Density functional theoryDensity Functional Theory (DFT) (DFT) provides an accurate way to compute many properties of materials from first principles. Nevertheless, an exhaustive exploration of chemical space in the quest for new materials requires prohibitive computational resources and is impractical. Artificial intelligence (AI) and machine learning (ML) can work in synergy with DFTDensity Functional Theory (DFT) computations to accelerate the pace of discovery of novel materials with specific properties of use to society. AI can help leverage the power and throughput of DFTDensity Functional Theory (DFT) by using accurate DFTDensity Functional Theory (DFT)-computed properties as the ground truth for training, validating and iteratively improving the accuracy of AI models. This chapter briefly reviews the fundamentals of DFTDensity Functional Theory (DFT), and then surveys several recent examples of specific applications where AI/ML methods, such as graph convolutional models, generative adversarial networksGenerative Adversarial Networks (GAN), reinforcement learning, active learningActive learning, transfer learningTransfer learning andTransformers transformer-based language models have been employed in conjunction with DFTDensity Functional Theory (DFT) computations to design novel materials much more rapidly than was hitherto considered possible.