Digital Catalysis Research Group
DigiCat advances digital catalysis by openly sharing tailored tools for optimizing catalytic processes from atom to process. We bring together advanced physical models and cutting-edge ML and AI to derive knowledge from data.
Research
Papers2Data
Powering catalysis informatics with augmented data
Data2Catalyst
Designing ready-to-synthesize catalysts
Catalyst2Process
Optimizing catalyzed processes from atom to plant, from economics to socio-ecological impact.
People
Daniel Costa
PhD student - Papers2Data and Data2Catalysts
Heterogeneous Catalysis - Machine Learning - Data Extraction - LLMs (Large Language Models)
Arij Ben Hassine
PhD student - Catalyst2Process
Sustainable Aviation Fuels - Catalytic Hydroconversion - Kinetic Modeling - Mixture Effects
Thomas Hietala
PhD student - Catalyst2Process and Data2Catalysts
Multi-Scale and Multi-Objective Optimization - Sustainable Catalysts - Machine Learning
Estevão Tibúrcio
PhD student - Catalyst2Process
Heterogeneous Catalysis - Natural Gas Dehydration - Adsorption Separation Process - Modeling Industrial Operational Problems - Adsorbent Aging
Rita Assis Dos Santos
Intern - Papers2Data
LIMS (Laboratory Information Management System) - ELN (Electronic Lab Notebook)
Diogo Vilela
MSc student - Catalyst2Process
Renewable Energy Sources - Smart Grid - Optimization - Sensitivity Analysis
Publications
Open data in catalysis: from today's big picture to the future of small data
Guidelines on how to maximize the usefulness of data sharing and on how to make use of catalysis informatics tools to extract key information.
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Descriptor-property relationships in heterogeneous catalysis: Exploiting synergies between statistics and fundamental kinetic modelling
By combining statistical machine learning methods with microkinetics modelling, the impact of catalyst properties on performance. Thereby, qualitative guidelines for designing more performant OCM catalysts were inferred.
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From powder to extrudate zeolite-based bifunctional hydroisomerization catalysts: on preserving zeolite integrity and optimizing Pt location
The impact of scale-up in bifunctional catalysts was unveiled. Zeolite properties can now be kept and Pt location optimized to maximize catalyst performance.
Open data in catalysis: from today's big picture to the future of small data
Guidelines on how to maximize the usefulness of data sharing and on how to make use of catalysis informatics tools to extract key information.
Learn More Descriptor-property relationships in heterogeneous catalysis: Exploiting synergies between statistics and fundamental kinetic modelling
By combining statistical machine learning methods with microkinetics modelling, the impact of catalyst properties on performance. Thereby, qualitative guidelines for designing more performant OCM catalysts were inferred.
Learn More From powder to extrudate zeolite-based bifunctional hydroisomerization catalysts: on preserving zeolite integrity and optimizing Pt location
The impact of scale-up in bifunctional catalysts was unveiled. Zeolite properties can now be kept and Pt location optimized to maximize catalyst performance.
Bifunctional Intimacy and its Interplay with Metal-Acid Balance in Shaped Hydroisomerization Catalysts
For shaped, industrial-like catalysts, the simultaneous requirement for nanometric metal-acid sites intimacy and adequate metal-acid balance was established. Only by optimizing these two key properties, maximal catalytic performance can be achieved
Learn MoreOpen resources
Contact us
- Avenida Rovisco Pais 1, Lisboa, Portugal
