Search for: All records

Local atomic environment descriptors (LAEDs) are used in the materials science and chemistry communities, for example, for the development of machine learning interatomic potentials. Despite the fact that LAEDs have been extensively studied and benchmarked for various applications, global structure descriptors (GSDs), i.e., descriptors for entire molecules or crystal structures, have been mostly developed independently based on other approaches. Here, we propose a systematically improvable methodology for constructing a space of representations of GSDs from LAEDs by incorporating statistical information and information about chemical elements. We apply the method to construct GSDs of varying complexity for lithium thiophosphate structures that are of interest as solid electrolytes and use an information-theoretic approach to obtain an optimally compressed GSD. Finally, we report the performance of the compressed GSD for energy prediction tasks. 

The prediction of temperature effects from first principles is computationally demanding and typically too approximate for the engineering of high-temperature processes. Here, we introduce a hybrid approach combining zero-Kelvin first-principles calculations with a Gaussian process regression model trained on temperature-dependent reaction free energies. We apply this physics-based machine-learning model to the prediction of metal oxide reduction temperatures in high-temperature smelting processes that are commonly used for the extraction of metals from their ores and from electronics waste and have a significant impact on the global energy economy and greenhouse gas emissions. The hybrid model predicts accurate reduction temperatures of unseen oxides, is computationally efficient, and surpasses in accuracy computationally much more demanding first-principles simulations that explicitly include temperature effects. The approach provides a general paradigm for capturing the temperature dependence of reaction free energies and derived thermodynamic properties when limited experimental reference data is available.

 

Somatic mosaicism is defined as an occurrence of two or more populations of cells having genomic sequences differing at given loci in an individual who is derived from a single zygote. It is a characteristic of multicellular organisms that plays a crucial role in normal development and disease. To study the nature and extent of somatic mosaicism in autism spectrum disorder, bipolar disorder, focal cortical dysplasia, schizophrenia, and Tourette syndrome, a multi-institutional consortium called the Brain Somatic Mosaicism Network (BSMN) was formed through the National Institute of Mental Health (NIMH). In addition to genomic data of affected and neurotypical brains, the BSMN also developed and validated a best practices somatic single nucleotide variant calling workflow through the analysis of reference brain tissue. These resources, which include >400 terabytes of data from 1087 subjects, are now available to the research community via the NIMH Data Archive (NDA) and are described here.

 

Transient noise, called "glitches," can mimic and obscure real gravitational waves in the strain data channel. One machine learning software package used to classify these glitches and identify their sources, GravitySpy, is successful when the spectrogram of the glitch has a very distinct and unique shape. However, one of the most common types of glitches, called a "blip," has an indistinct shape due to so few cycles being in-band, and tends to ring off template signals of binary black hole mergers, making it especially necessary to eliminate blips for future observing runs. Here we examine blip glitches in a Q-transform spectrogram with different parameters than those used by GravitySpy to determine if there are sub-classifications of blips that might have identifiable sources, and then use Convolutional Neural Networks to sub-classify these blips. The implementation of Convolutional Neural Networks has provided compelling evidence of distinguishable differences between these hypothesized sub-classes. 

Lattice oxygen redox yields anomalous capacity and can significantly increase the energy density of layered Li‐rich transition metal oxide cathodes, garnering tremendous interest. However, the mechanism behind O redox in these cathode materials is still under debate, in part due to the challenges in directly observing O and following associated changes upon electrochemical cycling. Here, with¹⁷O NMR as a direct probe of O activities, it is demonstrated that stacking faults enhance O redox participation compared with Li₂MnO₃domains without stacking faults. This work is concluded by combining both ex situ and in situ¹⁷O NMR to investigate the evolution of O at 4i, 8j sites from monoclinicC2/mand 6c(1), 6c(2), 6c(3) sites from the stacking faults (P3₁12). These measurements are further corroborated and explained by first‐principles calculations finding a stabilization effect of stacking faults in delithiated Li₂MnO₃. In situ¹⁷O NMR tracks O activities with temporal resolution and provides a quantitative determination of reversible O redox versus irreversible processes that form short covalent OO bonds. This work provides valuable insights into the O redox reactions in Li‐excess layered cathodes, which may inspire new material design for cathodes with high specific capacity.

 

Fluorine substitution is a critical enabler for improving the cycle life and energy density of disordered rocksalt (DRX) Li‐ion battery cathode materials which offer prospects for high energy density cathodes, without the reliance on limited mineral resources. Due to the strong Li–F interaction, fluorine also is expected to modify the short‐range cation order in these materials which is critical for Li‐ion transport. In this work, density functional theory and Monte Carlo simulations are combined to investigate the impact of Li–F short‐range ordering on the formation of Li percolation and diffusion in DRX materials. The modeling reveals that F substitution is always beneficial at sufficiently high concentrations and can, surprisingly, even facilitate percolation in compounds without Li excess, giving them the ability to incorporate more transition metal redox capacity and thereby higher energy density. It is found that for F levels below 15%, its effect can be beneficial or disadvantageous depending on the intrinsic short‐range order in the unfluorinated oxide, while for high fluorination levels the effects are always beneficial. Using extensive simulations, a map is also presented showing the trade‐off between transition‐metal capacity, Li‐transport, and synthetic accessibility, and two of the more extreme predictions are experimentally confirmed.