scholarly journals Unsupervised learning of sequence-specific aggregation behavior for a model copolymer

Soft Matter ◽  
2021 ◽  
Author(s):  
Antonia Statt ◽  
Devon C Kleeblatt ◽  
Wesley F. Reinhart
Keyword(s):  
Machine Learning ◽  
Unsupervised Learning ◽  
Large Scale ◽  
Materials Science ◽  
Aggregation Behavior ◽  
Unsupervised Machine Learning ◽  
Computational Materials Science ◽  
Learning Scheme ◽  
Local Environments ◽  
Computational Materials

We apply a recently developed unsupervised machine learning scheme for local environments [Reinhart, Computational Materials Science, 2021, 196, 110511] to characterize large-scale, disordered aggregates formed by sequence-defined macromolecules. This method...

Nanoinformatics, and the big challenges for the science of small things

Nanoscale ◽  
2019 ◽  
Vol 11 (41) ◽  
pp. 19190-19201 ◽  
Author(s):  
A. S. Barnard ◽  
B. Motevalli ◽  
A. J. Parker ◽  
J. M. Fischer ◽  
C. A. Feigl ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Computational Chemistry ◽  
Functional Properties ◽  
Materials Science ◽  
Structural Features ◽  
Computational Materials Science ◽  
Computational Materials ◽  
Small Things

The combination of computational chemistry and computational materials science with machine learning and artificial intelligence provides a powerful way of relating structural features of nanomaterials with functional properties.

Evolution of Metastable Structures in Bimetallic Catalysts from Microscopy and Machine-Learning Molecular Dynamics

2020 ◽  
Author(s):  
Jin Soo Lim ◽  
Jonathan Vandermause ◽  
Matthijs A. van Spronsen ◽  
Albert Musaelian ◽  
Christopher R. O’Connor ◽  
...  
Keyword(s):  
Machine Learning ◽  
Molecular Dynamics ◽  
Large Scale ◽  
Materials Science ◽  
Complete Characterization ◽  
Layer By Layer ◽  
Surface Restructuring ◽  
Metastable Structures ◽  
Mechanistic Investigation ◽  
Underlying Mechanisms

Restructuring of interface plays a crucial role in materials science and heterogeneous catalysis. Bimetallic systems, in particular, often adopt very different composition and morphology at surfaces compared to the bulk. For the first time, we reveal a detailed atomistic picture of the long-timescale restructuring of Pd deposited on Ag, using microscopy, spectroscopy, and novel simulation methods. Encapsulation of Pd by Ag always precedes layer-by-layer dissolution of Pd, resulting in significant Ag migration out of the surface and extensive vacancy pits. These metastable structures are of vital catalytic importance, as Ag-encapsulated Pd remains much more accessible to reactants than bulk-dissolved Pd. The underlying mechanisms are uncovered by performing fast and large-scale machine-learning molecular dynamics, followed by our newly developed method for complete characterization of atomic surface restructuring events. Our approach is broadly applicable to other multimetallic systems of interest and enables the previously impractical mechanistic investigation of restructuring dynamics.

Mentoring Undergraduate Students in Computational Materials Research

2015 ◽  
Vol 1762 ◽  
Author(s):  
Jie Zou
Keyword(s):  
Undergraduate Students ◽  
Experimental Research ◽  
Materials Science ◽  
Computational Materials Science ◽  
Undergraduate Institutions ◽  
Materials Research ◽  
Computational Research ◽  
Computational Materials ◽  
Eastern Illinois University ◽  
Physics Department

ABSTRACTComputation has become an increasingly important tool in materials science. Compared to experimental research, which requires facilities that are often beyond the financial capability of primarily-undergraduate institutions, computation provides a more affordable approach. In the Physics Department at Eastern Illinois University (EIU), students have opportunities to participate in computational materials research. In this paper, I will discuss our approach to involving undergraduate students in this area. Specifically, I will discuss (i) how to prepare undergraduate students for computational research, (ii) how to motivate and recruit students to participate in computational research, and (iii) how to select and design undergraduate projects in computational materials science. Suggestions on how similar approaches can be implemented at other institutions are also given.

scholarly journals A Deep and Scalable Unsupervised Machine Learning System for Cyber-Attack Detection in Large-Scale Smart Grids

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 80778-80788 ◽  
Author(s):  
Hadis Karimipour ◽  
Ali Dehghantanha ◽  
Reza M. Parizi ◽  
Kim-Kwang Raymond Choo ◽  
Henry Leung
Keyword(s):  
Machine Learning ◽  
Smart Grids ◽  
Large Scale ◽  
Attack Detection ◽  
Learning System ◽  
Cyber Attack ◽  
Unsupervised Machine Learning
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