# Data-driven study of the enthalpy of mixing in the liquid phase

https://mdr.nims.go.jp/datasets/abe06908-c299-46f0-9ddb-1f6378105dbf

## File

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## Id

abe06908-c299-46f0-9ddb-1f6378105dbf

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2024-10-04T06:19:56.811043Z

## Updated at

2024-10-04T23:30:08.869710Z

## Published at

2024-10-04T23:30:09.997344Z

## Doi



## First published url

https://doi.org/10.1016/j.calphad.2024.102745

## Date published

2024-09-25

## Recorded date published

2024-12

## Resource type

journal_article

## Manuscript type

vor

## Collection



## Title

- title: Data-driven study of the enthalpy of mixing in the liquid phase
  title_type: original
  lang: en

## Description

- description: The enthalpy of mixing in the liquid phase is a thermodynamic property
    reflecting interactions between elements that is key to predict phase transformations.
    Widely used models exist to predict it, but they have never been systematically
    evaluated. To address this, we collect a large amount of enthalpy of mixing data
    in binary liquids from a review of about 1000 thermodynamic evaluations. This
    allows us to clarify the prediction accuracy of Miedema's model which is state-of-the-art.
    We show that more accurate predictions can be obtained from a machine learning
    model based on LightGBM, and we provide them in 2415 binary systems. The data
    we collect also allows us to evaluate another empirical model to predict the excess
    heat capacity that we apply to 2211 binary liquids. We then extend the data collection
    to ternary metallic liquids and find that, when mixing is exothermic, extrapolations
    from the binary systems by Muggianu's model systematically lead to slight overestimations
    of roughly 10 % close to the equimolar composition. Therefore, our LightGBM model
    can provide reasonable estimates for ternary alloys and, by extension, for multicomponent
    alloys. Our findings extracted from rich datasets can be used to feed thermodynamic,
    empirical and machine learning models for material development. Our data, predictions,
    and code to generate machine learning descriptors from thermodynamic properties
    are all made available.
  description_type: abstract
  lang: und

## Creator

- name: Guillaume Deffrennes
  role: author
  orcid: https://orcid.org/0000-0002-3752-2537
- name: Bengt Hallstedt
  role: author
- name: Taichi Abe
  role: author
- name: Quentin Bizot
  role: author
- name: Evelyne Fischer
  role: author
- name: Jean-Marc Joubert
  role: author
- name: Kei Terayama
  role: author
- name: Ryo Tamura
  role: author
  orcid: https://orcid.org/0000-0002-0349-358X

## Contact agent



## Publisher

organization: Elsevier BV

## Managing organization



## Keyword

- subject: enthalpy of mixing
  schema: not_defined
- subject: machine learning
  schema: not_defined
- subject: liquid phase
  schema: not_defined

## Rights

- identifier: https://creativecommons.org/licenses/by/4.0/

## Other identifier(s)



## Data origin

- data_origin_type: other

## Embargo



## Journal

- title: Calphad
  issn: '03645916'
  volume: '87'
  article_number: '102745'

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## Specimen



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## Fileset

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## Thumbnail

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filename: 1-s2.0-S0364591624000877-main.pdf