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Machine learning and molecular descriptors enable rational solvent selection in asymmetric catalysis

Chemical science (Cambridge), 2019-07, Vol.1 (27), p.6697-676

2019

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Machine learning meets continuous flow chemistry: Automated optimization towards the Pareto front of multiple objectives

Chemical engineering journal (Lausanne, Switzerland : 1996), 2018-11, Vol.352, p.277-282

2018

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Empirical assessment of ChatGPT's answering capabilities in natural science and engineering

Scientific reports, 2024-02, Vol.14 (1), p.4998-4998, Article 4998

2024

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Efficient multiobjective optimization employing Gaussian processes, spectral sampling and a genetic algorithm

Journal of global optimization, 2018-06, Vol.71 (2), p.407-438

2018

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Graph machine learning for design of high‐octane fuels

AIChE journal, 2023-04, Vol.69 (4), p.n/a

2023

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Deterministic Global Optimization with Artificial Neural Networks Embedded

Journal of optimization theory and applications, 2019-03, Vol.180 (3), p.925-948

2019

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Graph Neural Networks for Prediction of Fuel Ignition Quality

Energy & fuels, 2020-09, Vol.34 (9), p.11395-11407

2020

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Flowsheet generation through hierarchical reinforcement learning and graph neural networks

AIChE journal, 2023-01, Vol.69 (1), p.n/a

2023

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Toward automatic generation of control structures for process flow diagrams with large language models

AIChE journal, 2024-01, Vol.70 (1), p.n/a

2024

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Model-based bidding strategies on the primary balancing market for energy-intense processes

Computers & chemical engineering, 2019-01, Vol.120, p.4-14

2019

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Deep reinforcement learning for process design: Review and perspective

Current opinion in chemical engineering, 2024-06, Vol.44, p.101012, Article 101012

2024

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Insight to Gene Expression From Promoter Libraries With the Machine Learning Workflow Exp2Ipynb

Frontiers in bioinformatics, 2021-10, Vol.1, p.747428

2021

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Chemical data intelligence for sustainable chemistry

Chemical Society reviews, 2021-11, Vol.5 (21), p.1213-1236

2021

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Dynamic modeling and optimization of sustainable algal production with uncertainty using multivariate Gaussian processes

Computers & chemical engineering, 2018-10, Vol.118, p.143-158

2018

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Rational design of ion separation membranes

Journal of membrane science, 2019-01, Vol.569, p.209-219

2019

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Globally optimal working fluid mixture composition for geothermal power cycles

Energy (Oxford), 2020-12, Vol.212, p.118731, Article 118731

2020

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Machine Learning in Chemical Engineering: A Perspective

Chemie ingenieur technik, 2021-12, Vol.93 (12), p.2029-2039

2021

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Techno-economic Optimization of a Green-Field Post-Combustion CO2 Capture Process Using Superstructure and Rate-Based Models

Industrial & engineering chemistry research, 2016-11, Vol.55 (46), p.12014-12026

2016

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Nonlinear scheduling with time‐variable electricity prices using sensitivity‐based truncations of wavelet transforms

AIChE journal, 2020-10, Vol.66 (10), p.n/a

2020

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Obey validity limits of data-driven models through topological data analysis and one-class classification

Optimization and engineering, 2022-06, Vol.23 (2), p.855-876

2022

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Working fluid selection for organic rankine cycles via deterministic global optimization of design and operation

Optimization and engineering, 2020-06, Vol.21 (2), p.517-536

2020

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A review and perspective on hybrid modeling methodologies

Digital Chemical Engineering, 2024-03, Vol.10, p.100136, Article 100136

2024

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Deterministic global optimization with Gaussian processes embedded

Mathematical programming computation, 2021-09, Vol.13 (3), p.553-581

2021

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A Multiobjective Optimization Including Results of Life Cycle Assessment in Developing Biorenewables‐Based Processes

ChemSusChem, 2017-09, Vol.10 (18), p.3632-3643

2017

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Correction to: Efficient multiobjective optimization employing Gaussian processes, spectral sampling and a genetic algorithm

Journal of global optimization, 2018-06, Vol.71 (2), p.439-440

2018

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Machine learning and molecular descriptors enable rational solvent selection in asymmetric catalysis

Machine learning meets continuous flow chemistry: Automated optimization towards the Pareto front of multiple objectives

Empirical assessment of ChatGPT's answering capabilities in natural science and engineering

Efficient multiobjective optimization employing Gaussian processes, spectral sampling and a genetic algorithm

Graph machine learning for design of high‐octane fuels

Deterministic Global Optimization with Artificial Neural Networks Embedded

Graph Neural Networks for Prediction of Fuel Ignition Quality

Flowsheet generation through hierarchical reinforcement learning and graph neural networks

Toward automatic generation of control structures for process flow diagrams with large language models

Model-based bidding strategies on the primary balancing market for energy-intense processes

Deep reinforcement learning for process design: Review and perspective

Insight to Gene Expression From Promoter Libraries With the Machine Learning Workflow Exp2Ipynb

Chemical data intelligence for sustainable chemistry

Dynamic modeling and optimization of sustainable algal production with uncertainty using multivariate Gaussian processes

Rational design of ion separation membranes

Globally optimal working fluid mixture composition for geothermal power cycles

Machine Learning in Chemical Engineering: A Perspective

Techno-economic Optimization of a Green-Field Post-Combustion CO2 Capture Process Using Superstructure and Rate-Based Models

Nonlinear scheduling with time‐variable electricity prices using sensitivity‐based truncations of wavelet transforms

Obey validity limits of data-driven models through topological data analysis and one-class classification

Working fluid selection for organic rankine cycles via deterministic global optimization of design and operation

A review and perspective on hybrid modeling methodologies

Deterministic global optimization with Gaussian processes embedded

A Multiobjective Optimization Including Results of Life Cycle Assessment in Developing Biorenewables‐Based Processes

Correction to: Efficient multiobjective optimization employing Gaussian processes, spectral sampling and a genetic algorithm

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