Scientists have developed a machine-learning algorithm that predicts electron energy levels in organic molecules. This breakthrough, trained on a database of over 22,000 molecules, could accelerate the design of functional molecules like pharmaceuticals. Organic chemistry, the study of carbon-bas
Researchers at the Institute of Industrial Science, The University of Tokyo, use artificial intelligence to help interpret data generated by material science spectroscopy experiments, which can aid in the development of new drugs and organic conductors. Credit: Institute of Industrial Science, The University of Tokyo
Organic chemistry, the study of carbon-based molecules, underlies not only the science of living organisms, but is critical for many current and future technologies, such as organic light-emitting diode displays. Understanding the electronic structure of a material’s molecules is key to predicting the material’s chemical properties.
The experimental techniques often used to find the density of states can be difficult to interpret. This is particularly true for the method known as core-loss spectroscopy, which combines energy loss near-edge spectroscopy and X-ray absorption near-edge structure .
“We attempted to extrapolate predictions for larger molecules using a model trained by smaller molecules. We discovered that theThe team also found that by using smoothing preprocessing and adding specific noise to the data, the predictions of density of state can be improved, which can accelerate adoption of the prediction model for use on real data.
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