Predictive design of bistable molecular materials through crystallographic descriptors and supervised machine learning

Topic description

Decoding molecular switches:

from crystal structure to predictive design

Some materials can switch reversibly between two electronic states under temperature, pressure, or light. These spin-crossover (SCO) and charge-transfer-induced spin-transition (CTIST) compounds hold real promise for applications in molecular memory, sensors, and actuators. But after decades of research, a central question remains open: can we predict, from crystal structure alone, whether a new material will switch and under which conditions?

This PhD project tackles that question directly. You will combine experimental crystallography with supervised machine learning to build a predictive framework linking structural features to macroscopic switching behaviour: transition temperature, hysteresis width, abruptness. This is not a project where you apply an existing method to a standard problem. The descriptors are still being identified. ...