Molecular Science
Molecular representations, property prediction, simulation, quantum chemistry, and molecular design.
- Why drug discovery is a sequence of linked inference problems—from target validation and molecular binding to exposure, safety, and clinical benefit.
- Where machine learning enters electronic-structure theory, from neural wavefunctions and learned functionals to Hamiltonians and energy surfaces.
- How molecular representations, conformers, data splits, pretraining, and uncertainty determine what a property-prediction benchmark actually measures.
- How generative models respect molecular geometry, how guidance turns sampling into design, and why oracle scores must survive experiment.
- Molecular graph generation and reaction modeling viewed as constrained structured prediction, from representation and symmetry to synthesis-aware evaluation.
- How learned energy surfaces become molecular dynamics, why rollout stability differs from static accuracy, and how to validate observables.
- A practical bridge from molecular dynamics to enhanced sampling, metadynamics, collective variables, and recent ML approaches for rare molecular events.
- Statistical mechanics: from Newton's equations to ensembles, thermostats, barostats, Monte Carlo, and connections to generative modeling.
- Quantum chemistry and density functional theory: from the Schrödinger equation to Kohn-Sham DFT and modern deep learning approaches.