# Active learning for predicting the enthalpy of mixing in binary liquids based on ab initio molecular dynamics

https://mdr.nims.go.jp/datasets/c6c01164-5bd6-402a-9287-0f596805ca0d

## File

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## Id

c6c01164-5bd6-402a-9287-0f596805ca0d

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2026-03-31T21:29:53.442760Z

## Updated at

2026-04-01T04:44:57.689454Z

## Published at

2026-04-01T07:26:13.960230Z

## Doi



## First published url

https://doi.org/10.1016/j.commatsci.2026.114568

## Date published

2026-02-07

## Recorded date published

2026-2

## Resource type

journal_article

## Manuscript type

vor

## Collection



## Title

- title: Active learning for predicting the enthalpy of mixing in binary liquids based
    on ab initio molecular dynamics
  title_type: original
  lang: en

## Description

- description: The enthalpy of mixing in the liquid phase is an important property
    for predicting phase formation in alloys. In multicomponent metallic liquids,
    it can be estimated from the binary interactions using a geometrical model, but
    data are available in less than a third of the binary systems. The prediction
    of this property in binary liquids is therefore important, and machine learning
    has recently achieved the highest accuracy. Further improvements requires acquiring
    high-quality data in liquids where models are poorly constrained. In this study,
    we propose an active learning approach to identify in which liquids additional
    data are most needed to improve an initial dataset that covers over 400 binary
    liquids. We identify a critical need for new data on liquids containing refractory
    elements, which we address by performing ab initio molecular dynamics simulations
    for 29 equimolar alloys of Ir, Os, Re and W. This enables more accurate predictions
    of the enthalpy of mixing, and we discuss the trends obtained for refractory elements
    of period 6. We use clustering analysis to interpret the results of active learning
    and to explore how our features can be linked to Miedema’s semi-empirical theory.
  description_type: abstract
  lang: und

## Creator

- name: Quentin Bizot
  role: author
  orcid: https://orcid.org/0000-0002-4314-2266
- name: Ryo Tamura
  role: author
  orcid: https://orcid.org/0000-0002-0349-358X
- name: Guillaume Deffrennes
  role: author
  orcid: https://orcid.org/0000-0002-3752-2537

## Contact agent



## Publisher

organization: Elsevier BV

## Managing organization



## Keyword

- subject: CALPHAD
  schema: not_defined
- subject: Machine learning
  schema: not_defined

## Rights

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

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## Journal

- title: Computational Materials Science
  issn: '09270256'
  volume: '266'
  article_number: '114568'

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## Funding

- identifier: ANR-19-P3IA-0003
  funder_name: Agence nationale de la recherche
- identifier: ANR-23-IACL-0006
  funder_name: Agence nationale de la recherche
- identifier: AD010914852R1
  funder_name: Centre Informatique National de l’Enseignement Supérieur
- identifier: AD010914852
  funder_name: Centre Informatique National de l’Enseignement Supérieur
- identifier: 25K01492
  funder_name: Japan Society for the Promotion of Science

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## Fileset

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  filename: 1-s2.0-S092702562600087X-main.pdf
  content_type: application/pdf
  size: 3570005
  md5: 0e0b055e26020a1cb6e5f07bcf057d8a

## Thumbnail

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filename: 1-s2.0-S092702562600087X-main.pdf