Machine Learning Assisted Parameter Tuning on Wavelet Transform Amorphous Radial Distribution Function

• URL: http://arxiv.org/abs/2512.17245v1
• Date: Fri, 19 Dec 2025 05:22:54 GMT
• Title: Machine Learning Assisted Parameter Tuning on Wavelet Transform Amorphous Radial Distribution Function
• Abstract summary: WT-RDF offers a physics-based framework for analyzing amorphous structures.<n> WT-RDF shows limitations in amplitude accuracy, which affects quantitative analyses such as coordination numbers.<n>This study addresses the issue by optimizing WT-RDF parameters using a machine learning approach, producing the enhanced WT-RDF+ framework.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Understanding atomic structures is crucial, yet amorphous materials remain challenging due to their irregular and non-periodic nature. The wavelet-transform radial distribution function (WT-RDF) offers a physics-based framework for analyzing amorphous structures, reliably predicting the first and second RDF peaks and overall curve trends in both binary Ge 0.25 Se 0.75 and ternary Ag x(Ge 0.25 Se 0.75)100-x (x=5,10,15,20,25) systems. Despite these strengths, WT-RDF shows limitations in amplitude accuracy, which affects quantitative analyses such as coordination numbers. This study addresses the issue by optimizing WT-RDF parameters using a machine learning approach, producing the enhanced WT-RDF+ framework. WT-RDF+ improves the precision of peak predictions and outperforms benchmark ML models, including RBF and LSTM, even when trained on only 25 percent of the binary dataset. These results demonstrate that WT-RDF+ is a robust and reliable model for structural characterization of amorphous materials, particularly Ge-Se systems, and support the efficient design and development of phase-change thin films for next-generation electronic devices and components.

