SPT-NRTL: A physics-guided machine learning model to predict thermodynamically consistent activity coefficients

• URL: http://arxiv.org/abs/2209.04135v1
• Date: Fri, 9 Sep 2022 06:21:05 GMT
• Title: SPT-NRTL: A physics-guided machine learning model to predict thermodynamically consistent activity coefficients
• Authors: Benedikt Winter, Clemens Winter, Timm Esper, Johannes Schilling, Andr\'e Bardow
• Abstract summary: We introduce SPT-NRTL, a machine learning model to predict thermodynamically consistent activity coefficients. SPT-NRTL achieves higher accuracy than UNIFAC in the prediction of activity coefficients across all functional groups.
• Score: 0.12352483741564477
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The availability of property data is one of the major bottlenecks in the development of chemical processes, often requiring time-consuming and expensive experiments or limiting the design space to a small number of known molecules. This bottleneck has been the motivation behind the continuing development of predictive property models. For the property prediction of novel molecules, group contribution methods have been groundbreaking. In recent times, machine learning has joined the more established property prediction models. However, even with recent successes, the integration of physical constraints into machine learning models remains challenging. Physical constraints are vital to many thermodynamic properties, such as the Gibbs-Dunham relation, introducing an additional layer of complexity into the prediction. Here, we introduce SPT-NRTL, a machine learning model to predict thermodynamically consistent activity coefficients and provide NRTL parameters for easy use in process simulations. The results show that SPT-NRTL achieves higher accuracy than UNIFAC in the prediction of activity coefficients across all functional groups and is able to predict many vapor-liquid-equilibria with near experimental accuracy, as illustrated for the exemplary mixtures water/ethanol and chloroform/n-hexane. To ease the application of SPT-NRTL, NRTL-parameters of 100 000 000 mixtures are calculated with SPT-NRTL and provided online.

Related papers

• 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)
• A Multi-Grained Symmetric Differential Equation Model for Learning Protein-Ligand Binding Dynamics [74.93549765488103]
  In drug discovery, molecular dynamics simulation provides a powerful tool for predicting binding affinities, estimating transport properties, and exploring pocket sites. We propose NeuralMD, the first machine learning surrogate that can facilitate numerical MD and provide accurate simulations in protein-ligand binding. We show the efficiency and effectiveness of NeuralMD, with a 2000$times$ speedup over standard numerical MD simulation and outperforming all other ML approaches by up to 80% under the stability metric.
  arXiv  Detail & Related papers  (2024-01-26T09:35:17Z)
• 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)
• Machine learning enabled experimental design and parameter estimation for ultrafast spin dynamics [54.172707311728885]
  We introduce a methodology that combines machine learning with Bayesian optimal experimental design (BOED) Our method employs a neural network model for large-scale spin dynamics simulations for precise distribution and utility calculations in BOED. Our numerical benchmarks demonstrate the superior performance of our method in guiding XPFS experiments, predicting model parameters, and yielding more informative measurements within limited experimental time.
  arXiv  Detail & Related papers  (2023-06-03T06:19:20Z)
• Stabilizing Machine Learning Prediction of Dynamics: Noise and Noise-inspired Regularization [58.720142291102135]
  Recent has shown that machine learning (ML) models can be trained to accurately forecast the dynamics of chaotic dynamical systems. In the absence of mitigating techniques, this technique can result in artificially rapid error growth, leading to inaccurate predictions and/or climate instability. We introduce Linearized Multi-Noise Training (LMNT), a regularization technique that deterministically approximates the effect of many small, independent noise realizations added to the model input during training.
  arXiv  Detail & Related papers  (2022-11-09T23:40:52Z)
• Dynamic weights enabled Physics-Informed Neural Network for simulating the mobility of Engineered Nano-particles in a contaminated aquifer [0.0]
  Engineered Nano-particles (ENPs) have emerged as an efficient reactive agent for the in-situ degradation of groundwater contaminants. The complex transport and retention mechanisms of ENPs hinder the development of an efficient remediation strategy. This work uses a dynamic, weight-enabled Physics-Informed Neural Network (dw-PINN) framework to model the nano-particle behavior within an aquifer.
  arXiv  Detail & Related papers  (2022-10-25T07:55:20Z)
• A smile is all you need: Predicting limiting activity coefficients from SMILES with natural language processing [0.1349420109127767]
  We introduce the SMILES-to-Properties-Transformer (SPT), a natural language processing network to predict binary limiting activity coefficients from SMILES codes. We train our network on a large dataset of synthetic data sampled from COSMO-RS and fine-tune the model on experimental data. This training strategy enables SPT to accurately predict limiting activity coefficients even for unknown molecules, cutting the mean prediction error in half compared to state-of-the-art models.
  arXiv  Detail & Related papers  (2022-06-15T07:11:37Z)
• Toward Development of Machine Learned Techniques for Production of Compact Kinetic Models [0.0]
  Chemical kinetic models are an essential component in the development and optimisation of combustion devices. We present a novel automated compute intensification methodology to produce overly-reduced and optimised chemical kinetic models.
  arXiv  Detail & Related papers  (2022-02-16T12:31:24Z)
• 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)
• Thermal Neural Networks: Lumped-Parameter Thermal Modeling With State-Space Machine Learning [0.0]
  Thermal models for electric power systems are required to be both, real-time capable and of high estimation accuracy. In this work, the thermal neural network (TNN) is introduced, which unifies both, consolidated knowledge in the form of heat-transfer-based lumped- parameter models. A TNN has physically interpretable states through its state-space representation, is end-to-end trainable, and requires no material, geometry, nor expert knowledge for its design.
  arXiv  Detail & Related papers  (2021-03-30T13:15:48Z)
• Automated discovery of a robust interatomic potential for aluminum [4.6028828826414925]
  Machine learning (ML) based potentials aim for faithful emulation of quantum mechanics (QM) calculations at drastically reduced computational cost. We present a highly automated approach to dataset construction using the principles of active learning (AL) We demonstrate this approach by building an ML potential for aluminum (ANI-Al) To demonstrate transferability, we perform a 1.3M atom shock simulation, and show that ANI-Al predictions agree very well with DFT calculations on local atomic environments sampled from the nonequilibrium dynamics.
  arXiv  Detail & Related papers  (2020-03-10T19:06:32Z)

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.