材料设计：化学与工程的下一个范例

IF 4.3 Q2 ENGINEERING, CHEMICAL ACS Engineering Au Pub Date : 2024-04-17 DOI:10.1021/acsengineeringau.4c00014

Steven G. Arturo*, Linda J. Broadbelt*, Paul J. Dauenhauer* and Ananth Govind Rajan*,

引用次数: 0

摘要

ChatGPT 和其他大型语言模型的出现为材料设计带来了巨大希望，使工程师和科学家能够智能地查询机器以生成新知识。在本期 VSI 中，Buehler 展示了使用知识检索增强的大型语言模型可以提高材料设计应用的准确性和适用性。此外，Gao 及其同事还展示了利用主动学习设计聚合条件的方法，这种方法有助于实现理想的聚合物分子量分布。这样就能利用尽可能少的动力学蒙特卡洛模拟来进行材料设计，而无需进行大量计算成本高昂的模拟。强化学习是人工智能的另一个备受关注的领域。在这方面，Groves 和 Villavicencio 提出了一种调整强化学习参数的新方法，以寻求增强进化优化算法。材料设计 VSI 的论文突出了化学和化学工程材料设计领域的创新进展。通过谨慎地应用机器学习和人工智能策略、明智地使用建模和计算以及创造性地设计实验和自动化平台，我们可以开发出结构-性能关系，并为特定的实际应用开发出量身定制的材料。希望您和我们一样喜欢阅读这些论文。我们感谢作者们的贡献，也感谢审稿人在工作中投入的时间。我们期待着在不久的将来审议这一快速发展领域的未来投稿。本文尚未被其他出版物引用。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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ACS Engineering Au 化学工程技术-

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期刊介绍：）ACS Engineering Au is an open access journal that reports significant advances in chemical engineering applied chemistry and energy covering fundamentals processes and products. The journal's broad scope includes experimental theoretical mathematical computational chemical and physical research from academic and industrial settings. Short letters comprehensive articles reviews and perspectives are welcome on topics that include:Fundamental research in such areas as thermodynamics transport phenomena (flow mixing mass & heat transfer) chemical reaction kinetics and engineering catalysis separations interfacial phenomena and materialsProcess design development and intensification (e.g. process technologies for chemicals and materials synthesis and design methods process intensification multiphase reactors scale-up systems analysis process control data correlation schemes modeling machine learning Artificial Intelligence)Product research and development involving chemical and engineering aspects (e.g. catalysts plastics elastomers fibers adhesives coatings paper membranes lubricants ceramics aerosols fluidic devices intensified process equipment)Energy and fuels (e.g. pre-treatment processing and utilization of renewable energy resources; processing and utilization of fuels; properties and structure or molecular composition of both raw fuels and refined products; fuel cells hydrogen batteries; photochemical fuel and energy production; decarbonization; electrification; microwave; cavitation)Measurement techniques computational models and data on thermo-physical thermodynamic and transport properties of materials and phase equilibrium behaviorNew methods models and tools (e.g. real-time data analytics multi-scale models physics informed machine learning models machine learning enhanced physics-based models soft sensors high-performance computing)

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