Targeting the partition function of chemically disordered materials with a generative approach based on inverse variational autoencoders

• URL: http://arxiv.org/abs/2408.14928v2
• Date: Tue, 10 Sep 2024 07:02:27 GMT
• Title: Targeting the partition function of chemically disordered materials with a generative approach based on inverse variational autoencoders
• Authors: Maciej J. Karcz, Luca Messina, Eiji Kawasaki, Emeric Bourasseau,
• Abstract summary: 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Computing atomic-scale properties of chemically disordered materials requires an efficient exploration of their vast configuration space. Traditional approaches such as Monte Carlo or Special Quasirandom Structures either entail sampling an excessive amount of configurations or do not ensure that the configuration space has been properly covered. In this work, we propose a novel approach where generative machine learning is used to yield a representative set of configurations for accurate property evaluation and provide accurate estimations of atomic-scale properties with minimal computational cost. Our method employs a specific type of variational autoencoder with inverse roles for the encoder and decoder, enabling the application of an unsupervised active learning scheme that does not require any initial training database. The model iteratively generates configuration batches, whose properties are computed with conventional atomic-scale methods. These results are then fed back into the model to estimate the partition function, repeating the process until convergence. We illustrate our approach by computing point-defect formation energies and concentrations in (U, Pu)O2 mixed-oxide fuels. In addition, the ML model provides valuable insights into the physical factors influencing the target property. Our method is generally applicable to explore other properties, such as atomic-scale diffusion coefficients, in ideally or non-ideally disordered materials like high-entropy alloys.

Related papers

• Text-Guided Multi-Property Molecular Optimization with a Diffusion Language Model [77.50732023411811]
  We propose a text-guided multi-property molecular optimization method utilizing transformer-based diffusion language model (TransDLM) TransDLM leverages standardized chemical nomenclature as semantic representations of molecules and implicitly embeds property requirements into textual descriptions. Our approach surpasses state-of-the-art methods in optimizing molecular structural similarity and enhancing chemical properties on the benchmark dataset.
  arXiv  Detail & Related papers  (2024-10-17T14:30:27Z)
• Data-efficient and Interpretable Inverse Materials Design using a Disentangled Variational Autoencoder [2.563209727695243]
  Inverse materials design has proven successful in accelerating novel material discovery. Many inverse materials design methods use unsupervised learning where a latent space is learned to offer a compact description of materials representations. Here, we present a semi-supervised learning approach based on a disentangled variational autoencoder to learn a probabilistic relationship between features, latent variables and target properties.
  arXiv  Detail & Related papers  (2024-09-10T02:21:13Z)
• 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)
• Molecule Design by Latent Prompt Transformer [76.2112075557233]
  This work explores the challenging problem of molecule design by framing it as a conditional generative modeling task. We propose a novel generative model comprising three components: (1) a latent vector with a learnable prior distribution; (2) a molecule generation model based on a causal Transformer, which uses the latent vector as a prompt; and (3) a property prediction model that predicts a molecule's target properties and/or constraint values using the latent prompt.
  arXiv  Detail & Related papers  (2024-02-27T03:33:23Z)
• Disentanglement via Latent Quantization [60.37109712033694]
  In this work, we construct an inductive bias towards encoding to and decoding from an organized latent space. We demonstrate the broad applicability of this approach by adding it to both basic data-re (vanilla autoencoder) and latent-reconstructing (InfoGAN) generative models.
  arXiv  Detail & Related papers  (2023-05-28T06:30:29Z)
• Generative structured normalizing flow Gaussian processes applied to spectroscopic data [4.0773490083614075]
  In the physical sciences, limited training data may not adequately characterize future observed data. It is critical that models adequately indicate uncertainty, particularly when they may be asked to extrapolate. We demonstrate the methodology on laser-induced breakdown spectroscopy data from the ChemCam instrument onboard the Mars rover Curiosity.
  arXiv  Detail & Related papers  (2022-12-14T23:57:46Z)
• 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)
• A Universal Framework for Featurization of Atomistic Systems [0.0]
  Reactive force fields based on physics or machine learning can be used to bridge the gap in time and length scales. We introduce the Gaussian multi-pole (GMP) featurization scheme that utilizes physically-relevant multi-pole expansions of the electron density around atoms. We demonstrate that GMP-based models can achieve chemical accuracy for the QM9 dataset, and their accuracy remains reasonable even when extrapolating to new elements.
  arXiv  Detail & Related papers  (2021-02-04T03:11:00Z)
• Goal-directed Generation of Discrete Structures with Conditional Generative Models [85.51463588099556]
  We introduce a novel approach to directly optimize a reinforcement learning objective, maximizing an expected reward. We test our methodology on two tasks: generating molecules with user-defined properties and identifying short python expressions which evaluate to a given target value.
  arXiv  Detail & Related papers  (2020-10-05T20:03:13Z)
• Augmentation of the Reconstruction Performance of Fuzzy C-Means with an Optimized Fuzzification Factor Vector [99.19847674810079]
  Fuzzy C-Means (FCM) is one of the most frequently used methods to construct information granules. In this paper, we augment the FCM-based degranulation mechanism by introducing a vector of fuzzification factors. Experiments completed for both synthetic and publicly available datasets show that the proposed approach outperforms the generic data reconstruction approach.
  arXiv  Detail & Related papers  (2020-04-13T04:17:30Z)
• Embedded-physics machine learning for coarse-graining and collective variable discovery without data [3.222802562733787]
  We present a novel learning framework that consistently embeds underlying physics. We propose a novel objective based on reverse Kullback-Leibler divergence that fully incorporates the available physics in the form of the atomistic force field. We demonstrate the algorithmic advances in terms of predictive ability and the physical meaning of the revealed CVs for a bimodal potential energy function and the alanine dipeptide.
  arXiv  Detail & Related papers  (2020-02-24T10:28:41Z)

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.