Search

The article "Discovering trends in big data: general discussion" from Faraday Discussions delves into the interpretability of machine learning models in chemistry, specifically in reaction prediction and retrosynthesis. It evaluates the performance and interpretability of models like FlanT5 and ByT5, highlighting the challenges of training models for multi-task chemistry applications. The text emphasizes the significance of data size and quality in training machine learning models for chemistry, while also discussing issues like overprediction in crystal structure prediction and the importance of crediting data generators in large databases. Additionally, it addresses the sustainable development of larger code projects, advocating for open-source repositories, transparent code guidance, and automated testing. The article also touches on the increasing adoption of FAIR tools, particularly among younger students, for data reuse and sharing, with a focus on standards and interoperability in the industry. [Extracted from the article]

The Faraday Discussions article explores the use of open-source software in academia and the challenges of competing with corporate-funded projects, focusing on the development of Web-BO for predicting anisotropic dielectric tensors in materials. It discusses the potential applications of anisotropic materials in various technologies and their impact on fields like metamaterials and thermoelectrics. The text also addresses data integrity, synthesis, and computational methods in scientific research, emphasizing the importance of high-quality data, proper training, and standardized procedures in both industry and academia, as well as the need for shared best practices and open peer review systems to enhance reproducibility and clarity in research. [Extracted from the article]

The Faraday Discussions journal article "Discovering chemical structure: general discussion" features a conversation among experts in chemistry discussing the use of technology, AI scientists, and machine learning in computational chemistry to accelerate scientific discovery. The experts explore challenges and opportunities in crystal structure prediction, emphasizing the importance of diversity in generated structures and optimization methods for efficiency. Venkat Kapil's work on fine-tuning machine learning models for molecular systems shows promise, but considerations must be made regarding generalizability and data quality. The text also discusses the use of machine learning interatomic potentials (MLIPs) in predicting crystal structures, solvation dynamics, and chemical reactions, highlighting the trade-off between computational speed and accuracy. Researchers stress the importance of accurate electronic structure theory and sufficient training data for MLIPs to be effective in real-world applications. [Extracted from the article]

The prediction of new compounds via crystal structure prediction may transform how the materials chemistry community discovers new compounds. In the prediction of inorganic crystal structures there are three distinct classes of prediction: performing crystal structure prediction via heuristic algorithms, using a range of established crystal structure prediction codes, an emerging community using generative machine learning models to predict crystal structures directly and the use of mathematical optimisation to solve crystal structures exactly. In this work, we demonstrate the combination of heuristic and generative machine learning, the use of a generative machine learning model to produce the starting population of crystal structures for a heuristic algorithm and discuss the benefits, demonstrating the method on eight known compounds with reported crystal structures and three hypothetical compounds. We show that the integration of machine learning structure generation with heuristic structure prediction results in both faster compute times per structure and lower energies. This work provides to the community a set of eleven compounds with varying chemistry and complexity that can be used as a benchmark for new crystal structure prediction methods as they emerge. [ABSTRACT FROM AUTHOR]