S）则悄然崛起为科技突破的 " 隐形引擎 "。 AI 赋能基础科研，打破 "猜假设、做实验" 的传统模式，为微观探索、新药研发等前沿领域开辟全新方向 。 2025年11月，特朗普签署行政令启动"创世纪计划"，以"曼哈顿计划2.0"的格局推动AI与科研深度融合，通过整合资源重构 科研全流程，让AI从辅助工具升级为科学发现核心驱动力。这一理念的源头，可追溯至2018年科研界的反思。彼时AI在科 研中仅零散作为工具，难以突破"被动辅助"瓶颈，无法触及核心规律探索。基于此，科研界萌生让AI深度参与科学发现的 构想，" AI for Science "概念正式诞生，中文定名为" 科学智能 "。此后，全球范围内包括加州理工、谷歌 DeepMind 等国 际顶尖科研机构，以及我国多所高校和阿里、华为等科技企业纷纷布局，相继成立相关实验室，科学智能的内涵也不断升 级，从最初提升科研效率，逐步拓展到参与科研全流程。2024 年诺贝尔物理、化学奖中 AI 元素 ...

Core Viewpoint - The "Panshi" scientific foundational model, developed by a team from the Chinese Academy of Sciences, was unveiled at the 2025 World Artificial Intelligence Conference in Shanghai, aimed at assisting scientists in interdisciplinary research [1] Group 1: Model Capabilities - The "Panshi" model features core capabilities such as scientific literature extraction and integration, scientific knowledge representation and reasoning, and scientific tool orchestration and planning [1] - It enables a complete research process from hypothesis generation to experimental validation and discovery of laws, functioning as a cross-disciplinary research support platform combining AI and science [1] Group 2: Customization and Development - The model is deeply customized for the scientific field based on domestic open-source large models, mastering core theorems, laws, and professional knowledge across six major disciplines: mathematics, physics, chemistry, biology, earth science, and astronomy [1] - Two scientific intelligent entities, "Literature Compass" and "Tool Scheduling Platform," have been developed to assist researchers in various tasks [1] Group 3: Application Examples - In mechanics research, the model demonstrates strong capabilities in understanding and predicting scientific data, efficiently calculating surface pressure fields of high-speed train models in various fluid environments to support design [1] - In high-energy physics research, the model can automatically decompose and efficiently plan particle physics research tasks, significantly enhancing particle simulation speed and reconstruction efficiency, aiding in the exploration of the fundamental composition of matter and the basic laws of the universe [1]