Machine Learning and Polymer Self-Consistent Field Theory in Two Spatial Dimensions

• URL: http://arxiv.org/abs/2212.10478v2
• Date: Mon, 3 Jul 2023 05:23:38 GMT
• Title: Machine Learning and Polymer Self-Consistent Field Theory in Two Spatial Dimensions
• Authors: Yao Xuan, Kris T. Delaney, Hector D. Ceniceros, Glenn H. Fredrickson
• Abstract summary: A computational framework that leverages data from self-consistent field theory simulations with deep learning is presented. A generative adversarial network (GAN) is introduced to efficiently and accurately predict saddle point, local average monomer density fields. This GAN approach yields important savings of both memory and computational cost.
• Score: 0.491574468325115
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A computational framework that leverages data from self-consistent field theory simulations with deep learning to accelerate the exploration of parameter space for block copolymers is presented. This is a substantial two-dimensional extension of the framework introduced in [1]. Several innovations and improvements are proposed. (1) A Sobolev space-trained, convolutional neural network (CNN) is employed to handle the exponential dimension increase of the discretized, local average monomer density fields and to strongly enforce both spatial translation and rotation invariance of the predicted, field-theoretic intensive Hamiltonian. (2) A generative adversarial network (GAN) is introduced to efficiently and accurately predict saddle point, local average monomer density fields without resorting to gradient descent methods that employ the training set. This GAN approach yields important savings of both memory and computational cost. (3) The proposed machine learning framework is successfully applied to 2D cell size optimization as a clear illustration of its broad potential to accelerate the exploration of parameter space for discovering polymer nanostructures. Extensions to three-dimensional phase discovery appear to be feasible.

Related papers

• Efficient and generalizable nested Fourier-DeepONet for three-dimensional geological carbon sequestration [5.77922305904338]
  Surrogate modeling with data-driven machine learning has become a promising alternative to accelerate physics-based simulations. We have developed a nested Fourier-DeepONet by combining the expressiveness of the FNO with the modularity of a deep operator network (DeepONet) This new framework is twice as efficient as a nested FNO for training and has at least 80% lower GPU memory requirement.
  arXiv  Detail & Related papers  (2024-09-25T02:58:45Z)
• Learning local equivariant representations for quantum operators [7.747597014044332]
  We introduce a novel deep learning model, SLEM, for predicting multiple quantum operators. SLEM achieves state-of-the-art accuracy while dramatically improving computational efficiency. We demonstrate SLEM's capabilities across diverse 2D and 3D materials, achieving high accuracy even with limited training data.
  arXiv  Detail & Related papers  (2024-07-08T15:55:12Z)
• Enhancing lattice kinetic schemes for fluid dynamics with Lattice-Equivariant Neural Networks [79.16635054977068]
  We present a new class of equivariant neural networks, dubbed Lattice-Equivariant Neural Networks (LENNs) Our approach develops within a recently introduced framework aimed at learning neural network-based surrogate models Lattice Boltzmann collision operators. Our work opens towards practical utilization of machine learning-augmented Lattice Boltzmann CFD in real-world simulations.
  arXiv  Detail & Related papers  (2024-05-22T17:23:15Z)
• Double Duality: Variational Primal-Dual Policy Optimization for Constrained Reinforcement Learning [132.7040981721302]
  We study the Constrained Convex Decision Process (MDP), where the goal is to minimize a convex functional of the visitation measure. Design algorithms for a constrained convex MDP faces several challenges, including handling the large state space.
  arXiv  Detail & Related papers  (2024-02-16T16:35:18Z)
• Dynamic Kernel-Based Adaptive Spatial Aggregation for Learned Image Compression [63.56922682378755]
  We focus on extending spatial aggregation capability and propose a dynamic kernel-based transform coding. The proposed adaptive aggregation generates kernel offsets to capture valid information in the content-conditioned range to help transform. Experimental results demonstrate that our method achieves superior rate-distortion performance on three benchmarks compared to the state-of-the-art learning-based methods.
  arXiv  Detail & Related papers  (2023-08-17T01:34:51Z)
• Bivariate DeepKriging for Large-scale Spatial Interpolation of Wind Fields [2.586710925821896]
  High spatial resolution wind data are essential for a wide range of applications in climate, oceanographic and meteorological studies. Large-scale spatial computation or downscaling of bivariate wind fields having velocity in two dimensions is a challenging task. In this paper, we propose a method, called bivariate DeepKriging, which is a spatially dependent deep neural network (DNN) with an embedding layer constructed by spatial radial basis functions. We demonstrate the computational efficiency and scalability of the proposed DNN model, with computations that are, on average, 20 times faster than those of conventional techniques.
  arXiv  Detail & Related papers  (2023-07-16T13:34:44Z)
• Fast emulation of cosmological density fields based on dimensionality reduction and supervised machine-learning [0.0]
  We show that it is possible to perform fast dark matter density field emulations with competitive accuracy using simple machine-learning approaches. New density cubes for different cosmological parameters can be estimated without relying directly on new N-body simulations.
  arXiv  Detail & Related papers  (2023-04-12T18:29:26Z)
• Optimal Scaling for Locally Balanced Proposals in Discrete Spaces [65.14092237705476]
  We show that efficiency of Metropolis-Hastings (M-H) algorithms in discrete spaces can be characterized by an acceptance rate that is independent of the target distribution. Knowledge of the optimal acceptance rate allows one to automatically tune the neighborhood size of a proposal distribution in a discrete space, directly analogous to step-size control in continuous spaces.
  arXiv  Detail & Related papers  (2022-09-16T22:09:53Z)
• Tensor-based Multi-view Spectral Clustering via Shared Latent Space [14.470859959783995]
  Multi-view Spectral Clustering (MvSC) attracts increasing attention due to diverse data sources. New method for MvSC is proposed via a shared latent space from the Restricted Kernel Machine framework.
  arXiv  Detail & Related papers  (2022-07-23T17:30:54Z)
• Information Entropy Initialized Concrete Autoencoder for Optimal Sensor Placement and Reconstruction of Geophysical Fields [58.720142291102135]
  We propose a new approach to the optimal placement of sensors for reconstructing geophysical fields from sparse measurements. We demonstrate our method on the two examples: (a) temperature and (b) salinity fields around the Barents Sea and the Svalbard group of islands. We find out that the obtained optimal sensor locations have clear physical interpretation and correspond to the boundaries between sea currents.
  arXiv  Detail & Related papers  (2022-06-28T12:43:38Z)
• GeoMol: Torsional Geometric Generation of Molecular 3D Conformer Ensembles [60.12186997181117]
  Prediction of a molecule's 3D conformer ensemble from the molecular graph holds a key role in areas of cheminformatics and drug discovery. Existing generative models have several drawbacks including lack of modeling important molecular geometry elements. We propose GeoMol, an end-to-end, non-autoregressive and SE(3)-invariant machine learning approach to generate 3D conformers.
  arXiv  Detail & Related papers  (2021-06-08T14:17:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.