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.

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...

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.

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