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Computational exploration of the compositional spaces of materials can provide guidance for synthetic research and thus accelerate the discovery of novel materials. Most approaches employ high-throughput sampling and focus on reducing the time for energy evaluation for individual compositions, often at the cost of accuracy. Here, we present an alternative approach focusing on effective sampling of the compositional space. The learning algorithm PhaseBO optimizes the stoichiometry of the potential target material while improving the probability of and accelerating its discovery without compromising the accuracy of energy evaluation. [ABSTRACT FROM AUTHOR]

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]

The vast size of composition space poses a significant challenge for materials chemistry: exhaustive enumeration of potentially interesting compositions is typically infeasible, hindering assessment of important criteria ranging from novelty and stability to cost and performance. We report a tool, Comgen, for the efficient exploration of composition space, which makes use of logical methods from computer science used for proving theorems. We demonstrate how these techniques, which have not previously been applied to materials discovery, can enable reasoning about scientific domain knowledge provided by human experts. Comgen accepts a variety of user‐specified criteria, converts these into an abstract form, and utilises a powerful automated reasoning algorithm to identify compositions that satisfy these user requirements, or prove that the requirements cannot be simultaneously satisfied. In contrast to machine learning techniques, explicitly reasoning about domain knowledge, rather than making inferences from data, ensures that Comgen's outputs are fully interpretable and provably correct. Users interact with Comgen through a high‐level Python interface. We illustrate use of the tool with several case studies focused on the search for new ionic conductors. Further, we demonstrate the integration of Comgen into an end‐to‐end automated workflow to propose and evaluate candidate compositions quantitatively, prior to experimental investigation. This highlights the potential of automated formal reasoning in materials chemistry. [ABSTRACT FROM AUTHOR]

High-resolution MRI images are essential for the accurate diagnosis of diseases. Increasing the magnetic field strength achieves this but also raises the Specific Absorption Rate (SAR) of electromagnetic waves in the body, leading to safety concerns. Currently, SAR is managed by assuming an even distribution of electromagnetic waves; however, high-field MRI can cause localized concentrations. No direct method for SAR visualization exists that relies on numerical analysis, resulting in conservative management and inefficiency. This paper presents a method for converting temperature images from magnetic resonance imaging (MRI) into SAR images using Pennes' bioheat equation and Fourier transformation with gentle low-pass filtering. The method was tested using an agar-gel phantom heated with a heating RF coil, and the temperature changes were measured using the proton resonance frequency shift (PRFS) technique in a 3T MRI system. The results are validated by electromagnetic numerical simulations using xFDTD. Experimental results showed SAR images could be reconstructed from temperature images with a maximum error of 3.6 % compared with numerical simulations. The reconstruction took 5.2 s on a PC. This method can aid in managing electromagnetic safety in MRI and verifying the RF coil performance during development. Future studies will validate this method using different phantom configurations and in vivo experiments. [ABSTRACT FROM AUTHOR]

PurposeTo predict signal-to-noise ratio (SNR) trends and absorbed energy in magnetic resonance imaging (MRI) of the brain up to 14T.Materials and methodsA human head in an eight-channel transmit/receive coil was simulated with Maxwell and Bloch equations to determine excitation homogeneity with radiofrequency (RF) shimming, image homogeneity, SNR, and absorbed energy in MRI from 1.5 to 14T considering realistic field distributions and relaxation properties.ResultsRF shimming alone achieved a standard deviation in excitation flip angle less than 10° in mid-brain up to 14T, but produced a small region with low excitation on a lower slice. Current reconstruction methods may produce shading artifacts at 14T. SNR increases with a greater-than-linear rate for gradient recalled echo (GRE) sequences having short (2 msec) echo time (TE) and long relaxation time (TR) (∼2.3-fold increase from 7T to 14T), but a less-than-linear rate if TE is 10 msec (∼1.6-fold increase from 7T to 14T). Depending on the sequence, SNR per square root of imaging time may produce a less-than-linear increase with B0 . Whole-head absorbed energy shows a less-than-quadratic increase with B0 (1.7-fold increase from 7T to 14T).ConclusionNumerical simulations indicate that with proper preparation and precautions, imaging of the human brain at up to 14T could be performed safely, with advantages in SNR.

Glass-crystallisation synthesis is coupled to probe structure prediction for the guided discovery of new metastable oxides in the SrO–Al2O3–SiO2 phase field, yielding a new ternary ribbon-silicate, Sr2Si3O8. In principle, this methodology can be applied to a wide range of oxide chemistries by selecting an appropriate non-equilibrium synthesis route. [ABSTRACT FROM AUTHOR]