Enlightenment of general AI to chemical engineering AGI: construction of synergy system integrating data, computing power, tools, and mechanisms

GU Ziyi; WANG Xiangru; NIU Yingchun; WANG Yufei; ZHOU Tianhang

doi:10.11949/0438-1157.20251408

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Enlightenment of general AI to chemical engineering AGI: construction of synergy system integrating data, computing power, tools, and mechanisms

更新时间：2026-06-01

- Enlightenment of general AI to chemical engineering AGI: construction of synergy system integrating data, computing power, tools, and mechanisms
- CIESC Journal Pages: 1-20(2026)
- 作者机构：
  
  中国石油大学（北京）重质油全国重点实验室，北京 102249
- 作者简介：
- 基金信息：
- DOI：10.11949/0438-1157.20251408
  CLC： TQ 028.8
- Received：15 December 2025，
  
  Revised：2026-06-01，
  
  Online First：01 June 2026，
- 稿件说明：
移动端阅览
顾紫怡, 王湘茹, 牛迎春, 王彧斐, 周天航. 通用AI对化工AGI的启示：数据-算力-工具-机理协同体系构建[J]. 化工学报,

GU Ziyi, WANG Xiangru, NIU Yingchun, WANG Yufei, ZHOU Tianhang. Enlightenment of general AI to chemical engineering AGI: construction of synergy system integrating data, computing power, tools, and mechanisms[J]. CIESC Journal,
顾紫怡, 王湘茹, 牛迎春, 王彧斐, 周天航. 通用AI对化工AGI的启示：数据-算力-工具-机理协同体系构建[J]. 化工学报, DOI： 10.11949/0438-1157.20251408

GU Ziyi, WANG Xiangru, NIU Yingchun, WANG Yufei, ZHOU Tianhang. Enlightenment of general AI to chemical engineering AGI: construction of synergy system integrating data, computing power, tools, and mechanisms[J]. CIESC Journal, DOI： 10.11949/0438-1157.20251408

摘要

人工智能正深度重塑工业生产范式，化学工程领域中，构建可自主理解、预测、设计与优化复杂化学过程的化工领域通用人工智能（chemical engineering artificial general intelligence，化工AGI），是推动行业颠覆性创新的核心目标。立足化工“三传一反”核心特性，以数据-算力-工具-机理为脉络，剖析化工AGI四大发展方向：数据层面借鉴通用AI，提出多尺度模拟驱动的自主造血式数据范式，破解多尺度、高成本困境；算力层面聚焦专用芯片与硬件架构，论证化工专用计算机的必要性；工具层面延续通用AI自主化逻辑，探讨自动模拟、自动实验与自动生产控制对化工创新的助力；机理层面强调物理化学机理与数据模型深度融合，筑牢可信可解释根基。数据-算力-工具-机理协同形成自我强化生态，聚焦行业需求与技术场景适配性，为化工智能化转型提供系统性思考框架与发展蓝图。

Abstract

Artificial intelligence is profoundly reshaping the paradigm of industrial production. In the field of chemical engineering

the development of chemical engineering artificial general intelligence (Chemical Engineering AGI)

which is capable of autonomously understanding

predicting

designing and optimizing complex chemical processes

stands as the core goal to drive disruptive innovation in the industry. Based on the core characteristic of "three transports and one reaction" (mass transfer

heat transfer

momentum transfer

and chemical reaction) in chemical engineering

this paper analyzes the four key development directions of Chemical Engineering AGI with the framework of data

computing power

tools

and mechanisms. In terms of data

drawing on the experience of general AI

we propose a multi-scale simulation-driven self-sustaining data paradigm to address the dilemmas of multi-scale data acquisition and high acquisition costs. At the computing power level

it focuses on dedicated chips and hardware architecture

demonstrating the necessity of specialized computers for the chemical industry; at the tool level

it continues the autonomous development logic of general AI

exploring how automated simulation

automated experimentation

and automated production control can contribute to innovation in the chemical industry. On the mechanism front

we emphasize the deep integration of physicochemical mechanisms with data models to consolidate the foundation of model credibility and interpretability. The four dimensions of data

computing power

tools

and mechanisms are mutually reinforcing and collectively form a self-evolving ecosystem. Focusing on the fit between industry demands and technical scenario adaptability

this work provides a systematic thinking framework and development blueprint for the intelligent transformation of the chemical engineering industry.

