Revitalizing Chemical Research in the Era of Large Language Models

The collection of references primarily focuses on advancements in machine learning (ML) and large language models (LLMs) in the realm of materials and chemical sciences. It highlights significant studies, including the use of ML for predicting molecular properties, organic photovoltaic materials, and chemical reaction optimization. The emergence of deep learning techniques, such as graph neural networks and Bayesian optimization, showcases innovations in predictive chemistry and automated experimentation. Various platforms and toolkits, including chemoinformatics software, are discussed for their role in data mining and integration of knowledge in materials engineering. Key experiments and methodologies reveal the potential of LLMs to enhance scientific discovery through autonomous systems and reinforcement learning. Overall, the literature underlines the transformative impact of computational models on research efficiency and accuracy in chemistry and materials science, indicating a shift towards automated, data-intensive approaches for accelerating discovery in these fields.

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