Computing formation enthalpies through an explainable machine learning
method: the case of Lanthanide Orthophosphates solid solutions
- URL: http://arxiv.org/abs/2303.03748v1
- Date: Tue, 7 Mar 2023 09:14:16 GMT
- Title: Computing formation enthalpies through an explainable machine learning
method: the case of Lanthanide Orthophosphates solid solutions
- Authors: Edoardo Di Napoli (1), Xinzhe Wu (1), Thomas Bornhake (2) Piotr M.
Kowalski (3) ((1) J\"ulich Supercomputing Centre Forschungszentrum J\"ulich
GmbH, (2) Physics Department RWTH Aachen University, (3) Institute of Energy
and Climate Research Forschungszentrum J\"ulich GmbH)
- Abstract summary: We describe a proposal to use a sophisticated combination of traditional Machine Learning methods to obtain an explainable model.
We demonstrate the effectiveness of our methodology in deriving a new highly accurate expression for the enthalpy of formation of solid solutions of lanthanides orthophosphates.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In the last decade, the use of Machine and Deep Learning (MDL) methods in
Condensed Matter physics has seen a steep increase in the number of problems
tackled and methods employed. A number of distinct MDL approaches have been
employed in many different topics; from prediction of materials properties to
computation of Density Functional Theory potentials and inter-atomic force
fields. In many cases the result is a surrogate model which returns promising
predictions but is opaque on the inner mechanisms of its success. On the other
hand, the typical practitioner looks for answers that are explainable and
provide a clear insight on the mechanisms governing a physical phenomena. In
this work, we describe a proposal to use a sophisticated combination of
traditional Machine Learning methods to obtain an explainable model that
outputs an explicit functional formulation for the material property of
interest. We demonstrate the effectiveness of our methodology in deriving a new
highly accurate expression for the enthalpy of formation of solid solutions of
lanthanides orthophosphates.
Related papers
- Balancing Molecular Information and Empirical Data in the Prediction of Physico-Chemical Properties [8.649679686652648]
We propose a general method for combining molecular descriptors with representation learning.
The proposed hybrid model exploits chemical structure information using graph neural networks.
It automatically detects cases where structure-based predictions are unreliable, in which case it corrects them by representation-learning based predictions.
arXiv Detail & Related papers (2024-06-12T10:51:00Z) - Information theory unifies atomistic machine learning, uncertainty quantification, and materials thermodynamics [4.59916193837551]
An accurate description of information is relevant for a range of problems in atomistic modeling.
We introduce an information theoretical framework that unifies predictions of phase transformations, kinetic events, dataset optimality, and model-free UQ from atomistic simulations.
arXiv Detail & Related papers (2024-04-18T17:50:15Z) - Machine learning for accuracy in density functional approximations [0.0]
Recent progress in applying machine learning to improve the accuracy of density functional approximations is reviewed.
Promises and challenges in devising machine learning models transferable between different chemistries and materials classes are discussed.
arXiv Detail & Related papers (2023-11-01T00:02:09Z) - Discovering Interpretable Physical Models using Symbolic Regression and
Discrete Exterior Calculus [55.2480439325792]
We propose a framework that combines Symbolic Regression (SR) and Discrete Exterior Calculus (DEC) for the automated discovery of physical models.
DEC provides building blocks for the discrete analogue of field theories, which are beyond the state-of-the-art applications of SR to physical problems.
We prove the effectiveness of our methodology by re-discovering three models of Continuum Physics from synthetic experimental data.
arXiv Detail & Related papers (2023-10-10T13:23:05Z) - MinT: Boosting Generalization in Mathematical Reasoning via Multi-View
Fine-Tuning [53.90744622542961]
Reasoning in mathematical domains remains a significant challenge for small language models (LMs)
We introduce a new method that exploits existing mathematical problem datasets with diverse annotation styles.
Experimental results show that our strategy enables a LLaMA-7B model to outperform prior approaches.
arXiv Detail & Related papers (2023-07-16T05:41:53Z) - On the Integration of Physics-Based Machine Learning with Hierarchical
Bayesian Modeling Techniques [0.0]
This paper proposes to embed mechanics-based models into the mean function of a Gaussian Process (GP) model and characterize potential discrepancies through kernel machines.
The stationarity of the kernel function is a difficult hurdle in the sequential processing of long data sets, resolved through hierarchical Bayesian techniques.
Using numerical and experimental examples, potential applications of the proposed method to structural dynamics inverse problems are demonstrated.
arXiv Detail & Related papers (2023-03-01T02:29:41Z) - MACE: An Efficient Model-Agnostic Framework for Counterfactual
Explanation [132.77005365032468]
We propose a novel framework of Model-Agnostic Counterfactual Explanation (MACE)
In our MACE approach, we propose a novel RL-based method for finding good counterfactual examples and a gradient-less descent method for improving proximity.
Experiments on public datasets validate the effectiveness with better validity, sparsity and proximity.
arXiv Detail & Related papers (2022-05-31T04:57:06Z) - A deep learning energy method for hyperelasticity and viscoelasticity [0.0]
The presented deep energy method (DEM) is self-contained and meshfree.
It can accurately capture the three-dimensional (3D) mechanical response without requiring any time-consuming training data generation.
arXiv Detail & Related papers (2022-01-15T05:52:38Z) - BIGDML: Towards Exact Machine Learning Force Fields for Materials [55.944221055171276]
Machine-learning force fields (MLFF) should be accurate, computationally and data efficient, and applicable to molecules, materials, and interfaces thereof.
Here, we introduce the Bravais-Inspired Gradient-Domain Machine Learning approach and demonstrate its ability to construct reliable force fields using a training set with just 10-200 atoms.
arXiv Detail & Related papers (2021-06-08T10:14:57Z) - 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) - Learning Manifold Implicitly via Explicit Heat-Kernel Learning [63.354671267760516]
We propose the concept of implicit manifold learning, where manifold information is implicitly obtained by learning the associated heat kernel.
The learned heat kernel can be applied to various kernel-based machine learning models, including deep generative models (DGM) for data generation and Stein Variational Gradient Descent for Bayesian inference.
arXiv Detail & Related papers (2020-10-05T03:39:58Z)
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.