Ready for Another Night

As the field of 2D halide perovskites (HPs) matures, state-of-the-art techniques to measure important properties, such as the band gap (E_g) and exciton binding energy (E_b), continue to produce inconsistent values. Here, we tackle this long-standing problem by obtaining direct measurements of E_g and E_b for 31 unique HP structures. The E_b values are lower than in previous literature reports and lower than expected from standard theory that assumes excitons are screened by optical-frequency dielectric constants. These low E_b values are shown to be a consequence of unique screening effects, such as superlattice screening and phonon screening. We find a strikingly strong correlation between E_b and E_g and provide design principles to a priori tune E_g and E_b to their optimal values. As such, this work offers a blueprint for E_g-E_b engineering of low-dimensional semiconductors as an even more useful replacement for simply band-gap engineering.

Symmetry-adapted distortion modes provide a natural way of describing distorted structures derived from higher-symmetry parent phases. Structural refinements using symmetry-mode amplitudes as fit variables have been used for at least ten years in Rietveld refinements of the average crystal structure from diffraction data; more recently, this approach has also been used for investigations of the local structure using real-space pair distribution function (PDF) data. Here, the value of performing symmetry-mode fits to PDF data is further demonstrated through the successful application of this method to two topical materials: TiSe2, where a subtle but long-range structural distortion driven by the formation of a charge-density wave is detected, and MnTe, where a large but highly localized structural distortion is characterized in terms of symmetry-lowering displacements of the Te atoms. The analysis is performed using fully open-source code within the DiffPy framework via two packages developed for this work: isopydistort, which provides a scriptable interface to the ISODISTORT web application for group theoretical calculations, and isopytools, which converts the ISODISTORT output into a DiffPy-compatible format for subsequent fitting and analysis. These developments expand the potential impact of symmetry-adapted PDF analysis by enabling high-throughput analysis and removing the need for any commercial software.

Temperature variations in the ocean cause changes in the sound speed and, hence, sound propagation. This project quantified the sound speed variation achievable in a laboratory water tank. The rectangular tank has paneling that minimizes lateral reflections. Two temperature sensors measured the temperature changes over time while the water was cooled with ice, heated, and naturally warmed back to room temperature. Sound speed values were calculated using the freshwater Marczak equation. We found that while the temperature remains relatively uniform near the bottom of the tank during heating and cooling. Heating increases the sound speed at a rate of 3.5 m/s per hour, while adding ice in various quantities decreases the temperature rapidly. After rapid cooling, the water near the surface of the tank warms faster than the water near the bottom, creating a depth-dependent sound speed gradient. Eight hours after adding 380 pounds of pebble ice, the sound speed gradient was 10.7 m/s per meter. The water temperature variability in these tank measurements replicates a portion of the sound speed variability seen in the ocean. This sound speed variability can then be used to test the robustness of machine learning algorithms.

Technologies that function at room temperature often require magnets with a high Curie temperature, T-C, and can be improved with better materials. Discovering magnetic materials with a substantial T-C is challenging because of the large number of candidates and the cost of fabricating and testing them. Using the two largest known datasets of experimental Curie temperatures, we develop machine-learning models to make rapid T-C predictions solely based on the chemical composition of a material. We train a random-forest model and a k-NN one and predict on an initial dataset of over 2500 materials and then validate the model on a new dataset containing over 3000 entries. The accuracy is compared for multiple compounds' representations ("descriptors") and regression approaches. A random-forest model provides the most accurate predictions and is not improved by dimensionality reduction or by using more complex descriptors based on atomic properties. A random-forest model trained on a combination of both datasets shows that cobalt-rich and iron-rich materials have the highest Curie temperatures for all binary and ternary compounds. An analysis of the model reveals systematic error that causes the model to over-predict low-T-C materials and under-predict high-T-C materials. For exhaustive searches to find new high-T-C materials, analysis of the learning rate suggests either that much more data is needed or that more efficient descriptors are necessary.

The presence of dust in spiral galaxies affects the ability of photometric decompositions to retrieve the parameters of their main structural components. For galaxies in an edge-on orientation, the optical depth integrated over the line of sight is significantly higher than for those with intermediate or face-on inclinations, so it is only natural to expect that for edge-on galaxies, dust attenuation should severely influence measured structural parameters. In this paper, we use radiative transfer simulations to generate a set of synthetic images of edge-on galaxies that are then analysed via decomposition. Our results demonstrate that for edge-on galaxies, the observed systematic errors of the fit parameters are significantly higher than for moderately inclined galaxies. Even for models with a relatively low dust content, all structural parameters suffer offsets that are far from negligible. In our search for ways to reduce the impact of dust on retrieved structural parameters, we test several approaches, including various masking methods and an analytical model that incorporates dust absorption. We show that using such techniques greatly improves the reliability of decompositions for edge-on galaxies.

Objectives: Evaluate the gland-level annotations in the PANDA Dataset and provide specific recommendations for the development of an improved prostate adenocarcinoma dataset. Provide insight into why currently developed artificial intelligence (AI) algorithms designed for automatic prostate adenocarcinoma detection have failed to be clinically applicable.

Methods: A neural network model was trained on 5009 Whole Slide Images (WSIs) from PANDA. One expert pathologist repeatedly performed gland-level annotations on 50 PANDA WSIs to create a test set and estimate an intra-pathologist variability value. Dataset labels, expert annotations, and model predictions were compared and analyzed.

Results: We found an intra-pathologist accuracy of 0.83 and Prevalence-Adjusted Bias-Adjusted Kappa (kappa) value of 0.65. The model predictions and dataset labels comparison yielded 0.82 accuracy and 0.64 kappa. The model predictions and dataset labels showed low concordance with the expert pathologist.

Conclusions: Simple AI models trained on PANDA achieve accuracies comparable to intra-pathologist accuracies. Due to variability within or between pathologists these models will unlikely find clinically application. A shift in dataset curation must take place. We urge for the creation of a dataset with multiple annotations from a group of experts. This will enable AI models, trained on this dataset, to produce panel opinions which augment pathological decision making.