Photoswitches are molecular systems that undergo chemical change due to light interaction and have potential use in numerous new technologies. Intricate molecular engineering of various features is required to create and identify photoswitch candidates to optimize a candidate for a particular application. This process can be completed via quantum chemical screening procedures.
Researchers used a quantum computing method in a new study to find a particularly efficient molecular structure. Their technique is based on a dataset of more than 400,000 molecules, which they screened to find the optimum molecular structure for solar energy storage materials.
In particular, researchers carefully studied molecules known as bicyclic dienes, which switch to a high-energy state when illuminated. Norbornadiene quadricyclane is the most prominent example of this bicyclic diene system. There also exist several similar candidates.
Researchers noted, “The resulting chemical space consists of approximately 466,000 bicyclic dienes that we have screened for their potential applicability in MOST technology.”
Machine learning can help screen a database of this size. However, it demands large amounts of training data based on real-world experiments, which the team needed. Hence, the team used a previously developed algorithm and a novel evaluation score, “eta.”
The screening and assessment of the molecules in the database led to a resounding conclusion: all six top-scoring molecules deviated from the original norbornadiene quadricyclane system at a critical structural site. The scientists concluded that the new molecules could store more energy than the original norbornadiene due to a structural modification that increased the molecular bridge between the two carbon rings in the bicyclic region.
The study could optimize solar energy storage molecules. But, there is a condition: molecules should be synthesized and tested under real conditions.
Researchers noted, “Even though the systems can be synthetically prepared, there is no guarantee that they are soluble in relevant solvents and will photoswitch in high yield or at all, as we have assumed in eta.”
Despite this, the team has created a brand-new, sizable set of training data for machine learning algorithms, reducing the time required for laborious study before synthesis for chemists working on similar systems in the future. Research into photoswitches for various applications will benefit from this considerably bigger collection of bicyclic dienes, possibly making it simpler to customize molecules to particular needs.