关键词

Keywords

references

曾玉娇 , 肖炘 , 杨刚 , 等 . 基于机理与数据混合驱动的湿法磷酸生产过程代理建模与优化 [J ] . 化工学报 , 2024 , 75 ( 3 ): 936 - 944 .

Zeng Y J , Xiao X , Yang G , et al . Surrogate modeling and optimization of wet phosphoric acid production process based on mechanism and data hybrid driven [J ] . CIESC Journal , 2024 , 75 ( 3 ): 936 - 944 .

吉远辉 , 朱家华 , 穆立文 , 等 . 化工基础数据获取新范式: 机制+数据驱动 [J ] . 中国科学基金 , 2024 , 38 ( 4 ): 712 - 718 .

Ji Y H , Zhu J H , Mu L W , et al . New Paradigm for Determining Fundamental Chemical Engineering Data: A "Mechanism+Data Driven" Strategy [J ] . Bulletin of National Natural Science Foundation of China , 2024 , 38 ( 4 ): 712 - 718 .

陈宇翔 , 刘传磊 , 龚子君 , 等 . 机器学习辅助乙硫醇高效吸收溶剂分子设计 [J ] . 化工学报 , 2024 , 75 ( 3 ): 914 - 923, 封 1 .

Chen Y X , Liu C L , Gong Z J , et al . Machine learning-assisted solvent molecule design for efficient absorption of ethanethiol [J ] . CIESC Journal , 2024 , 75 ( 3 ): 914 - 923, 封 1 .

刘东飞 , 张帆 , 刘铮 , 等 . 机器学习势及其在分子模拟中的应用综述 [J ] . 化工学报 , 2024 , 75 ( 4 ): 1241 - 1255 .

Liu D F , Zhang F , Liu Z , et al . A review of machine learning potentials and their applications to molecular simulation [J ] . CIESC Journal , 2024 , 75 ( 4 ): 1241 - 1255 .

王凯 , 袁志宏 , 王笑楠 , 等 . 化学品智能制造的科学基础 [J ] . 中国科学基金 , 2023 , 37 ( 5 ): 811 - 817 .

Wang K , Yuan Z H , Wang X N , et al . Scientific Fundamentals of Artificial Intelligence Involved Chemical Manufacturing [J ] . Bulletin of National Natural Science Foundation of China , 2023 , 37 ( 5 ): 811 - 817 .

缪青海 , 王兴霞 , 杨静 , 等 . 从基础智能到通用智能: 基于大模型的GenAI和AGI之现状与展望 [J ] . 自动化学报 , 2024 , 50 ( 4 ): 674 - 687 .

Miao Q H , Wang X X , Yang J , et al . From Foundation Intelligence to General Intelligence: The State-of-Art and Perspectives of GenAI and AGI Based on Foundation Models [J ] . Acta Automatica Sinica , 2024 , 50 ( 4 ): 674 - 687 .

赵志君 , 庄馨予 . 中国人工智能高质量发展: 现状、问题与方略 [J ] . 改革 , 2023 ( 9 ): 11 - 20 .

Zhao Z J , Zhuang X Y . High-quality Development of Artificial Intelligence in China: Current Situation, Problems and Strategies [J ] . Reform , 2023 ( 9 ): 11 - 20 .

Schweidtmann A M , Esche E , Fischer A , et al . Machine Learning in Chemical Engineering: A Perspective [J ] . Chemie Ingenieur Technik , 2021 , 93 ( 12 ): 2029 - 2039 .

