Chem + ML

Machine learning has gained significant importance in chemistry, offering opportunities to tackle complex problems and make predictive models.

Problems of interest

• Data augmentation & transfer learning for low-cost high-accuracy electronic structure calculations
• Spectroscopic predictions
• Conformation sampled electron transfer in biological/ soft matter systems
• Machine learning models with chemical interpretability
• Machine leaned force-fields
• Machine learned continuum models
• Multi-objective optimization and inverse design
• Clustering algorithms for accurate partial charges
• Genetic algorithms for cluster formation