The article discusses how the rapid development of artificial intelligence (AI) and machine learning (ML) is transforming chemical engineering by influencing problem formulation, analysis, and solution across a wide range of applications, from atomic-scale simulations to industrial operations. It highlights recent methodological advances and representative uses, noting a shift from purely black-box models to hybrid and physics-informed frameworks that incorporate conservation laws, thermodynamic consistency, and structural constraints. These integrated approaches enhance robustness, reliability, and human-AI collaboration, ultimately amplifying rather than replacing core chemical engineering principles.
By Michael Baldea, Linda J. Broadbelt, Marianthi G. Ierapetritou, Akhilesh Jain, Ankur Kumar, Thomas A. Kwan, F\`elix Llovell, Andrew J. Medford, Ilias Mitrai, Joel Paulson, Junyi Qiao, Matthew P. Rivera, Kirti C. Sahu, Lev Sarkisov, Zachary P. Smith, Calvin Tsay, Ching-Mei Wen, Victor M. Zavala, Huacheng Zhang, Dan Zhao
Frontier language models now score above 90% on popular knowledge benchmarks and are increasingly deployed as agents that carry out multi-hour professional work. Yet no existing benchmark tests whethe...
arXiv:2601. 21570v2 Announce Type: replace Abstract: The field of Embodied AI is witnessing a rapid evolution toward general-purpose robotic systems, fueled by high-fidelity simulation and large-scale data collection.
By Zixing Lei, Genjia Liu, Yuanshuo Zhang, Qipeng Liu, Yuzhu Cai, Sixiang Chen, Jixian Wu, Yunhong Wang, Weixin Li, Chuan Wen, Bo Zhao, Shanghang Zhang, Wenzhao Lian, Siheng Chen
arXiv:2608. 14407v1 Announce Type: new Abstract: We present a survey of the past and future of AI Scientists: machines capable of automating science.
By Ross D. King
arXiv:2607. 22877v1 Announce Type: new Abstract: With the emergence of Physical AI, artificial intelligence is extending beyond screen-based applications to embodied systems that perceive, interact with, and act in the physical world.
By Wang Yang, Shaobo Wang, Hongxuan Liu, Xiaoran Cai, Yunyu He, Jingzong Zhou, Mengzhong Ma, Yi Yu, Rohit Sharma, Jingjing Fu, Peng Qi
arXiv:2609.23360v1 Announce Type: cross
Abstract: Engineering predictions require physical mechanisms to be translated consistently into equations, discretization, code, and validation, yet errors ca...
By Jie Shi, Yimin Lu, Zhongkun Ouyang
arXiv:2606. 00133v1 Announce Type: new Abstract: World models, internal simulators that learn the structure and dynamics of an environment, have emerged as a central paradigm in the pursuit of artificial general intelligence, enabling agents to predict, plan, and reason within learned representations.
By Arif Hassan Zidan, Yi Pan, Hanqi Jiang, Ruiyu Yan, Wei Ruan, Zihao Wu, Lifeng Chen, Weihang You, Xinliang Li, Bowen Chen, Huawen Hu, Peilong Wang, Sizhuang Liu, Jing Zhang, Siyuan Li, Zhengliang Liu, Yu Bao, Lin Zhao, Lichao Sun, Dajiang Zhu, Xiang Li, Jinglei Lv, Quanzheng Li, Wei Liu, Tianming Liu, Wei Zhang
The paper introduces the concept of Physical AI—systems that understand and act within the physical world, where interactions are continuous, uncertain, and irreversible. It surveys trustworthy principles specific to Physical AI, outlines the role of physics in AI, and maps the end‑to‑end life cycle across five core stages, culminating in the Trustworthy Physical AI Operationalization (T‑PAIO) and the broader Trustworthy Physical AI (T‑PAI) framework.
By Wang Yang, Hongxuan Liu, Xinghui Xu, Arjun Menon, Xiaoran Cai, Yunyu He, Jingzong Zhou, Mengzhong Ma, Xinpeng Wei, Nathaniel Dennler, Yi Yu, Shaobo Wang, Cheng Peng, Aoran Jiao, Alexei Korolev, Ashis G. Banerjee, Yanyan Zhang, Kai Ye, Xinpeng Li, Chengquan Guo, Jingjing Fu, Marius Urbonas, Traian Tus, Gaoyue Zhou, George Ortiz, Irmak Guzey, Silei Ren, Lars Johannsm eier, Rohit Sharma, Felix Feng, Yoshua Bengio, Peng Qi
The monograph defines physical AI as artificial systems that perceive, model, and act upon the physical world through continuous feedback under real‑world constraints, distinguishing it from digital AI, embodied AI, cyber‑physical systems, and digital twins. It surveys the technical landscape—covering physics, control, cognition, learning, and reasoning—and reviews key methodologies such as multimodal perception, simulation, world models, embodied foundation models, motor‑control systems, multi‑agent coordination, and hardware‑software co‑design. The work also explores applications in robotics, manufacturing, infrastructure, scientific experimentation, and autonomous services, while addressing challenges like sim‑to‑real transfer, reliability, real‑time execution, evaluation, safety, governance, and long‑term deployment.
By Weinan Zhang, Chunhua Shen, Tianfan Xue, Bin Zhao, Bingqi Jiang, Zihan Guo, Zicai Cui, Yang Li, Ming Zhou, Xinyuan Chen, Lihe Ding, Xiao Chen, Yujin Wang, Shi Guo, Junting Dong, Tao Lu, Xing Shen, Haoyu Guo, Yao Mu, Haiyong Yu, Haoxiang Ma, Fuxian Huang, Qiaojun Yu, Yunsong Zhou, Kailin Li, Zirui Wang, Yufei Xue, Weishuai Zeng, Tao Huang, Zhigang Wang, Junli Liu, Pengyuan Wu, Pingrui Zhang, Haotian Wang, Dongzhan Zhou, Jiafei Cao, Feng Yuan, Xinyi Ye, Quanli Xuan, Mulin Yu, Shangding Gu, Kaiwen Song, Dong Wang, Jiarui Li, Kehui Liu, Zhongjie Jia, Dongjiang Li, Jiacheng Bao, Haoran Yang, Zhaoqing Chen, Yiming Sun, Bowen Zhou
arXiv:2606. 23991v1 Announce Type: new Abstract: What is an agent?
By Eric Xing, Mingkai Deng, Jinyu Hou
arXiv:2606. 16626v1 Announce Type: cross Abstract: Based on a questionnaire of 100 higher-education students, predominantly from engineering-related fields, and a critical review of recent literature, this chapter examines how students use and perceive Large Language Models (LLMs) in engineering education.
By Olya Kudina
The paper introduces little m, an AI agent that helps formulate industrial process control models by combining a domain-specific knowledge repository with LLM-driven interaction. It tackles the challenge of converting messy real-world specifications, including natural language and spatial diagrams, into rigorous mathematical optimization models. The authors also present IPC-Bench, a multimodal dataset of 50 canonical scenarios, and show through automated and human evaluations that little m outperforms state‑of‑the‑art LLMs in generating semantically correct models.
By Yongchao Ye, Xinyu He, Dutliff Boshoff, Way Kuo, Lishuai Li