Understanding how matter interacts with light – its optical properties – is critical in a myriad of energy and biomedical technologies, such as targeted drug delivery, quantum dots, fuel combustion, and cracking of biomass. But calculating these properties is computationally intensive, and the inverse problem – designing a structure with desired optical properties – is even harder.
Now Berkeley Lab scientists have developed a machine learning model that can be used for both problems – calculating optical properties of a known structure and, inversely, designing a structure with desired optical properties. Their study was published in Cell Reports Physical Science.
Read the full article: newscenter.lbl.gov/2020/12/02/a-machine-learning-solution-for-designing-materials-with-desired-optical-properties/