Emily Saldanha

Abstract The performance of machine learning techniques for the prediction of a wide range of molecular properties has seen rapid improvements in recent years due to developments in both molecular representations and deep learning modeling techniques. Sigma profiles, which are computational descriptors representing the surface charge distribution of molecules, have shown promise as a molecular representation to support robust property prediction.

Abstract The automation of chemical research through self-driving laboratories (SDLs) promises to accelerate scientific discovery, yet the reliability and granular performance of the underlying AI agents remain critical, under-examined challenges. In this work, we introduce AutoLabs, a self-correcting, multi-agent architecture designed to autonomously translate natural-language instructions into executable protocols for a high-throughput liquid handler.