Nano-Hall sensors with granular Co-C

We analyzed the performance of Hall sensors with different Co-C ratios, deposited directly in nano-structured form, using $Co_2(CO)_8$ gas molecules, by focused electron or ion beam induced deposition. Due to the enhanced inter-grain scattering in these granular wires, the Extraordinary Hall Effect can be increased by two orders of magnitude with respect to pure Co, up to a current sensitivity of $1 \Omega/T$. We show that the best magnetic field resolution at room temperature is obtained for Co ratios between 60% and 70% and is better than $1 \mu T/Hz^{1/2}$. For an active area of the sensor of $200 \times 200 nm^2$, the room temperature magnetic flux resolution is $\phi_{min} = 2\times10^{-5}\phi_0$, in the thermal noise frequency range, i.e. above 100 kHz.Comment: 5 pages, 4 figure

Benchmarking Materials Property Prediction Methods: The Matbench Test Set and Automatminer Reference Algorithm

We present a benchmark test suite and an automated machine learning procedure for evaluating supervised machine learning (ML) models for predicting properties of inorganic bulk materials. The test suite, Matbench, is a set of 13 ML tasks that range in size from 312 to 132k samples and contain data from 10 density functional theory-derived and experimental sources. Tasks include predicting optical, thermal, electronic, thermodynamic, tensile, and elastic properties given a materials composition and/or crystal structure. The reference algorithm, Automatminer, is a highly-extensible, fully-automated ML pipeline for predicting materials properties from materials primitives (such as composition and crystal structure) without user intervention or hyperparameter tuning. We test Automatminer on the Matbench test suite and compare its predictive power with state-of-the-art crystal graph neural networks and a traditional descriptor-based Random Forest model. We find Automatminer achieves the best performance on 8 of 13 tasks in the benchmark. We also show our test suite is capable of exposing predictive advantages of each algorithm - namely, that crystal graph methods appear to outperform traditional machine learning methods given ~10^4 or greater data points. The pre-processed, ready-to-use Matbench tasks and the Automatminer source code are open source and available online (http://hackingmaterials.lbl.gov/automatminer/). We encourage evaluating new materials ML algorithms on the MatBench benchmark and comparing them against the latest version of Automatminer.Comment: Main text, supplemental inf

Germination response of diverse wild and landrace chile peppers (Capsicum spp.) under drought stress simulated with polyethylene glycol.

Responses to drought within a single species may vary based on plant developmental stage, drought severity, and the avoidance or tolerance mechanisms employed. Early drought stress can restrict emergence and seedling growth. Thus, in areas where water availability is limited, rapid germination leading to early plant establishment may be beneficial. Alternatively, germination without sufficient water to support the seedling may lead to early senescence, so reduced germination under low moisture conditions may be adaptive at the level of the population. We studied the germination response to osmotic stress of diverse chile pepper germplasm collected in southern Mexico from varied ecozones, cultivation systems, and of named landraces. Drought stress was simulated using polyethylene glycol solutions. Overall, survival time analysis revealed delayed germination at the 20% concentration of PEG across all ecozones. The effect was most pronounced in the genotypes from hotter, drier ecozones. Additionally, accessions from wetter and cooler ecozones had the fastest rate of germination. Moreover, accessions of the landraces Costeño Rojo and Tusta germinated more slowly and incompletely if sourced from a drier ecozone than a wetter one, indicating that slower, reduced germination under drought stress may be an adaptive avoidance mechanism. Significant differences were also observed between named landraces, with more domesticated types from intensive cultivation systems nearly always germinating faster than small-fruited backyard- or wild-types, perhaps due to the fact that the smaller-fruited accessions may have undergone less selection. Thus, we conclude that there is evidence of local adaptation to both ecozone of origin and source cultivation system in germination characteristics of diverse chile peppers