Herres-Pawlis S , Bach F , Bruno I J , et al . Minimum Information Standards in Chemistry: A Call for Better Research Data Management Practices [J ] . Angewandte Chemie International Edition , 2022 , 61 ( 51 ): e202203038 .

Willoughby C , Frey J G . Data management matters [J ] . Digital Discovery , 2022 , 1 ( 3 ): 183 - 194 .

李梓良 , 张玮 , 胡恒 , 等 . 基于生成对抗网络的化工过程低成本mGAN-NN建模 [J ] . 化工进展 , 2025 , 44 ( 4 ): 1978 - 1986 .

Li Z L , Zhang W , Hu H , et al . mGAN-NN method for low-cost chemical process modeling based on generative adversarial networks [J ] . Chemical Industry and Engineering Progress , 2025 , 44 ( 4 ): 1978 - 1986 .

Eibeck A , Lim M Q , Kraft M . J-Park Simulator: An ontology-based platform for cross-domain scenarios in process industry [J ] . Computers & Chemical Engineering , 2019 , 131 : 106586 .

Jiang Z H , Xu C , Liu J S , et al . A Dual Closed-Loop Digital Twin Construction Method for Optimizing the Copper Disc Casting Process [J ] . IEEE/CAA Journal of Automatica Sinica , 2024 , 11 ( 3 ): 581 - 594 .

郭洪霞 . 高分子粗粒化分子动力学模拟进展 [J ] . 高分子通报 , 2011 ( 10 ): 154 - 163 .

Guo H X . Progress on Goarse-Grained Molecular Dynamics Simulation of Polymers [J ] . Polymer Bulletin , 2011 ( 10 ): 154 - 163 .

Marrink S J , Risselada H J , Yefimov S , et al . The MARTINI Force Field: Coarse Grained Model for Biomolecular Simulations [J ] . The Journal of Physical Chemistry B , 2007 , 111 ( 27 ): 7812 - 7824 .

Wu Z H , Zhou T H . Structural Coarse-Graining via Multiobjective Optimization with Differentiable Simulation [J ] . Journal of Chemical Theory and Computation , 2024 , 20 ( 6 ): 2605 - 2617 .

Noid W G . Perspective: Advances, Challenges, and Insight for Predictive Coarse-Grained Models [J ] . The Journal of Physical Chemistry B , 2023 , 127 ( 19 ): 4174 - 4207 .

Li J H . The principle of compromise-in-competition: understanding mesoscale complexity of different levels [J ] . Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences , 2024 , 480 ( 2301 ): 20240031 .

Guo L , Meng F Y , Qin P F , et al . A Case Study Applying Mesoscience to Deep Learning [J ] . Engineering , 2024 , 39 : 84 - 93 .

Jain A , Hautier G , Moore C J , et al . A high-throughput infrastructure for density functional theory calculations [J ] . Computational Materials Science , 2011 , 50 ( 8 ): 2295 - 2310 .

Horton M K , Huck P , Yang R X , et al . Accelerated data-driven materials science with the Materials Project [J ] . Nature Materials , 2025 , 24 ( 10 ): 1522 - 1532 .

Jain A , Ong S P , Hautier G , et al . Commentary: The Materials Project: A materials genome approach to accelerating materials innovation [J ] . APL Materials , 2013 , 1 : 011002 .

Zhu J X , Cheng J . Machine Learning Potential for Electrochemical Interfaces with Hybrid Representation of Dielectric Response [J ] . Physical Review Letters , 2025 , 135 : 018003 .

Wang M , Ishii A , Sakaushi K . Accelerated Electrocatalyst Degradation Testing by Accurate and Robust Forecasting of Multidimensional Kinetic Model with Bayesian Data Assimilation [J ] . ACS Energy Letters , 2025 , 10 ( 1 ): 22 - 29 .

Zhao L F , Li Z , Wang Z C , et al . Active- and transfer-learning applied to microscale-macroscale coupling to simulate viscoelastic flows [J ] . Journal of Computational Physics , 2021 , 427 : 110069 .

