Distributed Algorithms for AI Accelerated Materials Discovery

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In the rapidly evolving field of chemical discovery, the integration of high-throughput experimental data with computational simulation results offers a promising avenue to accelerate the identification of novel compounds and understand their properties. This research introduces a novel artificial intelligence framework leveraging multi-Fidelity Bayesian machine learning models, including Gaussian Processes (GPs) and Bayesian Neural Networks (BNNs), to harmonize insights drawn from disparate data sources-laboratory experiments and computer-driven simulations. By employing Multi-Output Bayesian methods, our approach uniquely captures the correlations between different physical and chemical outcomes, as well as the inherent biases in datasets of varying fidelity. The framework's utility is demonstrated through its application in predicting the properties of new chemical entities, thereby informing experimental design and decision-making processes in drug discovery and materials science. Our findings indicate a substantial enhancement in prediction accuracy and efficiency, underscoring the potential of multi-fidelity models to revolutionize chemical discovery by enabling a more effective fusion of experimental and simulated data. This work not only contributes to the methodological advancements in the application of AI in chemistry but also sets a new benchmark for future research aiming to optimize the synergy between computational simulations and empirical experiments for accelerated discovery