De Novo Molecular Design
Molecular Property Prediction with Machine Learning
Machine learning (ML) models, such as kernel methods, Gaussian Process (GP), Support-vector machine (SVM), random forests (RF), and artificial neural network (ANN), have emerged as a new form of prediction of molecular properties compared to the traditional physics-based Ab Initio methods. The relative black-box nature of ML methods allows faster property predictions (around seconds to minutes vs. hours to days) of the properties of interest by providing a direct mapping between the encoding of molecules (e.g. molecular fingerprints) and the learnt properties (excitation energies, redox potentials, etc). Our group’s interest focuses on applying ML property prediction to find better organic light emitting diodes (OLED) for display applications and catalysts for selective oxidation of CH₄ to CH₃OH, H₂O to O₂, and NH₃ to N₂.