Machine learning workflows for chemistry: applications in catalysis and ionic liquids

Thesis

In today’s world, data is being generated and accumulated at an astronomical rate, presenting new opportunities and challenges for the scientific community. In parallel, advancements in computer science have revolutionized the landscape of chemistry. The confluence of these two fields has given rise to a wave of sophisticated machine learning algorithms capable of building powerful predictive models. The field of computational chemistry is now finding itself navigating through this rapid evolution of technological progress.

This Thesis traces the progression from statistical models to modern machine learning techniques and is setting the stage for the intricate dance between data science and chemistry. The focus narrows down to the specific utilization of machine learning for selectivity predictions in organocatalysis and property predictions for ionic liquids. It introduces Pythia, a machine learning toolkit designed with accessibility in mind, aiming to democratize the application of machine learning in computational chemistry. Pythia employs 2D and 3D descriptors and shallow learners to predict selectivity for organocatalytic reactions. The power of Pythia is put to the test and its potential for predicting selectivity in catalysis is explored. This demonstrates the toolkit's practical utility in facilitating more efficient and targeted experimentation in the search for effective catalysts. Finally, we delve into the prediction of viscosity and solubility in ionic liquids, further highlighting the capabilities of machine learning in streamlining the prediction of chemical properties. This Thesis promises to accelerate the pace of discovery in computational chemistry, allowing scientists to handle the influx of data more efficiently and extract meaningful insights from it.

Authors: Zavitsanou, S

• DOI: 10.5287/ora-zrn00g8zm
• Type of award: DPhil
• Level of award: Doctoral
• Awarding institution: University of Oxford
• Language: English
• Keywords: machine learning; organocatalysis; ionic liquids; Python