Learning inducing points and uncertainty on molecular data by scalable variational Gaussian processes

• URL: http://arxiv.org/abs/2207.07654v3
• Date: Tue, 20 Feb 2024 03:34:30 GMT
• Title: Learning inducing points and uncertainty on molecular data by scalable variational Gaussian processes
• Authors: Mikhail Tsitsvero, Mingoo Jin, Andrey Lyalin
• Abstract summary: We show that variational learning of the inducing points in a molecular descriptor space improves the prediction of energies and atomic forces on two molecular dynamics datasets. We extend our study to a large molecular crystal system, showing that variational GP models perform well for predicting atomic forces by efficiently learning a sparse representation of the dataset.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Uncertainty control and scalability to large datasets are the two main issues for the deployment of Gaussian process (GP) models within the autonomous machine learning-based prediction pipelines in material science and chemistry. One way to address both of these issues is by introducing the latent inducing point variables and choosing the right approximation for the marginal log-likelihood objective. Here, we empirically show that variational learning of the inducing points in a molecular descriptor space improves the prediction of energies and atomic forces on two molecular dynamics datasets. First, we show that variational GPs can learn to represent the configurations of the molecules of different types that were not present within the initialization set of configurations. We provide a comparison of alternative log-likelihood training objectives and variational distributions. Among several evaluated approximate marginal log-likelihood objectives, we show that predictive log-likelihood provides excellent uncertainty estimates at the slight expense of predictive quality. Furthermore, we extend our study to a large molecular crystal system, showing that variational GP models perform well for predicting atomic forces by efficiently learning a sparse representation of the dataset.

Related papers

• Stacked ensemble\-based mutagenicity prediction model using multiple modalities with graph attention network [0.9736758288065405]
  Mutagenicity is a concern due to its association with genetic mutations which can result in a variety of negative consequences. In this work, we introduce a novel stacked ensemble based mutagenicity prediction model.
  arXiv  Detail & Related papers  (2024-09-03T09:14:21Z)
• Enhanced sampling of robust molecular datasets with uncertainty-based collective variables [0.0]
  We propose a method that leverages uncertainty as the collective variable (CV) to guide the acquisition of chemically-relevant data points. This approach employs a Gaussian Mixture Model-based uncertainty metric from a single model as the CV for biased molecular dynamics simulations.
  arXiv  Detail & Related papers  (2024-02-06T06:42:51Z)
• Learning Invariant Molecular Representation in Latent Discrete Space [52.13724532622099]
  We propose a new framework for learning molecular representations that exhibit invariance and robustness against distribution shifts. Our model achieves stronger generalization against state-of-the-art baselines in the presence of various distribution shifts.
  arXiv  Detail & Related papers  (2023-10-22T04:06:44Z)
• Variational Autoencoding Molecular Graphs with Denoising Diffusion Probabilistic Model [0.0]
  We propose a novel deep generative model that incorporates a hierarchical structure into the probabilistic latent vectors. We demonstrate that our model can design effective molecular latent vectors for molecular property prediction from some experiments by small datasets on physical properties and activity.
  arXiv  Detail & Related papers  (2023-07-02T17:29:41Z)
• Towards Predicting Equilibrium Distributions for Molecular Systems with Deep Learning [60.02391969049972]
  We introduce a novel deep learning framework, called Distributional Graphormer (DiG), in an attempt to predict the equilibrium distribution of molecular systems. DiG employs deep neural networks to transform a simple distribution towards the equilibrium distribution, conditioned on a descriptor of a molecular system.
  arXiv  Detail & Related papers  (2023-06-08T17:12:08Z)
• Uncertainty quantification for predictions of atomistic neural networks [0.0]
  This paper explores the value of uncertainty quantification on predictions for trained neural networks (NNs) on quantum chemical reference data. The architecture of the PhysNet NN was suitably modified and the resulting model was evaluated with different metrics to quantify calibration, quality of predictions, and whether prediction error and the predicted uncertainty can be correlated.
  arXiv  Detail & Related papers  (2022-07-14T13:39:43Z)
• Pre-training via Denoising for Molecular Property Prediction [53.409242538744444]
  We describe a pre-training technique that utilizes large datasets of 3D molecular structures at equilibrium. Inspired by recent advances in noise regularization, our pre-training objective is based on denoising.
  arXiv  Detail & Related papers  (2022-05-31T22:28:34Z)
• GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generation [102.85440102147267]
  We propose a novel generative model named GeoDiff for molecular conformation prediction. We show that GeoDiff is superior or comparable to existing state-of-the-art approaches.
  arXiv  Detail & Related papers  (2022-03-06T09:47:01Z)
• 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)
• Calibrated Uncertainty for Molecular Property Prediction using Ensembles of Message Passing Neural Networks [11.47132155400871]
  We extend a message passing neural network designed specifically for predicting properties of molecules and materials. We show that our approach results in accurate models for predicting molecular formation energies with calibrated uncertainty.
  arXiv  Detail & Related papers  (2021-07-13T13:28:11Z)
• Learning Neural Generative Dynamics for Molecular Conformation Generation [89.03173504444415]
  We study how to generate molecule conformations (textiti.e., 3D structures) from a molecular graph. We propose a novel probabilistic framework to generate valid and diverse conformations given a molecular graph.
  arXiv  Detail & Related papers  (2021-02-20T03:17:58Z)

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.