The lack of efficient discovery tools for advanced functional materials is a major bottleneck to enabling future-generation energy, health, and sustainability technologies. One main factor contributing to this inefficiency is the large combinatorial space of materials which is very sparsely observed. Searches of this large combinatorial space are often biased by expert knowledge and clustered close to material configurations that are known to perform well. Moreover, experimental characterization or first principles quantum mechanical calculations of all possible materials are extremely expensive leading to small available data sets. As a result, there is a need for the development of computational algorithms that can efficiently search this large space for a given material application. In this talk, we introduce, PAL 2.0, a method that combines a physics-informed belief model with Bayesian optimization. Every material is characterized by physical and chemical properties of components of the material in a complex manner but a priori knowledge of the identity of the important properties is often lacking. The key contributing factor of our proposed framework is the creation of a physics-based hypothesis using XGBoost and Neural Networks. The generated hypothesis provides a physics-based prior to the Gaussian process model which is then used to perform a search of the material space. Our method is unique since it picks out the physical descriptors that are most representative of the material domain making the search unbiased toward expert knowledge, which in many cases is unknown. The model also provides valuable chemical insight into the domain that can be used to develop new materials that were outside the domain that was initially searched. More recently our approach is being used in a closed-loop setup with experimentalists to discover high-temperature shape memory alloys.