Visualizing the Local Atomic Environment Features of Machine Learning Interatomic Potential

• URL: http://arxiv.org/abs/2501.16398v1
• Date: Mon, 27 Jan 2025 04:08:37 GMT
• Title: Visualizing the Local Atomic Environment Features of Machine Learning Interatomic Potential
• Authors: Xuqiang Shao, Yuqi Zhang, Di Zhang, Tianxiang Gao, Xinyuan Liu, Zhiran Gan, Fanshun Meng, Hao Li, Weijie Yang,
• Abstract summary: This paper addresses the challenges of creating efficient and high-quality datasets for machine learning potential functions.<n>We present a novel approach, termed DV-LAE (Difference Vectors based on Local Atomic Environments), which employs histogram statistics to generate difference vectors.<n>We have validated the optimized datasets in high-temperature and high-pressure hydrogen systems.
• Score: 14.011925165212244
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper addresses the challenges of creating efficient and high-quality datasets for machine learning potential functions. We present a novel approach, termed DV-LAE (Difference Vectors based on Local Atomic Environments), which utilizes the properties of atomic local environments and employs histogram statistics to generate difference vectors. This technique facilitates dataset screening and optimization, effectively minimizing redundancy while maintaining data diversity. We have validated the optimized datasets in high-temperature and high-pressure hydrogen systems as well as the {\alpha}-Fe/H binary system, demonstrating a significant reduction in computational resource usage without compromising prediction accuracy. Additionally, our method has revealed new structures that emerge during simulations but were underrepresented in the initial training datasets. The redundancy in the datasets and the distribution of these new structures can be visually analyzed through the visualization of difference vectors. This approach enhances our understanding of the characteristics of these newly formed structures and their impact on physical processes.

Related papers

• Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout [62.73150122809138]
  Federated Learning (FL) is a promising distributed machine learning approach that enables collaborative training of a global model using multiple edge devices.<n>We propose the FedDHAD FL framework, which comes with two novel methods: Dynamic Heterogeneous model aggregation (FedDH) and Adaptive Dropout (FedAD)<n>The combination of these two methods makes FedDHAD significantly outperform state-of-the-art solutions in terms of accuracy (up to 6.7% higher), efficiency (up to 2.02 times faster), and cost (up to 15.0% smaller)
  arXiv  Detail & Related papers  (2025-07-14T16:19:00Z)
• A Deep Bayesian Nonparametric Framework for Robust Mutual Information Estimation [9.68824512279232]
  Mutual Information (MI) is a crucial measure for capturing dependencies between variables.<n>We present a solution for training an MI estimator by constructing the MI loss with a finite representation of the Dirichlet process posterior to incorporate regularization.<n>We explore the application of our estimator in maximizing MI between the data space and the latent space of a variational autoencoder.
  arXiv  Detail & Related papers  (2025-03-11T21:27:48Z)
• Leveraging Large Language Models to Address Data Scarcity in Machine Learning: Applications in Graphene Synthesis [0.0]
  Machine learning in materials science faces challenges due to limited experimental data.<n>We propose strategies that utilize large language models (LLMs) to enhance machine learning performance.
  arXiv  Detail & Related papers  (2025-03-06T16:04:01Z)
• Towards Data-Efficient Pretraining for Atomic Property Prediction [51.660835328611626]
  We show that pretraining on a task-relevant dataset can match or surpass large-scale pretraining.<n>We introduce the Chemical Similarity Index (CSI), a novel metric inspired by computer vision's Fr'echet Inception Distance.
  arXiv  Detail & Related papers  (2025-02-16T11:46:23Z)
• Learnable Sparse Customization in Heterogeneous Edge Computing [27.201987866208484]
  We propose Learnable Personalized Sparsification for heterogeneous Federated learning (FedLPS)<n>FedLPS learns the importance of model units on local data representation and derives an importance-based sparse pattern with minimals to accurately extract personalized data features.<n>Experiments show that FedLPS outperforms status quo approaches in accuracy and training costs.
