Area of research
Materials Chemistry · Inorganic Chemistry
Research interest
Research interests include Machine Learning in Materials Science, Zeolite Catalysis and Synthesis, Computational Drug Discovery Methods, and Metal-Organic Frameworks: Synthesis and Applications.
Model-free estimation of completeness, uncertainties, and outliers in atomistic machine learning using information theory
A comprehensive mapping of zeolite–template chemical space
Comprehensive sampling of coverage effects in catalysis by leveraging generalization in neural network models
Approaching enzymatic catalysis with zeolites or how to select one reaction mechanism competing with others
Human- and machine-centred designs of molecules and materials for sustainability and decarbonization
Learning Matter: Materials Design with Machine Learning and Atomistic Simulations
A priori control of zeolite phase competition and intergrowth with high-throughput simulations
Discovering Relationships between OSDAs and Zeolites through Data Mining and Generative Neural Networks
Active learning accelerates ab initio molecular dynamics on reactive energy surfaces
Generative Models for Automatic Chemical Design
Temperature-transferable coarse-graining of ionic liquids with dual graph convolutional neural networks