Prediction of Diffusion Coefficients in Mixtures with Tensor Completion

• URL: http://arxiv.org/abs/2602.23142v1
• Date: Thu, 26 Feb 2026 16:08:50 GMT
• Title: Prediction of Diffusion Coefficients in Mixtures with Tensor Completion
• Authors: Zeno Romero, Kerstin Münnemann, Hans Hasse, Fabian Jirasek,
• Abstract summary: We present a hybrid tensor completion method (TCM) for predicting temperature-dependent diffusion coefficients at infinite dilution in binary mixtures.<n>The TCM extrapolates linearly to any temperature between 268 K and 378 K, achieving markedly improved prediction accuracy.<n>To further enhance predictive performance, the experimental database was expanded using active learning strategies.
• Score: 0.5039813366558306
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Predicting diffusion coefficients in mixtures is crucial for many applications, as experimental data remain scarce, and machine learning (ML) offers promising alternatives to established semi-empirical models. Among ML models, matrix completion methods (MCMs) have proven effective in predicting thermophysical properties, including diffusion coefficients in binary mixtures. However, MCMs are restricted to single-temperature predictions, and their accuracy depends strongly on the availability of high-quality experimental data for each temperature of interest. In this work, we address this challenge by presenting a hybrid tensor completion method (TCM) for predicting temperature-dependent diffusion coefficients at infinite dilution in binary mixtures. The TCM employs a Tucker decomposition and is jointly trained on experimental data for diffusion coefficients at infinite dilution in binary systems at 298 K, 313 K, and 333 K. Predictions from the semi-empirical SEGWE model serve as prior knowledge within a Bayesian training framework. The TCM then extrapolates linearly to any temperature between 268 K and 378 K, achieving markedly improved prediction accuracy compared to established models across all studied temperatures. To further enhance predictive performance, the experimental database was expanded using active learning (AL) strategies for targeted acquisition of new diffusion data by pulsed-field gradient (PFG) NMR measurements. Diffusion coefficients at infinite dilution in 19 solute + solvent systems were measured at 298 K, 313 K, and 333 K. Incorporating these results yields a substantial improvement in the TCM's predictive accuracy. These findings highlight the potential of combining data-efficient ML methods with adaptive experimentation to advance predictive modeling of transport properties.

Related papers

• High-throughput validation of phase formability and simulation accuracy of Cantor alloys [0.14547222152188427]
  We introduce a quantitative confidence metric to assess the agreement between predictions and experimental observations.<n>The experimental dataset was generated via high- throughput in-situ synchrotron X-ray diffraction on compositionally varied FeNiMnCr alloy libraries.<n>Agreement between the observed and predicted phases was evaluated using either temperature-independent phase classification or a model that incorporates a temperature-dependent probability of phase formation.
  arXiv  Detail & Related papers  (2025-11-24T17:31:16Z)
• Score Distillation of Flow Matching Models [67.86066177182046]
  We extend Score identity Distillation (SiD) to pretrained text-to-image flow-matching models.<n>SiD works out of the box across these models, in both data-free and data-aided settings.<n>This provides the first systematic evidence that score distillation applies broadly to text-to-image flow matching models.
  arXiv  Detail & Related papers  (2025-09-29T17:45:48Z)
• Surrogate Modelling of Proton Dose with Monte Carlo Dropout Uncertainty Quantification [0.0]
  We develop a neural surrogate that integrates Monte Carlo dropout to provide fast, differentiable dose predictions.<n>The approach achieves significant speedups over MC while retaining uncertainty information.<n>It is suitable for integration into robust planning, adaptive replanning and uncertainty-aware optimisation in proton therapy.
  arXiv  Detail & Related papers  (2025-09-16T19:54:49Z)
• Progressive Inference-Time Annealing of Diffusion Models for Sampling from Boltzmann Densities [93.13866975467549]
  We propose Progressive Inference-Time Annealing (PITA) to learn diffusion-based samplers.<n>PITA combines two complementary techniques: Annealing of the Boltzmann distribution and Diffusion smoothing.<n>It enables equilibrium sampling of N-body particle systems, Alanine Dipeptide, and tripeptides in Cartesian coordinates.
  arXiv  Detail & Related papers  (2025-06-19T17:14:22Z)
• Parametric Dynamic Mode Decomposition with multi-linear interpolation for prediction of thermal fields of Al2O3-water nanofluid flows at unseen parameters [0.0]
  The study employs an in-house-based solver to predict the thermal fields of Al$$O$_3$-water nano flow.<n>The performance of two models operating in one- and two-dimensional parametric spaces are investigated.
  arXiv  Detail & Related papers  (2025-03-24T11:27:34Z)
• Predicting the Temperature-Dependent CMC of Surfactant Mixtures with Graph Neural Networks [36.814181034608666]
  Surfactants are key ingredients in foaming and cleansing products across various industries. In practice, surfactant mixtures are typically used due to to performance, environmental, and cost reasons. We develop a graph neural network framework for surfactant mixtures to predict the temperature-dependent CMC.
  arXiv  Detail & Related papers  (2024-11-04T16:17:57Z)
• Accurate melting point prediction through autonomous physics-informed learning [52.217497897835344]
  We present an algorithm for computing melting points by autonomously learning from coexistence simulations in the NPT ensemble. We demonstrate how incorporating physical models of the solid-liquid coexistence evolution enhances the algorithm's accuracy and enables optimal decision-making.
  arXiv  Detail & Related papers  (2023-06-23T07:53:09Z)
• Reconstructing Graph Diffusion History from a Single Snapshot [87.20550495678907]
  We propose a novel barycenter formulation for reconstructing Diffusion history from A single SnapsHot (DASH) We prove that estimation error of diffusion parameters is unavoidable due to NP-hardness of diffusion parameter estimation. We also develop an effective solver named DIffusion hiTting Times with Optimal proposal (DITTO)
  arXiv  Detail & Related papers  (2023-06-01T09:39:32Z)
• Diffusion Models are Minimax Optimal Distribution Estimators [49.47503258639454]
  We provide the first rigorous analysis on approximation and generalization abilities of diffusion modeling. We show that when the true density function belongs to the Besov space and the empirical score matching loss is properly minimized, the generated data distribution achieves the nearly minimax optimal estimation rates.
  arXiv  Detail & Related papers  (2023-03-03T11:31:55Z)
• A deep learning driven pseudospectral PCE based FFT homogenization algorithm for complex microstructures [68.8204255655161]
  It is shown that the proposed method is able to predict central moments of interest while being magnitudes faster to evaluate than traditional approaches. It is shown, that the proposed method is able to predict central moments of interest while being magnitudes faster to evaluate than traditional approaches.
  arXiv  Detail & Related papers  (2021-10-26T07:02:14Z)
• Deep learning for surrogate modelling of 2D mantle convection [1.7499351967216341]
  We show that deep learning techniques can produce reliable parameterized surrogates of partial differential equations. We first use convolutional autoencoders to compress the temperature fields by a factor of 142. We then use FNN and long-short term memory networks (LSTM) to predict the compressed fields.
  arXiv  Detail & Related papers  (2021-08-23T12:13:04Z)

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.