Grant: Discovery Grants Program - Individual

Most of the worlds major technological and societal challenges are being addressed through research in chemistry, including health, climate change, energy, security, and food supply. With rapid advances in computational power over the last two decades, advanced research computing (ARC) has now become an indispensable research tool in the chemical sciences and many other fields. Research scientists routinely rely on computational simulations to complement, and often exceed, what can be achieved with experiments alone, securing ARC as a transformative modern technology with broad socioeconomic impact in Canada. Unfortunately however, modern computational techniques for modelling chemical systems scale very poorly with molecular size, leading to a practical limit on the scope of molecular species accessible by computational means. Consequently, if computational techniques are to be reliably applied to the ever-increasing scope of important problems in chemistry, radically new computational models are required. Additionally, as the prevalence of computational simulation in chemistry expands, there is a concurrent need for techniques to distill relevant insight and meaning from resultant data, particularly for predictions of detailed electronic structures in quantum chemistry. Recent work in our group has made significant strides toward providing innovative solutions to these important problems in chemistry. We have worked to establish a comprehensive, open, and interactive data management platform for the chemical sciences with capabilities for pattern recognition and complex queries. These new technologies for data-driven inquiry will allow researchers to distill new knowledge from a vast chemical data landscape, which promises to yield new industrial catalysts, advanced materials, medicines, and transformative new computational simulation techniques among countless other substantial applications. In particular, we will utilize the technology to design radically new computational models based on machine learning algorithms applied to large datasets. Additionally, we will develop electronic structure analysis tools that predict and interpret the distribution of electrons within chemical bonds and lone pairs, which affords unique qualitative and quantitative insight into how molecular species interact and react.