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MH-PCTpro:用于快速预测压力-组成-温度 (PCT) 异热的机器学习模型

Ashwini Verma^1,2, Kavita Joshi^1,2

¹Physical and Materials Chemistry Division, CSIR-National Chemical Laboratory, Dr. Homi Bhabha Road, Pashan, Pune 411008, India.

|April 16, 2025

概括

一个新的机器学习模型,金属化物.

科学领域:

材料科学材料科学材料科学
计算化学的计算化学
化学工程是化学工程的重要组成部分.

背景情况:

准确预测金属化物压力-组成-温度 (PCT) 异热体对于设计固态储存材料至关重要.
实验性确定PCT异热体是耗时且资源密集的.
现有的计算方法可能缺乏用于广泛材料选的准确性或范围.

研究的目的:

开发和验证一种机器学习模型,MH-PCTpro,用于预测金属化物PCT异温体.
为了培训和强大的模型性能,利用一个全面的实验数据库.
为指导实验工作提供一个工具,并加速用于储存的材料的发现.

主要方法:

构建一个实验数据库,包含来自237个等温体和138个组合的14000多个PCT等温体数据点.
在这个广泛的数据集上训练一个机器学习模型 (MH-PCTpro).
在各种合金家族和温度范围 (263-653 K) 中验证模型.

主要成果:

MH-PCTpro准确地预测了各种金属组成的PCT异温.
该模型捕捉了平原压力的温度依赖的变化.
预测显示在广泛的材料中与实验结果一致.

结论:

MH-PCTpro 作为一个有价值的机器学习工具,用于预测 PCT 异温.
该模型有助于指导实验设计和选潜在的存材料.
它通过范特霍夫图表提供热力学见解,包括化物形成的和.

关键词:

计算材料科学计算材料科学能量材料是能源材料.机器学习机器学习

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