Hybrid Generative-Discriminative Models for Inverse Materials Design

Scientific innovations relating physical processes require laborious experimentation and expensive simulation as the relationships between design variables and output characteristics are unknown [4, 28]. To design a new product with certain target characteristics, a search is typically performed in the design space - a large number of the design combinations (input variables) are tried in simulators before reaching to the target characteristics (see Figure 1a). Although modern search methods such as Bayesian Optimization [30] are efficient, there is an inherent problem with the search: Each time the target characteristics are changed or refined, the search process needs to be restarted making the design task extremely time-consuming. The current search paradigm does not harness existing experimentation data and simulator queries systematically as there is no clear provision to reuse them. Thanks to the availability of growing data, accurate simulators and modern machine learning algorithms, this search process can be avoided with a potential to accelerate scientific innovations by multiple orders of magnitude. An effective way to address this problem is to leverage the existing data and query the simulators in an offline mode to sample the data space sufficiently and then harness this data to build a machine learning model. Given sufficient data, modern machine learning (ML) models (e.g. a deep neural network) can approximate the underlying physical relationships and the simulators arbitrarily closely. The ML models can then be used to convert the search process in a less expensive optimization.