Balancing Molecular Information and Empirical Data in the Prediction of Physico-Chemical Properties

• URL: http://arxiv.org/abs/2406.08075v1
• Date: Wed, 12 Jun 2024 10:51:00 GMT
• Title: Balancing Molecular Information and Empirical Data in the Prediction of Physico-Chemical Properties
• Authors: Johannes Zenn, Dominik Gond, Fabian Jirasek, Robert Bamler,
• Abstract summary: 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.
• Score: 8.649679686652648
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Predicting the physico-chemical properties of pure substances and mixtures is a central task in thermodynamics. Established prediction methods range from fully physics-based ab-initio calculations, which are only feasible for very simple systems, over descriptor-based methods that use some information on the molecules to be modeled together with fitted model parameters (e.g., quantitative-structure-property relationship methods or classical group contribution methods), to representation-learning methods, which may, in extreme cases, completely ignore molecular descriptors and extrapolate only from existing data on the property to be modeled (e.g., matrix completion methods). In this work, we propose a general method for combining molecular descriptors with representation learning using the so-called expectation maximization algorithm from the probabilistic machine learning literature, which uses uncertainty estimates to trade off between the two approaches. The proposed hybrid model exploits chemical structure information using graph neural networks, but it automatically detects cases where structure-based predictions are unreliable, in which case it corrects them by representation-learning based predictions that can better specialize to unusual cases. The effectiveness of the proposed method is demonstrated using the prediction of activity coefficients in binary mixtures as an example. The results are compelling, as the method significantly improves predictive accuracy over the current state of the art, showcasing its potential to advance the prediction of physico-chemical properties in general.

Related papers

• Predicting ionic conductivity in solids from the machine-learned potential energy landscape [68.25662704255433]
  Superionic materials are essential for advancing solid-state batteries, which offer improved energy density and safety. Conventional computational methods for identifying such materials are resource-intensive and not easily scalable. We propose an approach for the quick and reliable evaluation of ionic conductivity through the analysis of a universal interatomic potential.
  arXiv  Detail & Related papers  (2024-11-11T09:01:36Z)
• Pre-trained Molecular Language Models with Random Functional Group Masking [54.900360309677794]
  We propose a SMILES-based underlineem Molecular underlineem Language underlineem Model, which randomly masking SMILES subsequences corresponding to specific molecular atoms. This technique aims to compel the model to better infer molecular structures and properties, thus enhancing its predictive capabilities.
  arXiv  Detail & Related papers  (2024-11-03T01:56:15Z)
• Hierarchical Matrix Completion for the Prediction of Properties of Binary Mixtures [3.0478550046333965]
  We introduce a novel generic approach for improving data-driven models. We lump components that behave similarly into chemical classes and model them jointly. Using clustering leads to significantly improved predictions compared to an MCM without clustering.
  arXiv  Detail & Related papers  (2024-10-08T14:04:30Z)
• Accurate melting point prediction through autonomous physics-informed learning [52.217497897835344]
  We present an algorithm for computing melting points by autonomously learning from coexistence simulations in the NPT ensemble. We demonstrate how incorporating physical models of the solid-liquid coexistence evolution enhances the algorithm's accuracy and enables optimal decision-making.
  arXiv  Detail & Related papers  (2023-06-23T07:53:09Z)
• Atomic and Subgraph-aware Bilateral Aggregation for Molecular Representation Learning [57.670845619155195]
  We introduce a new model for molecular representation learning called the Atomic and Subgraph-aware Bilateral Aggregation (ASBA) ASBA addresses the limitations of previous atom-wise and subgraph-wise models by incorporating both types of information. Our method offers a more comprehensive way to learn representations for molecular property prediction and has broad potential in drug and material discovery applications.
  arXiv  Detail & Related papers  (2023-05-22T00:56:00Z)
• Capturing dynamical correlations using implicit neural representations [85.66456606776552]
  We develop an artificial intelligence framework which combines a neural network trained to mimic simulated data from a model Hamiltonian with automatic differentiation to recover unknown parameters from experimental data. In doing so, we illustrate the ability to build and train a differentiable model only once, which then can be applied in real-time to multi-dimensional scattering data.
  arXiv  Detail & Related papers  (2023-04-08T07:55:36Z)
• 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)
• Low cost prediction of probability distributions of molecular properties for early virtual screening [0.8702432681310399]
  This article applies Hierarchical Correlation Reconstruction approach, previously applied in the analysis of demographic, financial and astronomical data. The whole methodology constitutes therefore a great support for medicinal chemists, as it enable fast rejection of compounds with the lowest potential of desired physicochemical/ADMET characteristic.
  arXiv  Detail & Related papers  (2022-07-21T13:29:26Z)
• Hybridizing Physical and Data-driven Prediction Methods for Physicochemical Properties [19.50116420011026]
  We present a generic way to hybridize physical and data-driven methods for predicting physicochemical properties. The approach distills' the physical method's predictions into a prior model and combines it with sparse experimental data using Bayesian inference.
  arXiv  Detail & Related papers  (2022-02-17T18:15:03Z)
• 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)

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.