Shahnam M , Gel A , Subramaniyan A K , et al . Uncertainty Quantification Analysis of Both Experimental and CFD Simulation Data of a Bench-scale Fluidized Bed Gasifier [R ] . Morgantown, WV: National Energy Technology Laboratory (NETL), Research and Innovation Center , 2017 .

Kusne A G , Yu H S , Wu C M , et al . On-the-fly closed-loop materials discovery via Bayesian active learning [J ] . Nature Communications , 2020 , 11 : 5966 .

Steinmann S N , Hermawan A , Bin Jassar M , et al . Autonomous high-throughput computations in catalysis [J ] . Chem Catalysis , 2022 , 2 ( 5 ): 940 - 956 .

Tom G , Schmid S P , Baird S G , et al . Self-Driving Laboratories for Chemistry and Materials Science [J ] . Chemical Reviews , 2024 , 124 ( 16 ): 9633 - 9732

Tran K , Ulissi Z W . Active learning across intermetallics to guide discovery of electrocatalysts for CO 2 reduction and H 2 evolution [J ] . Nature Catalysis , 2018 , 1 : 696 - 703 .

Mu Y , Li N , Hang L Q , et al . Investigation of the Rheological Behaviors of Polymeric Materials in the Film Casting Process through Multiscale Modeling and Simulation Method [J ] . Macromolecular Theory and Simulations , 2019 , 28 ( 3 ): 1900001 .

颜同安 . 数据驱动下用于碳捕集纳米多孔材料的筛选与设计 [D ] . 北京 : 北京化工大学 , 2023 .

Yan T A . Data-Driven Screening and Design of Nanoporous Materials for Carbon Capture [D ] . Beijing : Beijing University of Chemical Technology , 2023 .

Mao R Z , Zhang M , Wang Y R , et al . An integrated framework for accelerating reactive flow simulation using GPU and machine learning models [J ] . Proceedings of the Combustion Institute , 2024 , 40 ( 1-4 ): 105512 .

Mittal S , Vetter J S . A Survey of CPU-GPU Heterogeneous Computing Techniques [J ] . ACM Computing Surveys , 2015 , 47 ( 4 ): 1 - 35 .

Horowitz M . 1.1 Computing's energy problem (and what we can do about it) [C ] // 2014 IEEE International Solid-State Circuits Conference Digest of Technical Papers (ISSCC) . February 9-13, 2014 , San Francisco, CA, USA . IEEE , 2014 : 10 - 14 .

Shaw D E , Deneroff M M , Dror R O , et al . Anton, a special-purpose machine for molecular dynamics simulation [J ] . Communications of the ACM , 2008 , 51 ( 7 ): 91 - 97 .

Ohmura I , Morimoto G , Ohno Y , et al . MDGRAPE-4: a special-purpose computer system for molecular dynamics simulations [J ] . Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences , 2014 , 372 ( 2021 ): 20130387 .

Mayr C , Hoeppner S , Furber S . SpiNNaker 2: A 10 Million Core Processor System for Brain Simulation and Machine Learning [EB/OL ] . 2019 : arXiv : 1911 . 02385 . https://arxiv.org/abs/1911.02385 https://arxiv.org/abs/1911.02385

Joubert W , Archibald R , Berrill M , et al . Accelerated application development: The ORNL Titan experience [J ] . Computers & Electrical Engineering , 2015 , 46 : 123 - 138 .

Santos K , Moore S , Oppelstrup T , et al . Breaking the Molecular Dynamics Timescale Barrier Using a Wafer-Scale System [C ] // SC24: International Conference for High Performance Computing, Networking, Storage and Analysis . November 17-22, 2024 , Atlanta, GA, USA . IEEE , 2024 : 1 - 13 .

Hamm P . Toward an FPGA-based dedicated computer for molecular dynamics simulations [J ] . The Journal of Chemical Physics , 2025 , 162 ( 5 ): 054108 .

