DIGITAL CHEMISTRY

INTRODUCTION

The Digital Chemistry (DC) Division team was established as a horizontal enabler to accelerate innovation across ISCE2. The DC Division focuses on accelerating chemical discovery and reaction optimization processes across various divisions and institutional technology focus areas. This is achieved by leveraging high-throughput experimentation tools, reaction miniaturization, automated synthesis, machine learning (ML), and artificial intelligence (AI). 

RESEARCH FOCUS

HIGH-THROUGHPUT EXPERIMENTATION

By integrating high-throughput experimentation (HTE) platforms and automation the DC Division aims to improve scientific output and productivity by up to 100-fold without requiring more time or material. This will allow us to generate high-fidelity, coherent, structured datasets required for digitally enhanced workflows, such as the Design of experiments, ML-based reaction optimization, and predictions. This will directly benefit ISCE² technological focus research areas such as, the discovery of new catalysts for carbon capture/utilization, biomass conversion, synthesis of functional polymers, multi-component formulation screening, and bioactive molecules. The division will also focus on protocols to enable in-situ reaction analysis and rapid analytical data processing, delivering effective solutions to instant data collection.

HIGH-THROUGHPUT SYNTHESIS

The DC division labs provide capabilities for high-throughput synthesis (HTS) of chemicals, running up to 1000 reactions per week. Our automated instrumentation allows for rapid setup and deployment of multiple parallel reactions at milligram scale, suitable for catalyst screening, reaction optimisation and compound library preparations. The workflow allows for highly diverse, customisable HTS protocols suitable for a wide range of starting materials, reaction parameters and analysis. Key areas of interest for DC’s HTS work include common, yet challenging chemical transformations such as cross couplings, selected functional group interconversions and new tailored protection/deprotection methodologies.

DIGITALIZATION AND DATA MANAGEMENT

The Digital Chemistry Division is committed to adopting and practicing FAIR (Findable, Accessible, Interoperable, and Reusable) principles in its data management workflow. This approach ensures that the diverse data sets from experiments, molecular modelling, and ML predictions are structured and useful for data consumers. The data management workflow will be supported by leveraging salient features of the Electronic Lab Notebook.  The standardized data would help in rapid data analysis and ML predictions that complement the quick decision-making in chemical synthesis and processes.