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References from Benchmarking AI prompt performance of large language models across chemical process systems engineering domains using multi-judge evaluation. Local targets link to admitted publications; unresolved targets remain external evidence.
Research challenges in process systems engineering
10.1002/aic.690460902 · 2000 · External reference
Integrated multi-objective MINLP optimization for cost-effectiveness and safety in multi-floor process layout for manufacturing industries
10.1016/j.cie.2025.111442 · 2025 · External reference
Intelligent process simulation framework for bio-oil production from Wolffia globosa fast pyrolysis via Aquila optimizer
10.1016/j.nexus.2026.100713 · 2026 · External reference
Machine learning-guided catalyst selection for bio-oil hydrodeoxygenation to maximize hydrocarbon yields toward renewable naphtha and sustainable aviation fuel
10.1016/j.fuel.2026.139511 · 2026 · External reference
Integrated machine learning models for prediction of multiphase products from hydrothermal treatment of biomass
10.1016/j.renene.2026.125200 · 2026 · External reference
Machine learning in chemical engineering: a perspective
10.1002/cite.202100083 · 2021 · External reference
The promise of artificial intelligence in chemical engineering: is it here, finally?
10.1002/aic.16489 · 2019 · External reference
Quo vadis ChatGPT? From large language models to large knowledge models
10.1016/j.compchemeng.2024.108895 · 2025 · External reference
Building hybrid AI models in chemical engineering: a tutorial review
10.1016/j.compchemeng.2025.109236 · 2025 · External reference
From prompt design to iterative generation: leveraging LLMs in PSE applications
10.1016/j.compchemeng.2025.109282 · 2025 · External reference
Learning from flowsheets: a generative transformer model for autocompletion of flowsheets
10.1016/j.compchemeng.2023.108162 · 2023 · External reference
Artificial intelligence and machine learning at various stages and scales of process systems engineering
10.1002/cjce.25525 · 2025 · External reference
From lab to fab: a large language model for chemical engineering
10.1016/s1872-2067(25)64725-5 · 2025 · External reference
Unresolved reference
2016 · External reference
Analysis of the AI prompt used to improve safety management system accessibility
10.1016/j.jlp.2025.105834 · 2026 · External reference
A framework for evaluating the chemical knowledge and reasoning abilities of large language models against the expertise of chemists
10.1038/s41557-025-01815-x · 2025 · External reference
Do large language models understand chemistry? A conversation with ChatGPT
10.1021/acs.jcim.3c00285 · 2023 · External reference
Unresolved reference
External reference
L. Zheng, W.-L. Chiang, Y. Sheng, S. Zhuang, Z. Wu, Y. Zhuang, Z. Lin, Z. Li, D. Li, E.P. Xing, H. Zhang, J.E. Gonzalez, I. Stoica, Judging LLM-as-a-judge with mt-bench and chatbot arena
2023 · External reference
Humans or LLMs as the judge? A study on judgement bias
2024 · External reference
Unresolved reference
External reference
Unresolved reference
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Unresolved reference
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What can large language models do in chemistry? A comprehensive benchmark on eight tasks
10.52202/075280-2607 · 2023 · External reference
Legalbench: a collaboratively built benchmark for measuring legal reasoning in large language models
10.52202/075280-1915 · 2023 · External reference
What disease does this patient have? a large-scale open domain question answering dataset from medical exams
10.3390/app11146421 · 2021 · External reference
Unresolved reference
2001 · External reference
Unresolved reference
2019 · External reference
Unresolved reference
2001 · External reference
A plant-wide industrial process control problem
10.1016/0098-1354(93)80018-i · 1993 · External reference
Unresolved reference
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Rogue scores
2023 · External reference
Unresolved reference
1983 · External reference
