The team, led by Philip Gusev and Alexander Isaev, used machine learning to analyze nearly half a million molecules. Their goal is to identify substances that can form the crystal structures necessary for efficient charge conduction. AI reduced the number of potential candidates to 44; of these, six molecules were experimentally confirmed to have the desired properties.
Crystallized organic semiconductors (COS) are capable of forming large sheet-shaped crystals that are ideal for use in electronics. Scientists have developed two machine learning models to predict the thermal properties and crystallization abilities of molecules.
Experimental tests confirmed the accuracy of the predictions. The team used differential scanning calorimetry (DSC) and other techniques to evaluate the thermal properties of the molecules and their suitability for crystal formation.
Source: Ferra
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