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Learning to grow: control of material self-assembly using evolutionary
  reinforcement learning
v1v2v3 (latest)

Learning to grow: control of material self-assembly using evolutionary reinforcement learning

18 December 2019
S. Whitelam
Isaac Tamblyn
ArXiv (abs)PDFHTML

Papers citing "Learning to grow: control of material self-assembly using evolutionary reinforcement learning"

4 / 4 papers shown
Title
Generative methods for sampling transition paths in molecular dynamics
Generative methods for sampling transition paths in molecular dynamics
T. Lelièvre
Geneviève Robin
Inass Sekkat
G. Stoltz
Gabriel Victorino Cardoso
GAN
46
9
0
05 May 2022
Functional Nanomaterials Design in the Workflow of Building
  Machine-Learning Models
Functional Nanomaterials Design in the Workflow of Building Machine-Learning Models
Zhexu Xi
AI4CE
28
0
0
16 Aug 2021
Accelerating GMRES with Deep Learning in Real-Time
Accelerating GMRES with Deep Learning in Real-Time
Kevin Luna
Katherine Klymko
Johannes P. Blaschke
AI4CE
81
13
0
19 Mar 2021
Correspondence between neuroevolution and gradient descent
Correspondence between neuroevolution and gradient descent
S. Whitelam
V. Selin
Sang-Won Park
Isaac Tamblyn
43
20
0
15 Aug 2020
1