Hasil untuk "q-bio.SC"

Jian Zhou, Fengling Zhuo, Xinxin Long et al.

Eda Oktay, Erin Carson

Mitsutaro Umehara, Helge S. Stein, Dan Guevarra et al.

AbstractMachine learning for materials science envisions the acceleration of basic science research through automated identification of key data relationships to augment human interpretation and gain scientific understanding. A primary role of scientists is extraction of fundamental knowledge from data, and we demonstrate that this extraction can be accelerated using neural networks via analysis of the trained data model itself rather than its application as a prediction tool. Convolutional neural networks excel at modeling complex data relationships in multi-dimensional parameter spaces, such as that mapped by a combinatorial materials science experiment. Measuring a performance metric in a given materials space provides direct information about (locally) optimal materials but not the underlying materials science that gives rise to the variation in performance. By building a model that predicts performance (in this case photoelectrochemical power generation of a solar fuels photoanode) from materials parameters (in this case composition and Raman signal), subsequent analysis of gradients in the trained model reveals key data relationships that are not readily identified by human inspection or traditional statistical analyses. Human interpretation of these key relationships produces the desired fundamental understanding, demonstrating a framework in which machine learning accelerates data interpretation by leveraging the expertize of the human scientist. We also demonstrate the use of neural network gradient analysis to automate prediction of the directions in parameter space, such as the addition of specific alloying elements, that may increase performance by moving beyond the confines of existing data.

Daniel E. Perea, Daniel K. Schreiber, Joseph V. Ryan et al.

AbstractCryo-based atom probe tomography has been applied to directly reveal the water-solid interface and hydrated corrosion layers making up the nanoscale porous structure of a corroded borosilicate glass in its native aqueous environment. The analysis includes morphology and compositional mapping of the inner gel/glass interface, isolation of a tomographic sub-volume of the tortuous water-filled gel, and comparison of the gel structure with simulations. The nanoscale porous structure is qualitatively consistent with that of the molecular dynamics simulation, enabling in greater confidence in both interrogations. Comparison of the gel/glass interface between desiccated and cryogenically preserved samples reveals consistently abrupt B dissolution behavior and quantitative differences in the apparent H ingress into the glass. These comparisons give some guidance to future experimental approaches to understanding glass corrosion behavior. More broadly, the cryogenic preservation and 3D visualization of the native water/solid structure in 3D at the nanoscale has direct relevance to a wide range of materials systems beyond glass science.

Tikina R. Sethy, Arun K. Pradhan, Prafulla K. Sahoo

Johannes Taendl, Cecilia Poletti

H. Bo, L.B. Liu, Z.P. Jin

Yako AB, Hassan SC, Olayinka MD et al.

Jani CP, Chovatia PK, Kathiriya SC et al.

Bing Xu, Dao-li Zhu

S. Booth, E. Mourao

J.M. Malard, R.D. Stewart

D. Petrou, G.A. Gibson, G.R. Ganger