Quasi-van Hove singularities informed approach improving DOS/pDOS predictions in GNN

• URL: http://arxiv.org/abs/2509.02818v1
• Date: Tue, 02 Sep 2025 20:36:59 GMT
• Title: Quasi-van Hove singularities informed approach improving DOS/pDOS predictions in GNN
• Abstract summary: We introduce an extension of the concept of Van Hoff singularities in order to explicitly use them in machine learning methods of models as a priori information.<n>The claimed method becomes possible when, instead of directly calculating the density of quantum states, we operate with estimates obtained by machine learning methods.
• Score: 0.0
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: In this paper, we introduce an extension of the concept of Van Hoff singularities in order to explicitly use them in machine learning methods of models as a priori information. The claimed method becomes possible when, instead of directly calculating the density of quantum states, we operate with estimates obtained by machine learning methods. Then, determining additional information allows us to organize post-processing, which significantly improves the quality of model prediction. Analytically, the effect is similar to Van Hove singularities of high order due to the additional degree of degeneracy of the system. As a mechanism for redistributing information about the system, we propose regularization, which is close in meaning to taking into account Fisher information.

Related papers

• Informed Spectral Normalized Gaussian Processes for Trajectory Prediction [0.0]
  We propose a novel regularization-based continual learning method for SNGPs. Our proposal builds upon well-established methods and requires no rehearsal memory or parameter expansion. We apply our informed SNGP model to the trajectory prediction problem in autonomous driving by integrating prior drivability knowledge.
  arXiv  Detail & Related papers  (2024-03-18T17:05:24Z)
• Generalizing Backpropagation for Gradient-Based Interpretability [103.2998254573497]
  We show that the gradient of a model is a special case of a more general formulation using semirings. This observation allows us to generalize the backpropagation algorithm to efficiently compute other interpretable statistics.
  arXiv  Detail & Related papers  (2023-07-06T15:19:53Z)
• Prediction-Oriented Bayesian Active Learning [51.426960808684655]
  Expected predictive information gain (EPIG) is an acquisition function that measures information gain in the space of predictions rather than parameters. EPIG leads to stronger predictive performance compared with BALD across a range of datasets and models.
  arXiv  Detail & Related papers  (2023-04-17T10:59:57Z)
• Self-Distillation for Gaussian Process Regression and Classification [0.0]
  We propose two approaches to extend the notion of knowledge distillation to Gaussian Process Regression (GPR) and Gaussian Process Classification (GPC) The data-centric approach resembles most current distillation techniques for machine learning, and refits a model on deterministic predictions from the teacher. The distribution-centric approach re-uses the full probabilistic posterior for the next iteration.
  arXiv  Detail & Related papers  (2023-04-05T17:59:20Z)
• STEERING: Stein Information Directed Exploration for Model-Based Reinforcement Learning [111.75423966239092]
  We propose an exploration incentive in terms of the integral probability metric (IPM) between a current estimate of the transition model and the unknown optimal. Based on KSD, we develop a novel algorithm algo: textbfSTEin information dirtextbfEcted exploration for model-based textbfReinforcement LearntextbfING.
  arXiv  Detail & Related papers  (2023-01-28T00:49:28Z)
• Sparsity-Inducing Categorical Prior Improves Robustness of the Information Bottleneck [4.2903672492917755]
  We present a novel sparsity-inducing spike-slab prior that uses sparsity as a mechanism to provide flexibility. We show that the proposed approach improves the accuracy and robustness compared with the traditional fixed -imensional priors.
  arXiv  Detail & Related papers  (2022-03-04T22:22:51Z)
• Self-explaining variational posterior distributions for Gaussian Process models [0.0]
  We introduce a corresponding concept for variational GaussianProcesses. Our proposed self-explaining variational posterior distribution allows to incorporate both general prior knowledge about a target function as a whole and prior knowledge about the contribution of individual features.
  arXiv  Detail & Related papers  (2021-09-08T15:14:22Z)
• Beyond Trivial Counterfactual Explanations with Diverse Valuable Explanations [64.85696493596821]
  In computer vision applications, generative counterfactual methods indicate how to perturb a model's input to change its prediction. We propose a counterfactual method that learns a perturbation in a disentangled latent space that is constrained using a diversity-enforcing loss. Our model improves the success rate of producing high-quality valuable explanations when compared to previous state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-03-18T12:57:34Z)
• Incorporating Causal Graphical Prior Knowledge into Predictive Modeling via Simple Data Augmentation [92.96204497841032]
  Causal graphs (CGs) are compact representations of the knowledge of the data generating processes behind the data distributions. We propose a model-agnostic data augmentation method that allows us to exploit the prior knowledge of the conditional independence (CI) relations. We experimentally show that the proposed method is effective in improving the prediction accuracy, especially in the small-data regime.
  arXiv  Detail & Related papers  (2021-02-27T06:13:59Z)
• Using Data Assimilation to Train a Hybrid Forecast System that Combines Machine-Learning and Knowledge-Based Components [52.77024349608834]
  We consider the problem of data-assisted forecasting of chaotic dynamical systems when the available data is noisy partial measurements. We show that by using partial measurements of the state of the dynamical system, we can train a machine learning model to improve predictions made by an imperfect knowledge-based model.
  arXiv  Detail & Related papers  (2021-02-15T19:56:48Z)

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.