Related papers

• Transformer-based Multisensor Data Fusion of Ultrasonic Guided Wave and FBG-based Strain Measurements for Multitask Aerospace Structural Health Monitoring [5.884291793763452]
  This paper proposes a Transformer-based data fusion framework that integrates multisensor data streams from piezoelectric transducer (PZT)<n>By an attention-mechanism visualization, the proposed framework enables transparent, multitask learning for both health indicator (HI) prediction and damage localization.
  arXiv  Detail & Related papers  (2026-06-24T13:47:27Z)
• Beyond Quadratic: Linear-Time Change Detection with RWKV [48.3313296326424]
  ChangeRWKV is a new architecture for remote sensing change detection.<n>It combines the parallelizable training of Transformers with the linear-time inference of RNNs.<n>It achieves state-of-the-art performance on the LEVIR-CD benchmark, with an 85.46% IoU and 92.16% F1 score.
  arXiv  Detail & Related papers  (2026-03-20T03:23:02Z)
• Machine Learning-Driven Crystal System Prediction for Perovskites Using Augmented X-ray Diffraction Data [0.10262304700896197]
  Prediction of crystal system from X-ray diffraction (XRD) spectra is a critical task in materials science.<n>In this study, we present a machine learning (ML)-driven framework that leverages advanced models.<n>The model demonstrated high performance for symmetry classes, including cubic crystal systems, point groups 3m and m-3m, and space groups Pnma and Pnnn.
  arXiv  Detail & Related papers  (2026-02-04T11:09:51Z)
• Timely Parameter Updating in Over-the-Air Federated Learning [45.5660377179285]
  We propose Freshness Freshness-mAgnItude awaRe top-k (FAIR-k), an algorithm that selects, in each communication round, the most impactful subset of gradients to be updated over the air.<n>We show that FAIR-k promotes fresh (and fair) parameter updates and enhances communication efficiency by enabling an extended period of local training without significantly affecting overall training efficiency.
  arXiv  Detail & Related papers  (2025-12-22T07:18:13Z)
• Leakage-Aware Bandgap Prediction on the JARVIS-DFT Dataset: A Phase-Wise Feature Analysis [0.0]
  We perform a systematic analysis of the JARVIS-DFT bandgap dataset.<n>We identify and remove descriptors that may inadvertently encode band-structure information, such as effective masses.<n>This process yields a curated, leakage-controlled subset of 2280 materials.
  arXiv  Detail & Related papers  (2025-12-17T08:22:32Z)
• Flexible Gravitational-Wave Parameter Estimation with Transformers [73.44614054040267]
  We introduce a flexible transformer-based architecture paired with a training strategy that enables adaptation to diverse analysis settings at inference time.<n>We demonstrate that a single flexible model -- called Dingo-T1 -- can analyze 48 gravitational-wave events from the third LIGO-Virgo-KAGRA Observing Run.
  arXiv  Detail & Related papers  (2025-12-02T17:49:08Z)
• Fast and Accurate RFIC Performance Prediction via Pin Level Graph Neural Networks and Probabilistic Flow [0.5599792629509228]
  This work proposes a lightweight, data-efficient, and topology-aware graph neural network (GNN) model for predicting key performance metrics of active RF circuits.<n> circuits are modeled at the device-terminal level, enabling scalable message passing while reducing data requirements.<n>Experiments on datasets demonstrate high prediction accuracy, with symmetric mean absolute percentage error (sMAPE) and mean relative error (MRE) averaging 2.40% and 2.91%, respectively.
  arXiv  Detail & Related papers  (2025-08-22T14:06:21Z)
• UAV Individual Identification via Distilled RF Fingerprints-Based LLM in ISAC Networks [60.16924915676577]
  Unmanned aerial vehicle (UAV) individual (ID) identification is a critical security surveillance strategy in low-altitude integrated sensing and communication (ISAC) networks.<n>We propose a novel dynamic knowledge distillation (KD)-enabled wireless radio frequency fingerprint large language model (RFF-LLM) framework for UAV ID identification.<n>Experiment results show that the proposed framework achieves 98.38% ID identification accuracy with merely 0.15 million parameters and 2.74 ms response time.
  arXiv  Detail & Related papers  (2025-08-18T03:14:44Z)
• Research on Aerodynamic Performance Prediction of Airfoils Based on a Fusion Algorithm of Transformer and GAN [10.711056540184023]
  This study proposes a deep learning model, Deeptrans, based on the fusion of improved Transformer and generative Adversarial network (GAN)<n>Experiments show that the MSE loss of Deeptrans on the verification set is reduced to 5.6*10-6, and the single-sample prediction time is only 0.0056 seconds.
  arXiv  Detail & Related papers  (2025-06-08T03:30:53Z)
• How to systematically develop an effective AI-based bias correction model? [15.73933701556121]
  This study introduces ReSA-ConvLSTM, an artificial intelligence (AI) framework for systematic bias correction in numerical weather prediction (NWP)<n>We propose three innovations by integrating dynamic climatological normalization, ConvLSTM with temporal causality constraints, and residual self-attention mechanisms.<n>Using 41 years (1981-2021) of global atmospheric data, the framework reduces systematic biases in 2-m air temperature (T2m), 10-m winds (UV10), and sea-level pressure (SLP)<n>The lightweight architecture (10.6M parameters) enables efficient generalization to multiple variables and downstream applications, reducing retraining time by
  arXiv  Detail & Related papers  (2025-04-21T03:02:42Z)
• Forecasting Local Ionospheric Parameters Using Transformers [39.58317527488534]
  We present a novel method for forecasting key ionospheric parameters using transformer-based neural networks.<n>The model provides accurate forecasts and uncertainty quantification of the F2-layer peak plasma frequency (foF2), the F2-layer peak density height (hmF2), and total electron content (TEC) for a given geographic location.
  arXiv  Detail & Related papers  (2025-02-20T23:23:09Z)
• D-FINE: Redefine Regression Task in DETRs as Fine-grained Distribution Refinement [37.78880948551719]
  D-FINE is a powerful real-time object detector that achieves outstanding localization precision. D-FINE comprises two key components: Fine-grained Distribution Refinement (FDR) and Global Optimal localization Self-Distillation (GO-LSD) When pretrained on Objects365, D-FINE-L / X attains 57.1% / 59.3% AP, surpassing all existing real-time detectors.
  arXiv  Detail & Related papers  (2024-10-17T17:57:01Z)
• Machine learning Hubbard parameters with equivariant neural networks [0.0]
  We present a machine learning model based on equivariant neural networks.<n>We target here the prediction of Hubbard parameters computed self-consistently with iterative linear-response calculations.<n>Our model achieves mean absolute relative errors of 3% and 5% for Hubbard $U$ and $V$ parameters, respectively.
  arXiv  Detail & Related papers  (2024-06-04T16:21:24Z)
• Advancing Parameter Efficiency in Fine-tuning via Representation Editing [41.81020951061438]
  We propose a novel fine-tuning approach for neural models, named Representation EDiting (RED) RED modifies the representations generated at some layers through the application of scaling and biasing operations. Remarkably, RED achieves results comparable or superior to both full parameter fine-tuning and other PEFT methods.
  arXiv  Detail & Related papers  (2024-02-23T08:21:02Z)
• Transform Once: Efficient Operator Learning in Frequency Domain [69.74509540521397]
  We study deep neural networks designed to harness the structure in frequency domain for efficient learning of long-range correlations in space or time. This work introduces a blueprint for frequency domain learning through a single transform: transform once (T1)
  arXiv  Detail & Related papers  (2022-11-26T01:56:05Z)
• Scaling & Shifting Your Features: A New Baseline for Efficient Model Tuning [126.84770886628833]
  Existing finetuning methods either tune all parameters of the pretrained model (full finetuning) or only tune the last linear layer (linear probing) We propose a new parameter-efficient finetuning method termed as SSF, representing that researchers only need to Scale and Shift the deep Features extracted by a pre-trained model to catch up with the performance full finetuning.
  arXiv  Detail & Related papers  (2022-10-17T08:14:49Z)
• From Environmental Sound Representation to Robustness of 2D CNN Models Against Adversarial Attacks [82.21746840893658]
  This paper investigates the impact of different standard environmental sound representations (spectrograms) on the recognition performance and adversarial attack robustness of a victim residual convolutional neural network. We show that while the ResNet-18 model trained on DWT spectrograms achieves a high recognition accuracy, attacking this model is relatively more costly for the adversary.
  arXiv  Detail & Related papers  (2022-04-14T15:14:08Z)

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.