The Adaptive Tunability for Synthesis and Control via Autonomous Learning on Edge (AT SCALE) Initiative will transform materials synthesis through closed-loop autonomous experimentation. To achieve this goal, the AT SCALE team is reimagining the current synthesis-characterization-properties paradigm used in materials science. They will codevelop artificial intelligence, machine learning, heterogeneous computing systems, and control methodologies in concert with synthesis and characterization platforms to enable precision synthesis and processing “on the fly.”
AT SCALE will focus on developing the necessary control in both space and time to discover and innovate at the building-block level of atoms or qubits. AT SCALE seeks to go beyond automation to lead to autonomy. The goal is to develop and demonstrate a precision synthesis platform that will provide the adaptability, decision, and functionality required for autonomous closed-loop operations.
AT SCALE will integrate theory and simulation with experiments in a closed-loop enabled by machine learning, control, and evolutionary algorithms.