A major challenge in developing a Deep Generative Model (DGM) for materials discovery is the high cost of evaluating the generated candidates. This typically involves conducting repetitive experiments¹ or simulations in a laboratory², which can be labour-intensive and require a lot of resources. In a project titled "Efficient Materials Design with Fewer Domain Evaluations", Dr Li Jing, a Scientist from A*STAR’s Centre for Frontier AI Research (CFAR) addressed this challenge by proposing two strategies.

Firstly, he selected a subset of generated candidates based on information criteria to exhibit the materials with desired properties. By focusing on this selected group, the evaluation process became more efficient and cost-effective.

Fig. 1 Generate materials with desired properties using fewer domain evaluations.

By combining these two strategies, the DGM could efficiently produce materials with a higher likelihood of meeting the desired criteria with less intervention from domain experts. Compared with traditional DGM training paradigms, the research outcomes are expected to reduce experts' involvement by 50% and the framework could potentially be applied to different materials designs.

References:
¹Christian H. Ahrens et al. A Practical Guide to Small Protein Discovery and Characterization Using Mass Spectrometry, Journal of Bacteriology, doi:10.1128/jb.00353-21, 2022.
²Neurosnap