Matthew S. Sigman

Mar 30, 2024 | nature.com | Hongyuan Sheng |Jingwen Sun |Weitong Zhang |Avijit Hazra |Abigail Doyle |Matthew S. Sigman | +3 more

AbstractElectrochemical research often requires stringent combinations of experimental parameters that are demanding to manually locate. Recent advances in automated instrumentation and machine-learning algorithms unlock the possibility for accelerated studies of electrochemical fundamentals via high-throughput, online decision-making. Here we report an autonomous electrochemical platform that implements an adaptive, closed-loop workflow for mechanistic investigation of molecular electrochemistry.

Feb 23, 2023 | onlinelibrary.wiley.com | Jonas Rein |Jonathan M. Meinhardt |Matthew S. Sigman |Song Lin

Graphical Abstract Tetrazoles and azides find broad application in pharmaceuticals, chemical biology, and high-energy materials. In this work multivariant linear regression and classification are leveraged to develop predictive statistical models for their decomposition temperature and impact sensitivity, which are key in evaluating the safety of a given molecule. Furthermore, interpretation of the statistical models provided rich insight into the decomposition mechanisms.