Prediction of material defects and phase formation using deep learning and transfer learning

Machine learning has been successfully employed in computer vision, speech processing, and natural language processing. However, when machine learning is applied to materials study, many challenges remain, and they include small datasets, tricky manual feature (descriptor) engineering, isolated models and poor interpretability. In this project, possible solutions to these challenges have been explored using deep learning and transfer learning.For the challenge of small datasets, fully connected deep neural network and tree-based models were used to predict solidification cracking susceptibility of stainless steels with a dataset of 487 samples. It is found that deep neural network with pre-training and fine-tuning improves prediction accuracy, and tree-based models reveal the relative importance of input variables.To overcome the challenge of tricky manual feature engineering in predicting phase formation in inorganic substances and compounds properties, I proposed a general and transferable deep learning framework as follows: (1) mapping raw data to pseudo images with periodic table structure, (2) automatically extracting features through convolutional neural networks, (3) transferring knowledge by sharing features extractors between models. The proposed deep learning models outperformed previous models in predicting glass-forming ability using a medium dataset of 16k samples and compounds properties using a big dataset of 228k samples. The developed transfer learning model for multi-principal element alloys can distinguish five phases (BCC, FCC, HCP, amorphous, mixture) with high scores (0.94) in a small dataset of 345 samples. The transfer learning model for phase prototypes can discriminate 170 phase prototypes with an accuracy of 0.9 in a dataset of 17k inorganic substances. Periodic table knowledge embedded in data representations and knowledge shared between models is beneficial for tasks with small datasets.</div

sj-docx-1-slr-10.1177_02676583241232218 – Supplemental material for Online processing and offline judgments of different types of presupposition triggers by second language speakers

Supplemental material, sj-docx-1-slr-10.1177_02676583241232218 for Online processing and offline judgments of different types of presupposition triggers by second language speakers by Shuo Feng and Kailun Zhang in Second Language Research</p

Use of human amniotic epithelial cells in mouse models of bleomycin-induced lung fibrosis: A systematic review and meta-analysis - Fig 4

<p>Egger’s Plots of Ashcroft Scores (A) and Lung Collagen Contents (B).</p

Summary of inflammation and fibrosis.

<p>Summary of inflammation and fibrosis.</p

Included and excluded studies.

<p>Included and excluded studies.</p

Characteristics of included studies.

<p>Characteristics of included studies.</p

Use of human amniotic epithelial cells in mouse models of bleomycin-induced lung fibrosis: A systematic review and meta-analysis - Fig 3

<p><b>Funnel Plots of Ashcroft Scores (A) and Lung Collagen Contents (B).</b> SE: standard error; and SMD: standard mean difference.</p

Examining the Long-Range Effect in Very Long Graphene Nanoribbons: A First-Principles Study

The role of long-range effect on the modulation of the electronic structure of graphene nanoribbons has been little studied due to the limitations of existing theoretical and computational methods. By splitting a molecule top-down and calculating and jointing the Fock matrix of fragments, we developed a computational method suitable for large-size molecules with random doping and arbitrary geometry. Utilizing this method, we achieved the study of the effects of dopants and curvature on graphene nanoribbons (GNRs). It reveals that both dopants and curvature can change the charge distribution of GNRs, while the influence of dopants is more significant and can extend up to 1–3 nm. The electronic excitation properties of GNRs are also largely modified by the doping state or nonuniform curvature. Our findings provide not only a feasible approach for studying the electronic structure of large-size molecules but also the possibility to improve the properties of graphene-based materials by dopants and local curvature

Additional file 1: of Influence of age on results following surgery for displaced acetabular fractures in the elderly

Detailing patients’ data. (XLSX 31 kb

Interconnected Fe, S, N‑Codoped Hollow and Porous Carbon Nanorods as Efficient Electrocatalysts for the Oxygen Reduction Reaction

As promising precious metal-free oxygen reduction reaction (ORR) electrocatalysts, Fe–N–C catalysts still face a great challenge to meet the requirement of practical applications. In this study, Fe, S, N-codoped hollow and porous carbon nanorods (Fe–S–N HPCNRs) were designed with the aim of improving the performance of Fe–N–C catalysts from the perspective of composition and structure. They were successfully prepared using cysteine, Fe²⁺ salt, and polydopamine-encapsulated ZnO nanorods (ZnO NRs@PDA) as precursors by a pyrolysis-acid etching strategy. The hollow and porous structure and composition of Fe, S, N, and C were verified by transmission electron microscopy, X-ray diffraction, Brunauer–Emmett–Teller, and X-ray photoelectron spectroscopy tests. At the optimum ratio of ZnO NRs@PDA/cysteine and pyrolysis temperature, the Fe–S–N HPCNRs display higher ORR activities than the control samples which are lack of one of the precursors. Electrochemical tests show that the ORR follows a 4e pathway with the Fe–S–N HPCNRs. In addition, the long-term stability and methanol tolerance of Fe–S–N HPCNRs are good and superior to those of 20 wt % Pt/C