The materials innovation R&D cycle has long benefitted from the use of physics-based simulation engines such as quantum mechanics and molecular dynamics to help lower the cost of discovering novel chemistries, structures, morphologies, and compositions of materials for a wide array of applications and industries. In the past few years, the growth of computational power and the interest in building large datasets of materials properties has led to the growing adoption of materials informatics and AI-powered approaches in materials science. However, such approaches are highly data intensive and suffer from an inability to extrapolate beyond the chemical space of the training model. In this webinar we demonstrate, using a number of case studies, that the tradeoffs between accuracy and computational complexity lead to a natural synergy between physics-based modeling and machine learning methods and showcase the ability to apply these methods successfully even in the absence of large datasets. By combining the latest physics-based and data-driven approaches, decision-making process for the materials design is quickly assessed over the extensive chemical design space. We will demonstrate this idea over a few recent case studies including in organic electronics, aerospace, automotive, and semiconductor industry. Advanced machine learning techniques such as active learning, genetic optimization, and deep neural network will be utilized to showcase how key materials properties like vapor pressure, electronic structures, chemical stability, optical characteristics, and thermomechanical properties can be predicted with little to no user bias.