  arXiv  Detail & Related papers  (2024-12-10T06:14:31Z)
• Topology-aware Reinforcement Feature Space Reconstruction for Graph Data [22.5530178427691]
  Reconstructing a good feature space is essential to augment the AI power of data, improve model generalization, and increase the availability of downstream ML models. We use topology-aware reinforcement learning to automate and optimize feature space reconstruction for graph data. Our approach combines the extraction of core subgraphs to capture essential structural information with a graph neural network (GNN) to encode topological features and reduce computing complexity.
  arXiv  Detail & Related papers  (2024-11-08T18:01:05Z)
• Task-Oriented Real-time Visual Inference for IoVT Systems: A Co-design Framework of Neural Networks and Edge Deployment [61.20689382879937]
  Task-oriented edge computing addresses this by shifting data analysis to the edge. Existing methods struggle to balance high model performance with low resource consumption. We propose a novel co-design framework to optimize neural network architecture.
  arXiv  Detail & Related papers  (2024-10-29T19:02:54Z)
• An Investigation on Machine Learning Predictive Accuracy Improvement and Uncertainty Reduction using VAE-based Data Augmentation [2.517043342442487]
  Deep generative learning uses certain ML models to learn the underlying distribution of existing data and generate synthetic samples that resemble the real data. In this study, our objective is to evaluate the effectiveness of data augmentation using variational autoencoder (VAE)-based deep generative models. We investigated whether the data augmentation leads to improved accuracy in the predictions of a deep neural network (DNN) model trained using the augmented data.
  arXiv  Detail & Related papers  (2024-10-24T18:15:48Z)
• Probabilistic transfer learning methodology to expedite high fidelity simulation of reactive flows [0.3495246564946556]
  This study presents a novel probabilistic transfer learning (TL) framework to enhance the trust in Machine Learning (ML) models. The framework uses Bayesian neural networks, and autoencoders, to reduce the dimensionality of the state space. The results reveal that there is an optimal amount of knowledge to be transferred, which depends on the amount of data available in the target domain.
  arXiv  Detail & Related papers  (2024-05-17T17:58:52Z)
• Minimally Supervised Learning using Topological Projections in Self-Organizing Maps [55.31182147885694]
  We introduce a semi-supervised learning approach based on topological projections in self-organizing maps (SOMs) Our proposed method first trains SOMs on unlabeled data and then a minimal number of available labeled data points are assigned to key best matching units (BMU) Our results indicate that the proposed minimally supervised model significantly outperforms traditional regression techniques.
  arXiv  Detail & Related papers  (2024-01-12T22:51:48Z)
• Closing the loop: Autonomous experiments enabled by machine-learning-based online data analysis in synchrotron beamline environments [80.49514665620008]
  Machine learning can be used to enhance research involving large or rapidly generated datasets. In this study, we describe the incorporation of ML into a closed-loop workflow for X-ray reflectometry (XRR) We present solutions that provide an elementary data analysis in real time during the experiment without introducing the additional software dependencies in the beamline control software environment.
  arXiv  Detail & Related papers  (2023-06-20T21:21:19Z)
• Transfer learning for atomistic simulations using GNNs and kernel mean embeddings [24.560340485988128]
  We propose a transfer learning algorithm that leverages the ability of graph neural networks (GNNs) to represent chemical environments together with kernel mean embeddings. We test our approach on a series of realistic datasets of increasing complexity, showing excellent generalization and transferability performance.
  arXiv  Detail & Related papers  (2023-06-02T14:58:16Z)
• Joint Feature and Differentiable $ k $-NN Graph Learning using Dirichlet Energy [103.74640329539389]
  We propose a deep FS method that simultaneously conducts feature selection and differentiable $ k $-NN graph learning. We employ Optimal Transport theory to address the non-differentiability issue of learning $ k $-NN graphs in neural networks. We validate the effectiveness of our model with extensive experiments on both synthetic and real-world datasets.
  arXiv  Detail & Related papers  (2023-05-21T08:15:55Z)
• Enhancing Performance of Vision Transformers on Small Datasets through Local Inductive Bias Incorporation [13.056764072568749]
  Vision transformers (ViTs) achieve remarkable performance on large datasets, but tend to perform worse than convolutional neural networks (CNNs) on smaller datasets. We propose a module called Local InFormation Enhancer (LIFE) that extracts patch-level local information and incorporates it into the embeddings used in the self-attention block of ViTs. Our proposed module is memory and efficient, as well as flexible enough to process auxiliary tokens such as the classification and distillation tokens.
