An emerging machine learning strategy for the assisted‐design of high-performance supercapacitor materials by mining the relationship between capacitance and structural features of porous carbon

Abstract We have previously demonstrated the isolation of platelet membrane glycoprotein IIb-IIIa by affinity chromatography with a specific monoclonal antibody. We have now separated the polypeptide subunits IIb and IIIa of the isolated glycoprotein by preparative sodium dodecyl sulfate polyacrylamide gel electrophoresis and have compared their structural features. Both IIb and IIIa contain approximately 15% carbohydrate, but IIIa contains a larger percentage of mannose residues, suggesting the presence of high mannose as well as complex N- linked oligosaccharide chains. The amino acid compositions are sufficiently similar to imply areas of sequence homology between the two subunits. To examine further the relationship between the subunits, we digested a mixture of 125I-IIb and 131I-IIIa with trypsin and then separated the radiolabeled peptides by high performance liquid chromatography. The resultant peptide maps of IIb and IIIa are completely different. This indicates that neither subunit is derived from the other and suggests that polypeptides IIb and IIIa are products of separate genes.

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Quantifying the link between local structure and cellular rearrangements using information in models of biological tissues

Soft Matter ◽

10.1039/d0sm01575j ◽

2021 ◽

Author(s):

Indrajit Tah ◽

Tristan Sharp ◽

Andrea Liu ◽

Daniel Marc Sussman

Keyword(s):

Machine Learning ◽

Local Structure ◽

Biological Tissues ◽

Structural Features ◽

Machine Learning Techniques ◽

Glass Forming ◽

Learning Techniques ◽

Glass Forming Materials ◽

The Relationship

Machine learning techniques have been used to quantify the relationship between local structural features and variations in local dynamical activity in disordered glass-forming materials. To date these methods have been...

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Isolation and structural characterization of the polypeptide subunits of membrane glycoprotein IIb-IIIa from human platelets

Blood ◽

10.1182/blood.v59.1.80.bloodjournal59180 ◽

1982 ◽

Vol 59 (1) ◽

pp. 80-85 ◽

Cited By ~ 1

Author(s):

RP McEver ◽

JU Baenziger ◽

PW Majerus

Keyword(s):

High Performance ◽

Polyacrylamide Gel Electrophoresis ◽

Sodium Dodecyl ◽

Structural Features ◽

Human Platelets ◽

Membrane Glycoprotein ◽

Radiolabeled Peptides ◽

The Relationship ◽

Peptide Maps

We have previously demonstrated the isolation of platelet membrane glycoprotein IIb-IIIa by affinity chromatography with a specific monoclonal antibody. We have now separated the polypeptide subunits IIb and IIIa of the isolated glycoprotein by preparative sodium dodecyl sulfate polyacrylamide gel electrophoresis and have compared their structural features. Both IIb and IIIa contain approximately 15% carbohydrate, but IIIa contains a larger percentage of mannose residues, suggesting the presence of high mannose as well as complex N- linked oligosaccharide chains. The amino acid compositions are sufficiently similar to imply areas of sequence homology between the two subunits. To examine further the relationship between the subunits, we digested a mixture of 125I-IIb and 131I-IIIa with trypsin and then separated the radiolabeled peptides by high performance liquid chromatography. The resultant peptide maps of IIb and IIIa are completely different. This indicates that neither subunit is derived from the other and suggests that polypeptides IIb and IIIa are products of separate genes.

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Improving prediction accuracy of high-performance materials via modified machine learning strategy

Computational Materials Science ◽

10.1016/j.commatsci.2021.111181 ◽

2022 ◽

Vol 204 ◽

pp. 111181

Author(s):

Wei Yong ◽

Hongtao Zhang ◽

Huadong Fu ◽

Yaliang Zhu ◽

Jie He ◽

...

Keyword(s):

Machine Learning ◽

Prediction Accuracy ◽

High Performance ◽

Learning Strategy

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Machine learning–assisted molecular design and efficiency prediction for high-performance organic photovoltaic materials

Science Advances ◽

10.1126/sciadv.aay4275 ◽

2019 ◽

Vol 5 (11) ◽

pp. eaay4275 ◽

Cited By ~ 22

Author(s):

Wenbo Sun ◽

Yujie Zheng ◽

Ke Yang ◽

Qi Zhang ◽

Akeel A. Shah ◽

...

