11 papers · ranked by Valyu relevance
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The value of generative artificial intelligence (AI) for teaching and learning is currently hotly debated. Concerns regarding the accuracy of information produced by generative AI as well as student over-reliance on this tool coexist with excitement about tailored opportunities that AI may provide for educational…
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This article proposes a three-level classification of artificial intelligence (AI) application in chemical sciences, reflecting the increasing degree of technology involvement in scientific and production processes: from automation of routine tasks (the level of "AI Assistant"), to the creation of specialized…
Tatsuya Yoshizawa, Shoichi Ishida, Tomohiro Sato, Masateru Ohta + 2 more
Molecular design using data-driven generative models has emerged as a promising technology, impacting various fields such as drug discovery and the development of functional materials. However, this approach is often susceptible to optimization failure due to reward hacking, where prediction models fail to accurately…
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The description of heterogeneous catalysis is challenged by the intricacy of numerous multi-scale processes that govern the performance of catalyst materials. The chemical environment of the catalytic process and the kinetics of structural changes create configurations of typically unknown local geometries and…
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Machine learning (ML) models are increasingly used in quantum chemistry, but their reliability hinges on uncertainty quantification (UQ). In this study, we compare two prominent UQ paradigms—Deep Evidential Regression (DER) and Deep Ensembles—on the QM9 and WS22 datasets, with a specific emphasis on the role of post…
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Artificial intelligence (AI) is poised to transform heterogeneous catalysis, ushering in a new paradigm for catalytic materials discovery. By uncovering intricate patterns in high-dimensional data, AI has been reshaping our pursuit of sustainable catalytic processes across the energy, environmental, and chemical…
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As the utilization of artificial intelligence (AI) and generative AI (GenAI) is expanding in the educational field, presenting significant implications for STEM disciplines, it is bringing opportunities to enhance how chemistry and chemical engineering are taught and learned. This perspective critically explores the…
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Artificial intelligence (AI) is reshaping chemical engineering. Still, its role in safety-critical operations is limited because we rarely see tools that link physical models with data-driven methods. This study brings together three elements: physics-constrained neural networks, uncertainty quantification, and a…
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Artificial intelligence (AI) is reshaping scientific research by accelerating discovery and enabling the analysis of complex data that traditional methods struggle to handle. This review examines over 310,000 journal articles and patents from the CAS Content Collection (2015–2025), with a focus on, biomedical research…
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Graph Neural Networks (GNNs) are powerful tools for molecular property prediction, but they are not magic. When applied to molecules unlike their training data, they produce unreliable predictions that are difficult to detect. The Applicability Domain (AD) concept addresses this by defining regions of chemical space…
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Theoretical prediction of enantioselectivity for broad range of substrates in a given reaction has long been a formidable challenge, traditionally replaced by labor-intensive screening of multiple conditions. Until recently this remained an unaddressed problem in asymmetric catalysis under data-limited scenarios, yet…