  arXiv  Detail & Related papers  (2023-05-15T11:23:18Z)
• Distributed Neural Representation for Reactive in situ Visualization [23.80657290203846]
  Implicit neural representations (INRs) have emerged as a powerful tool for compressing large-scale volume data. We develop a distributed neural representation and optimize it for in situ visualization. Our technique eliminates data exchanges between processes, achieving state-of-the-art compression speed, quality and ratios.
  arXiv  Detail & Related papers  (2023-03-28T03:55:47Z)
• Pre-training via Denoising for Molecular Property Prediction [53.409242538744444]
  We describe a pre-training technique that utilizes large datasets of 3D molecular structures at equilibrium. Inspired by recent advances in noise regularization, our pre-training objective is based on denoising.
  arXiv  Detail & Related papers  (2022-05-31T22:28:34Z)
• Inducing Gaussian Process Networks [80.40892394020797]
  We propose inducing Gaussian process networks (IGN), a simple framework for simultaneously learning the feature space as well as the inducing points. The inducing points, in particular, are learned directly in the feature space, enabling a seamless representation of complex structured domains. We report on experimental results for real-world data sets showing that IGNs provide significant advances over state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-04-21T05:27:09Z)
• Convolutional generative adversarial imputation networks for spatio-temporal missing data in storm surge simulations [86.5302150777089]
  Generative Adversarial Imputation Nets (GANs) and GAN-based techniques have attracted attention as unsupervised machine learning methods. We name our proposed method as Con Conval Generative Adversarial Imputation Nets (Conv-GAIN)
  arXiv  Detail & Related papers  (2021-11-03T03:50:48Z)
• An Empirical Evaluation of the t-SNE Algorithm for Data Visualization in Structural Engineering [2.4493299476776773]
  t-Distributed Neighbor Embedding (t-SNE) algorithm is used to reduce the dimensions of an earthquake related data set for visualization purposes. Synthetic Minority Oversampling Technique (SMOTE) is used to tackle the imbalanced nature of such data set. We show that using t-SNE on the imbalanced data and SMOTE on the training data set, neural network classifiers have promising results without sacrificing accuracy.
  arXiv  Detail & Related papers  (2021-09-18T01:24:39Z)
• Rank-R FNN: A Tensor-Based Learning Model for High-Order Data Classification [69.26747803963907]
  Rank-R Feedforward Neural Network (FNN) is a tensor-based nonlinear learning model that imposes Canonical/Polyadic decomposition on its parameters. First, it handles inputs as multilinear arrays, bypassing the need for vectorization, and can thus fully exploit the structural information along every data dimension. We establish the universal approximation and learnability properties of Rank-R FNN, and we validate its performance on real-world hyperspectral datasets.
  arXiv  Detail & Related papers  (2021-04-11T16:37:32Z)
• Graph Embedding with Data Uncertainty [113.39838145450007]
  spectral-based subspace learning is a common data preprocessing step in many machine learning pipelines. Most subspace learning methods do not take into consideration possible measurement inaccuracies or artifacts that can lead to data with high uncertainty.
  arXiv  Detail & Related papers  (2020-09-01T15:08:23Z)
• Diversity inducing Information Bottleneck in Model Ensembles [73.80615604822435]
  In this paper, we target the problem of generating effective ensembles of neural networks by encouraging diversity in prediction. We explicitly optimize a diversity inducing adversarial loss for learning latent variables and thereby obtain diversity in the output predictions necessary for modeling multi-modal data. Compared to the most competitive baselines, we show significant improvements in classification accuracy, under a shift in the data distribution.
  arXiv  Detail & Related papers  (2020-03-10T03:10:41Z)
• New advances in enumerative biclustering algorithms with online partitioning [80.22629846165306]
  This paper further extends RIn-Close_CVC, a biclustering algorithm capable of performing an efficient, complete, correct and non-redundant enumeration of maximal biclusters with constant values on columns in numerical datasets. The improved algorithm is called RIn-Close_CVC3, keeps those attractive properties of RIn-Close_CVC, and is characterized by: a drastic reduction in memory usage; a consistent gain in runtime.
  arXiv  Detail & Related papers  (2020-03-07T14:54:26Z)

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.