A group of Skoltech scientists developed machine learning (ML) algorithms that can teach artificial intelligence (AI) to determine oil viscosity based on nuclear magnetic resonance (NMR) data. The new method can come in handy for the petroleum industry and other sectors, which have to rely on indirect measurements to characterize a substance. The research was published in the Energy and Fuels journal. An important parameter of oil and petrochemicals, viscosity has implications for production and processing, while helping to better understand and model the natural processes in the reservoir. Standard oil viscosity assessment and monitoring techniques are very time and money consuming and sometimes technically unfeasible.

A group of Skoltech scientists have developed machine learning (ML) algorithms that can teach artificial intelligence (AI) to determine oil viscosity based on nuclear magnetic resonance (NMR) data. The new method can come in handy for the petroleum industry and other sectors that have to rely on indirect measurements to characterize a substance. The research was published in the Energy and Fuels journal. An important parameter of oil and petrochemicals, viscosity has implications for production and processing, while helping to better understand and model the natural processes in the reservoir. Standard oil viscosity assessment and monitoring techniques are very time and money consuming and sometimes technically unfeasible.

Every organic chemist has had to solve problems of structure elucidation, such as determining the structure of a biologically-active natural product or understanding the products of a reaction. These problems are often difficult and may be a bottleneck of chemical discovery. Structural misassignment leads to the waste of time and resources. In the last two decades computational tools have become increasingly useful in tackling these problems, with the DP4 Probability developed by the Goodman Lab being a key contribution to this toolkit (https://doi.org/10.1021/ja105035r). By comparing experimental NMR spectra and those computed for candidate structures, DP4 quantifies confidence in structural assignment, enabling chemists to use their resources more effectively.