Machine learning (ML) has become a part of the fabric of high-throughput screening and computational discovery of materials. Despite its increasingly central role, challenges remain in fully realizing the promise of ML. This is especially true for the practical acceleration of the engineering of robust materials and the development of design strategies that surpass trial and error or high-throughput screening alone. Depending on the quantity being predicted and the experimental data available, ML can either outperform physics-based models, be used to accelerate such models or be integrated with them to improve their performance. Our group focuses on the discovery of new materials through large-scale screening for various applications like hydrocarbon capture and storage, and inverse design of materials for any particular application.