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Machine learning for molecular simulations of crystal nucleation and growth

Journal Article · · MRS Bulletin
; ;

Not provided.

Research Organization:
Clemson Univ., SC (United States)
Sponsoring Organization:
USDOE Office of Science (SC)
DOE Contract Number:
SC0015448
OSTI ID:
1981009
Journal Information:
MRS Bulletin, Vol. 47, Issue 9; ISSN 0883-7694
Publisher:
Materials Research Society
Country of Publication:
United States
Language:
English

References (31)

Structural characterization of ice XIX as the second polymorph related to ice VI February 2021
Polymorphism as an emerging design strategy for high performance organic electronics January 2016
Machine Learning for Molecular Simulation April 2020
Neural networks for local structure detection in polymorphic systems October 2013
GCIceNet: a graph convolutional network for accurate classification of water phases January 2020
DeepIce: A Deep Neural Network Approach To Identify Ice and Water Molecules March 2019
Unsupervised learning for local structure detection in colloidal systems October 2019
Analysis of Self-Assembly Pathways with Unsupervised Machine Learning Algorithms December 2019
Unsupervised learning of atomic environments from simple features August 2021
Deep learning for characterizing the self-assembly of three-dimensional colloidal systems January 2021
Automated crystal characterization with a fast neighborhood graph analysis method January 2018
Descriptor-free unsupervised learning method for local structure identification in particle packings April 2022
Data-centric framework for crystal structure identification in atomistic simulations using machine learning April 2022
An Artificial Neural Network Reveals the Nucleation Mechanism of a Binary Colloidal AB 13 Crystal February 2021
Automatic Method for Identifying Reaction Coordinates in Complex Systems † April 2005
An extended autoencoder model for reaction coordinate discovery in rare event molecular dynamics datasets August 2021
Automated design of collective variables using supervised machine learning September 2018
Collective Variables from Local Fluctuations May 2018
Collective variables for the study of crystallisation March 2021
Rethinking Metadynamics: From Bias Potentials to Probability Distributions March 2020
Spectral gap optimization of order parameters for sampling complex molecular systems February 2016
Toward Automated Sampling of Polymorph Nucleation and Free Energies with the SGOOP and Metadynamics November 2021
Competing nucleation pathways in a mixture of oppositely charged colloids: Out-of-equilibrium nucleation revisited December 2009
Identification of a multi-dimensional reaction coordinate for crystal nucleation in Ni 3 Al June 2020
Nucleation mechanism of clathrate hydrates of water-soluble guest molecules November 2017
Molecular enhanced sampling with autoencoders: On-the-fly collective variable discovery and accelerated free energy landscape exploration: Molecular Enhanced Sampling with Autoencoders: On-The-Fly Collective Variable Discovery and Accelerated Free Energy Landscape Exploration September 2018
Chasing Collective Variables Using Autoencoders and Biased Trajectories December 2021
Reweighted autoencoded variational Bayes for enhanced sampling (RAVE) August 2018
Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics June 2018
Past–future information bottleneck for sampling molecular reaction coordinate simultaneously with thermodynamics and kinetics August 2019
Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning September 2019

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