Keyword(s):

Machine Learning ◽

High Performance ◽

Molecular Design ◽

Structure Property ◽

Photovoltaic Properties ◽

Chemical Structures ◽

Fast Screening ◽

Organic Photovoltaic Materials ◽

High Prediction ◽

The Relationship

In the process of finding high-performance materials for organic photovoltaics (OPVs), it is meaningful if one can establish the relationship between chemical structures and photovoltaic properties even before synthesizing them. Here, we first establish a database containing over 1700 donor materials reported in the literature. Through supervised learning, our machine learning (ML) models can build up the structure-property relationship and, thus, implement fast screening of OPV materials. We explore several expressions for molecule structures, i.e., images, ASCII strings, descriptors, and fingerprints, as inputs for various ML algorithms. It is found that fingerprints with length over 1000 bits can obtain high prediction accuracy. The reliability of our approach is further verified by screening 10 newly designed donor materials. Good consistency between model predictions and experimental outcomes is obtained. The result indicates that ML is a powerful tool to prescreen new OPV materials, thus accelerating the development of the OPV field.

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Interpretable machine-learning strategy for soft-magnetic property and thermal stability in Fe-based metallic glasses

npj Computational Materials ◽

10.1038/s41524-020-00460-x ◽

2020 ◽

Vol 6 (1) ◽

Author(s):

Zhichao Lu ◽

Xin Chen ◽

Xiongjun Liu ◽

Deye Lin ◽

Yuan Wu ◽

...

Keyword(s):

Machine Learning ◽

Thermal Stability ◽

Magnetic Property ◽

Metallic Glasses ◽

High Performance ◽

Learning Strategy ◽

Soft Magnetic Property ◽

Gradient Boosting ◽

Soft Magnetic ◽

Extreme Gradient Boosting

AbstractFe-based metallic glasses (MGs) have been extensively investigated due to their unique properties, especially the outstanding soft-magnetic properties. However, conventional design of soft-magnetic Fe-based MGs is heavily relied on “trial and error” experiments, and thus difficult to balance the saturation flux density (Bs) and thermal stability due to the strong interplay between the glass formation and magnetic interaction. Herein, we report an eXtreme Gradient Boosting (XGBoost) machine-learning (ML) model for developing advanced Fe-based MGs with a decent combination of Bs and thermal stability. While it is an attempt to apply ML for exploring soft-magnetic property and thermal stability, the developed XGBoost model based on the intrinsic elemental properties (i.e., atomic size and electronegativity) can well predict Bs and Tx (the onset crystallization temperature) with an accuracy of 93.0% and 94.3%, respectively. More importantly, we derived the key features that primarily dictate Bs and Tx of Fe-based MGs from the ML model, which enables the revelation of the physical origins underlying the high Bs and thermal stability. As a proof of concept, several Fe-based MGs with high Tx (>800 K) and high Bs (>1.4 T) were successfully developed in terms of the ML model. This work demonstrates that the XGBoost ML approach is interpretable and feasible in the extraction of decisive parameters for properties of Fe-based magnetic MGs, which might allow us to efficiently design high-performance glassy materials.

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Skeletal elements of crinoid echinoderms: High-resolution structural and microanalytical results

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100074768 ◽

1983 ◽

Vol 41 ◽

pp. 194-195

Author(s):

D. F. Blake ◽

L. F. Allard ◽

D. R. Peacor

Keyword(s):

High Resolution ◽

Marine Invertebrates ◽

Sea Urchins ◽

Structural Features ◽

Sea Stars ◽

High Resolution Tem ◽

Relationship Of ◽

The Relationship ◽

Insight Into

Echinodermata is a phylum of marine invertebrates which has been extant since Cambrian time (c.a. 500 m.y. before the present). Modern examples of echinoderms include sea urchins, sea stars, and sea lilies (crinoids). The endoskeletons of echinoderms are composed of plates or ossicles (Fig. 1) which are with few exceptions, porous, single crystals of high-magnesian calcite. Despite their single crystal nature, fracture surfaces do not exhibit the near-perfect {10.4} cleavage characteristic of inorganic calcite. This paradoxical mix of biogenic and inorganic features has prompted much recent work on echinoderm skeletal crystallography. Furthermore, fossil echinoderm hard parts comprise a volumetrically significant portion of some marine limestones sequences. The ultrastructural and microchemical characterization of modern skeletal material should lend insight into: 1). The nature of the biogenic processes involved, for example, the relationship of Mg heterogeneity to morphological and structural features in modern echinoderm material, and 2). The nature of the diagenetic changes undergone by their ancient, fossilized counterparts. In this study, high resolution TEM (HRTEM), high voltage TEM (HVTEM), and STEM microanalysis are used to characterize tha ultrastructural and microchemical composition of skeletal elements of the modern crinoid Neocrinus blakei.

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