徐硕 , 毕可鑫 , 邱彤 , 等 . 智能过程系统工程(iPSE): 当过程系统工程与新一代人工智能相遇 [J ] . 化工进展 , 2026 , 45 ( 4 ): 1877 - 1899 .

Xu S , Bi K X , Qiu T , et al . Intelligent process systems engineering(iPSE): When process systems engineering meets new generation artificial intelligence [J ] . Chemical Industry and Engineering Progress , 2026 , 45 ( 4 ): 1877 - 1899 .

Trotter J D , Langguth J , Cai X . Targeting performance and user-friendliness: GPU-accelerated finite element computation with automated code generation in FEniCS [J ] . Parallel Computing , 2023 , 118 : 103051 .

Porcu R , Musser J , Almgren A S , et al . MFIX-Exa: CFD-DEM simulations of thermodynamics and chemical reactions in multiphase flows [J ] . Chemical Engineering Science , 2023 , 273 : 118614 .

Robertson L A , Shkrob I A , Lewis R , et al . High-Throughput Discovery Illuminates Design Principles and Limits for Long-Lived Charged Species in Organic Electrolytes [J ] . Journal of the American Chemical Society , 2025 , 147 ( 41 ): 37211 - 37222 .

Delgado-Licona F , Alsaiari A , Dickerson H , et al . Flow-driven data intensification to accelerate autonomous inorganic materials discovery [J ] . Nature Chemical Engineering , 2025 , 2 ( 7 ): 436 - 446 .

Boiko D A , MacKnight R , Kline B , et al . Autonomous chemical research with large language models [J ] . Nature , 2023 , 624 ( 7992 ): 570 - 578 .

Du W L , Yang S Y . The potential and challenges of large language model agent systems in chemical process simulation: from automated modeling to intelligent design [J ] . Frontiers of Chemical Science and Engineering , 2025 , 19 ( 10 ): 99 .

Ren L , Wang H T , Wang Y Q , et al . Foundation models for the process industry: challenges and opportunities [J ] . Engineering , 2025 , 52 : 53 - 59 .

鹿兰停 , 康胜 , 许文轲 , 等 . 化工领域中的人工智能: 人工神经网络技术的应用与前景 [J ] . 化工进展 , 2025 , 44 ( 8 ): 4808 - 4820 .

Lu L T , Kang S , Xu W K , et al . Artificial intelligence in the chemical industry: Applications and prospects of artificial neural network technology [J ] . Chemical Industry and Engineering Progress , 2025 , 44 ( 8 ): 4808 - 4820 .

侯卫锋 , 古绍武 , 张志铭 , 等 . 工业大模型赋能的新型流程工业智能工厂核心工业软件体系 [J ] . 中国科学(信息科学) , 2025 , 55 ( 7 ): 1783 - 1800 .

Hou W F , Gu S W , Zhang Z M , et al . Core industrial software system of the new process industrial intelligent factory enabled by the industrial large model [J ] . Scientia Sinica (Informationis) , 2025 , 55 ( 7 ): 1783 - 1800 .

Zhu Q , Zhang F , Huang Y , et al . An all-round AI-Chemist with a scientific mind [J ] . National Science Review , 2022 , 9 ( 10 ): nwac190 .

Huang L , Zhang C , Fu Y , et al . Natural-Language-Interfaced Robotic Synthesis for AI-Copilot-Assisted Exploration of Inorganic Materials [J ] . Journal of the American Chemical Society , 2025 , 147 ( 26 ): 23014 - 23025 .

Vyas J , Mercangöz M . Autonomous Industrial Control using an Agentic Framework with Large Language Models [J ] . IFAC-PapersOnLine , 2025 , 59 ( 6 ): 349 - 354 .

Zhong M , Tran K , Min Y M , et al . Accelerated discovery of CO 2 electrocatalysts using active machine learning [J ] . Nature , 2020 , 581 ( 7807 ): 178 - 183 .

