Domain-agnostic and Multi-level Evaluation of Generative Models

Machine Learning (ML) methods, particularly generative models, are effective in addressing critical problems across different domains, which includes material sciences. Examples include the design of novel molecules by combining data-driven techniques and domain knowledge to efficiently search the space of all plausible molecules and generate new and valid ones [1, 2, 3, 4]. Traditional high-throughput wet-lab experiments, physics-based simulations, and bioinformatics tools for the molecular design process heavily depend on human expertise. These processes require significant resource expenditure to propose, synthesize and test new molecules, thereby limiting the exploration space [5, 6, 7]. For example, generative models have been applied to facilitate the material discovery process by employing inverse molecular design problem. This approach transforms the conventional and slow discovery process by mapping the desired set of properties to a set of structures. The generative process is then optimized to encourage the generation of molecules with those selected properties. Countless approaches have been suggested for such tasks, most prominently VAEs with different sampling techniques [8, 9, 10]), GANs [11, 12], diffusion models [13], flow networks [14] and Transformers [15].