Machine learning for molecular and materials science

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Diffusion Models: A Comprehensive Survey of Methods and Applications (2022, background): “Solid state materials are the critical foundation of numerous key technologies [19].”
The shape of porphyrins (2021, background): “parameter, through measurement, to photophysical properties, combined with the wealth of data available for these analyses, indicates that this form of computational analysis is ideally suited to machine learning [175,176].”
A Structure-Based Drug Discovery Paradigm (2019, background): “In a GAN (Figure 3), two ANN models—the generator and discriminator—are trained simultaneously and generate a new molecule from scratch by optimizing a different and opposing objective function in a zero-sum game [123].”
Accurate Thermochemistry with Small Data Sets: A Bond Additivity Correction and Transfer Learning Approach. (2019, background): “1,2 In particular, automated reaction mechanism generation requires estimates of chemical kinetic rates and molecular thermochemistry, which is typically used to calculate reverse reaction rates from the relationship between the Gibbs free energy change of a reaction and the equilibrium constant.”