Budhathoki S , Ajayi O , Steckel J A , et al . High-throughput computational prediction of the cost of carbon capture using mixed matrix membranes [J ] . Energy & Environmental Science , 2019 , 12 ( 4 ): 1255 - 1264 .

Karpatne A , Atluri G , Faghmous J H , et al . Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data [J ] . IEEE Transactions on Knowledge and Data Engineering , 2017 , 29 ( 10 ): 2318 - 2331 .

Psichogios D C , Ungar L H . A hybrid neural network-first principles approach to process modeling [J ] . AIChE Journal , 1992 , 38 ( 10 ): 1499 - 1511 .

Gunning D , Stefik M , Choi J , et al . XAI—Explainable artificial intelligence [J ] . Science Robotics , 2019 , 4 ( 37 ): eaay7120 .

Hinton G E . Learning distributed representations of concepts [C ] //Proceeding s of the Eighth Annual Conference of the Cognitive Science Society: Vol 1 . Amherst, MA , 1986 : 12 .

Rudin C . Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead [J ] . Nature Machine Intelligence , 2019 , 1 ( 5 ): 206 - 215 .

Chen Z Y , Xie Y Q , Xu C M , et al . Scale-up of complex molecular reaction system by hybrid mechanistic modeling and deep transfer learning [J ] . Nature Communications , 2025 , 16 : 8905 .

Nascimento R G , Corbetta M , Kulkarni C S , et al . Hybrid physics-informed neural networks for lithium-ion battery modeling and prognosis [J ] . Journal of Power Sources , 2021 , 513 : 230526 .

Alhajeri M S , Abdullah F , Wu Z , et al . Physics-informed machine learning modeling for predictive control using noisy data [J ] . Chemical Engineering Research and Design , 2022 , 186 : 34 - 49 .

Raissi M , Perdikaris P , Karniadakis G E . Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations [J ] . Journal of Computational Physics , 2019 , 378 : 686 - 707 .

Ji W Q , Deng S L . Autonomous Discovery of Unknown Reaction Pathways from Data by Chemical Reaction Neural Network [J ] . The Journal of Physical Chemistry A , 2021 , 125 ( 4 ): 1082 - 1092 .

Ulissi Z W , Tang M T , Xiao J P , et al . Machine-Learning Methods Enable Exhaustive Searches for Active Bimetallic Facets and Reveal Active Site Motifs for CO 2 Reduction [J ] . ACS Catalysis , 2017 , 7 ( 10 ): 6600 - 6608 .

Behler J , Parrinello M . Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces [J ] . Physical Review Letters , 2007 , 98 ( 14 ): 146401 .

刘科均 . 浅谈人工智能技术在化工生产自动化控制系统中的应用 [J ] . 石化技术 , 2022 , 29 ( 6 ): 261 - 263 .

Liu K J . Application of artificial intelligence technology in automatic control system of chemical production [J ] . Petrochemical Industry Technology , 2022 , 29 ( 6 ): 261 - 263 .

吉远辉 , 苏隽奕 , 李弋 , 等 . 人工智能背景下化工专业课程教学改革研究与实践 [J ] . 化工高等教育 , 2024 , 41 ( 3 ): 9 - 13, 105 .

Ji Y H , Su J Y , Li Y , et al . Research and Practice on Courses Teaching Reform in Chemical Engineering under the Background of Artificial Intelligence [J ] . Higher Education in Chemical Engineering , 2024 , 41 ( 3 ): 9 - 13, 105 .

陈光宇 , 杨欣昱 , 梁娜 , 等 . 以人为本的人工智能工程伦理准则探析 [J ] . 电子科技大学学报(社科版) , 2020 , 22 ( 6 ): 32 - 38 .

Chen G Y , Yang X Y , Liang N , et al . Analysis of People-oriented Engineering Ethics Principles for Artificial Intelligence [J ] . Journal of University of Electronic Science and Technology of China (Social Sciences Edition) , 2020 , 22 ( 6 ): 32 - 38 .

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