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Discovering novel molecules with targeted properties remains a formidable challenge in materials science, often likened to finding a needle in a haystack. Traditional experimental approaches are slow, costly, and inefficient. In this study, we present an inverse design framework based on a molecular graph conditional variational autoencoder (CVAE) that enables the generation of new molecules with user-specified optical properties, particularly molar extinction coefficient ($$\varepsilon$$). Our model encodes molecular graphs, derived from SMILES strings, into a structured latent space, and then decodes them into valid molecular structures conditioned on a target $$\varepsilon$$ value. Trained on a curated dataset of known molecules with corresponding extinction coefficients, the CVAE learns to generate chemically valid structures, as verified by RDKit. Subsequent Density Functional Theory (DFT) simulations confirm that many of the generated molecules exhibit the electronic structures similar to those molecules with desired $$\varepsilon$$ values. We have also verified the $$\varepsilon$$ values of the generated molecules using a graph neural network (GNN) and the synthesizability of those molecules using an open-source module named ASKCOS. This approach demonstrates the potential of CVAEs to accelerate molecular discovery by enabling user-guided, property-driven molecule generation -- offering a scalable, data-driven alternative to traditional trial-and-error synthesis. 

While scientific workflows have been established and used in a number of disciplines for specifying and executing experiments and data analysis, early and recent studies have demonstrated that an important proportion of workflows suffer from decay. This phenomena is exacerbated by legacy scientific workflow systems, notably Taverna, which was popular in e-science for orchestrating complex analyses. A step towards addressing this issue, we report on in this paper a feasibility study on using generative AI to revive decayed workflows, combining large language models with modern workflow technologies. Our approach automates critical revival tasks including parsing of legacy Taverna workflows, failure point identification, repair suggestion, and conversion to contemporary formats, viz. SnakeMake. The methodology integrates AI-driven workflow summarization, pseudocode abstraction, graph-based visualization, automated service substitution, and code generation. We demonstrate and evaluate this approach through a real-world decayed workflow case study. We conclude the paper with a discussion on key lessons that we learned and will guide development of a systematic workflow revival framework as part of our future work. 

The rise of Large Language Models (LLMs) as powerful knowledge-processing tools has sparked a wave of innovation in tutoring and assessment systems. Despite their well-documented limitations, LLMs offer unique capabilities that have been effectively harnessed for automated feedback generation and grading in intelligent learning environments. In this paper, we introduce {\em Project 360}, an experimental intelligent tutoring system designed for teaching SQL. Project 360 leverages the concept of {\em query equivalence} to assess the accuracy of student queries, using ChatGPT’s advanced natural language analysis to measure their semantic distance from a reference query. By integrating LLM-driven evaluation, Project 360 significantly outperforms traditional SQL tutoring and grading systems, offering more precise assessments and context-aware feedback. This study explores the feasibility and limitations of using ChatGPT as the analytical backbone of Project 360, evaluating its reliability for autonomous tutoring and assessment in database education. Our findings provide valuable insights into the evolving role of LLMs in education, highlighting their potential to revolutionize SQL learning while identifying areas for further refinement and improvement.