Thermodynamic properties of chemically disordered compounds via AI-driven estimation of partition function with the PULSE method

• URL: http://arxiv.org/abs/2605.28594v1
• Date: Wed, 27 May 2026 15:13:09 GMT
• Title: Thermodynamic properties of chemically disordered compounds via AI-driven estimation of partition function with the PULSE method
• Abstract summary: We present an improved version of the PULSE method for estimating the thermodynamic properties of chemically disordered compounds.<n>The aim is to reduce the computational cost of Monte Carlo approaches for this type of material.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this article, we present an improved version of the PULSE method (Partition function Unsupervised Learning Sampling and Evaluation) for estimating the thermodynamic properties of chemically disordered compounds. The aim is to reduce the computational cost of Monte Carlo approaches for this type of material and to demonstrate that this generative tool can estimate thermodynamic properties by sampling and estimating the partition function of the system. To validate this innovative approach, we use the 2D Ising model as a benchmark. We demonstrate that our method accurately reproduces average properties with high precision and efficiency compared to traditional Monte Carlo sampling methods. Our results highlight the efficiency and adaptability of the PULSE method, making it a valuable tool for studying materials for which conventional methods are too inefficient to compute properties affected by chemical disorder at low cost.

Related papers

• Pure Component Property Estimation Framework Using Explainable Machine Learning Methods [4.8601239628666635]
  The molecular representation method based on the connectivity matrix effectively considers atomic bonding relationships to automatically generate features.<n>The prediction results for normal boiling point (Tb), liquid molar volume, critical temperature (Tc) and critical pressure (Pc) obtained using Artificial Neural Network and Gaussian Process Regression models.<n>To enhance the interpretability of the model, a feature analysis method based on Shapley values is employed to determine the contribution of each feature to the property predictions.
  arXiv  Detail & Related papers  (2025-05-14T20:21:23Z)
• Machine Learning for Improved Density Functional Theory Thermodynamics [0.0]
  We present a machine learning (ML) approach to systematically correct intrinsic energy resolution errors in density functional theory calculations.<n>A neural network model has been trained to predict the discrepancy between DFT-calculated and experimentally measured enthalpies for binary and ternary alloys and compounds.<n>We illustrate the effectiveness of this method by applying it to the Al-Ni-Pd and Al-Ni-Ti systems, which are of interest for high-temperature applications in aerospace and protective coatings.
  arXiv  Detail & Related papers  (2025-03-07T15:46:30Z)
• Feynman-Kac Correctors in Diffusion: Annealing, Guidance, and Product of Experts [64.34482582690927]
  We provide an efficient and principled method for sampling from a sequence of annealed, geometric-averaged, or product distributions derived from pretrained score-based models.<n>We propose Sequential Monte Carlo (SMC) resampling algorithms that leverage inference-time scaling to improve sampling quality.
  arXiv  Detail & Related papers  (2025-03-04T17:46:51Z)
• Asymptotically Optimal Change Detection for Unnormalized Pre- and Post-Change Distributions [65.38208224389027]
  This paper addresses the problem of detecting changes when only unnormalized pre- and post-change distributions are accessible.<n>Our approach is based on the estimation of the Cumulative Sum statistics, which is known to produce optimal performance.
  arXiv  Detail & Related papers  (2024-10-18T17:13:29Z)
• Targeting the partition function of chemically disordered materials with a generative approach based on inverse variational autoencoders [0.0]
  We propose a novel approach where generative machine learning is used to yield a representative set of configurations for accurate property evaluation. Our method employs a specific type of variational autoencoder with inverse roles for the encoder and decoder. We illustrate our approach by computing point-defect formation energies and concentrations in (U, Pu)O2 mixed-oxide fuels.
  arXiv  Detail & Related papers  (2024-08-27T10:05:37Z)
• Advancing Thermodynamic Group-Contribution Methods by Machine Learning: UNIFAC 2.0 [16.003484735462706]
  We use the novel approach to predict a complete set of pair-interaction parameters for the most successful GC method: UNIFAC. The resulting new method, UNIFAC 2.0, is trained and validated on more than 224,000 experimental data points.
  arXiv  Detail & Related papers  (2024-07-25T13:04:58Z)
• Online Variational Sequential Monte Carlo [49.97673761305336]
  We build upon the variational sequential Monte Carlo (VSMC) method, which provides computationally efficient and accurate model parameter estimation and Bayesian latent-state inference. Online VSMC is capable of performing efficiently, entirely on-the-fly, both parameter estimation and particle proposal adaptation.
  arXiv  Detail & Related papers  (2023-12-19T21:45:38Z)
• Monte Carlo Neural PDE Solver for Learning PDEs via Probabilistic Representation [59.45669299295436]
  We propose a Monte Carlo PDE solver for training unsupervised neural solvers.<n>We use the PDEs' probabilistic representation, which regards macroscopic phenomena as ensembles of random particles.<n>Our experiments on convection-diffusion, Allen-Cahn, and Navier-Stokes equations demonstrate significant improvements in accuracy and efficiency.
  arXiv  Detail & Related papers  (2023-02-10T08:05:19Z)
• Molecular Attributes Transfer from Non-Parallel Data [57.010952598634944]
  We formulate molecular optimization as a style transfer problem and present a novel generative model that could automatically learn internal differences between two groups of non-parallel data. Experiments on two molecular optimization tasks, toxicity modification and synthesizability improvement, demonstrate that our model significantly outperforms several state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-11-30T06:10:22Z)
• Prediction of liquid fuel properties using machine learning models with Gaussian processes and probabilistic conditional generative learning [56.67751936864119]
  The present work aims to construct cheap-to-compute machine learning (ML) models to act as closure equations for predicting the physical properties of alternative fuels. Those models can be trained using the database from MD simulations and/or experimental measurements in a data-fusion-fidelity approach. The results show that ML models can predict accurately the fuel properties of a wide range of pressure and temperature conditions.
  arXiv  Detail & Related papers  (2021-10-18T14:43:50Z)
• Spatial Monte Carlo Integration with Annealed Importance Sampling [0.45687771576879593]
  A new method is proposed to evaluate the expectations on Ising models combining AIS and SMCI. The proposed method performs efficiently in both high- and low-temperature regions.
  arXiv  Detail & Related papers  (2020-12-21T09:26:40Z)

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.