What are the Benefits of Machine Learning in Design Optimization?
Machine learning (ML) can accelerate the design optimization process by quickly analyzing large datasets and identifying optimal configurations. ML algorithms can be trained to predict the properties of new nanomaterials based on existing data, thereby directing experimental efforts more efficiently. This is particularly beneficial in high-throughput screening where thousands of potential nanomaterial configurations need to be evaluated.
