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The Journal of Chemical Physics
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September 22, 2022
Derivable genetic programming for two-dimensional colloidal materials
Nathan A Mahynski, Bliss Han, Daniel Markiewitz, et al.
Nature Communications
|
May 4, 2019
Using symmetry to elucidate the importance of stoichiometry in colloidal crystal assembly
Nathan A Mahynski, Evan Pretti, Vincent K Shen, et al.
G.I.T. Laboratory Journal Europe
|
May 14, 2019
Void-Based Assembly of Colloidal Crystals: Using Structure-Directing Agents to Direct the Assembly of Open Colloidal Crystals
Nathan A Mahynski, Lorenzo Rovigatti, Christos N Likos, et al.
Food Chemistry
|
May 9, 2025
Comparing machine learning models to chemometric ones to detect food fraud: A case study in Slovenian fruits and vegetables
Nathan A Mahynski, Lidija Strojnik, Vincent K Shen, et al.
The Journal of Physical Chemistry. A
|
March 17, 2020
Symmetry-Based Crystal Structure Enumeration in Two Dimensions
Evan Pretti, Vincent K Shen, Jeetain Mittal, et al.
ACS Nano
|
April 29, 2016
Bottom-Up Colloidal Crystal Assembly with a Twist
Nathan A Mahynski, Lorenzo Rovigatti, Christos N Likos, et al.
The Journal of Chemical Physics
|
March 11, 2014
Flow-induced demixing of polymer-colloid mixtures in microfluidic channels
Arash Nikoubashman, Nathan A Mahynski, Amir H Pirayandeh, et al.
The Journal of Chemical Physics
|
February 24, 2017
Predicting low-temperature free energy landscapes with flat-histogram Monte Carlo methods
Nathan A Mahynski, Marco A Blanco, Jeffrey R Errington, et al.
Soft Matter
|
March 6, 2020
Grand canonical inverse design of multicomponent colloidal crystals
Nathan A Mahynski, Runfang Mao, Evan Pretti, et al.
Environmental Science & Technology
|
October 5, 2022
Building Interpretable Machine Learning Models to Identify Chemometric Trends in Seabirds of the North Pacific Ocean
Nathan A Mahynski, Jared M Ragland, Stacy S Schuur, et al.
Page
of 5
Search research articles
Search
Showing results (21-30 of 43) with videos related to
Sort By:
Page
of 5
The Journal of Chemical Physics
|
September 22, 2022
Derivable genetic programming for two-dimensional colloidal materials
Nathan A Mahynski, Bliss Han, Daniel Markiewitz, et al.
Nature Communications
|
May 4, 2019
Using symmetry to elucidate the importance of stoichiometry in colloidal crystal assembly
Nathan A Mahynski, Evan Pretti, Vincent K Shen, et al.
G.I.T. Laboratory Journal Europe
|
May 14, 2019
Void-Based Assembly of Colloidal Crystals: Using Structure-Directing Agents to Direct the Assembly of Open Colloidal Crystals
Nathan A Mahynski, Lorenzo Rovigatti, Christos N Likos, et al.
Food Chemistry
|
May 9, 2025
Comparing machine learning models to chemometric ones to detect food fraud: A case study in Slovenian fruits and vegetables
Nathan A Mahynski, Lidija Strojnik, Vincent K Shen, et al.
The Journal of Physical Chemistry. A
|
March 17, 2020
Symmetry-Based Crystal Structure Enumeration in Two Dimensions
Evan Pretti, Vincent K Shen, Jeetain Mittal, et al.
ACS Nano
|
April 29, 2016
Bottom-Up Colloidal Crystal Assembly with a Twist
Nathan A Mahynski, Lorenzo Rovigatti, Christos N Likos, et al.
The Journal of Chemical Physics
|
March 11, 2014
Flow-induced demixing of polymer-colloid mixtures in microfluidic channels
Arash Nikoubashman, Nathan A Mahynski, Amir H Pirayandeh, et al.
The Journal of Chemical Physics
|
February 24, 2017
Predicting low-temperature free energy landscapes with flat-histogram Monte Carlo methods
Nathan A Mahynski, Marco A Blanco, Jeffrey R Errington, et al.
Soft Matter
|
March 6, 2020
Grand canonical inverse design of multicomponent colloidal crystals
Nathan A Mahynski, Runfang Mao, Evan Pretti, et al.
Environmental Science & Technology
|
October 5, 2022
Building Interpretable Machine Learning Models to Identify Chemometric Trends in Seabirds of the North Pacific Ocean
Nathan A Mahynski, Jared M Ragland, Stacy S Schuur, et al.
Page
of 5