Assessment and Ranking of Difluoromethane (R32) and Pentafluoroethane (R125) Interatomic Potentials Using Several Thermophysical and Transport Properties Across Multiple State Points,Journal of Chemical & Engineering Data

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Assessment and Ranking of Difluoromethane (R32) and Pentafluoroethane (R125) Interatomic Potentials Using Several Thermophysical and Transport Properties Across Multiple State Points
Journal of Chemical & Engineering Data ( IF 2.0 ) Pub Date : 2023-09-19 , DOI: 10.1021/acs.jced.3c00379
Barnabas Agbodekhe ₁ , Eliseo Marin-Rimoldi ₁ , Yong Zhang ₁ , Alexander W. Dowling ₁ , Edward J. Maginn ₁

Affiliation

Difluoromethane (R32) and pentafluoroethane (R125) are two common hydrofluorocarbon refrigerants, often used in a mixture termed R410A. Many refrigerants, including R32 and especially R125, have high global warming potentials and so are being phased out. There is a desire to develop processes that can separate and recover these materials, which means that there is a need to determine the thermodynamic and transport properties of these fluids. In this work, we evaluate the ability of molecular dynamics simulations to determine the key thermodynamic and transport properties of these two fluids. We test whether classical interatomic force fields (FFs) parametrized against vapor–liquid equilibrium (VLE) data using a machine learning directed (MLD) approach can also yield accurate estimates of other key properties. The top-performing MLD FFs tuned against VLE data were nearly indistinguishable based on VLE results. This work seeks to investigate if these MLD-tuned FFs are transferable to other properties not used in tuning them and if they can be ranked to identify the “best” FFs. Literature FFs, one each for R32 and R125, are included in the study. A total of ten FFs were tested. Thermal conductivity (λ), viscosity (η), self-diffusivity (D), liquid density (ρ), isobaric heat capacity (C_P), isochoric heat capacity (C_V), thermal expansivity (α_P), thermal pressure coefficient (γ_ρ), isothermal compressibility (β_T), speed of sound (c_sound), Joule-Thomson coefficient (μ_JT), and center of mass radial distribution functions (g_r) were computed using molecular dynamics and compared with experiments when possible. Somewhat surprisingly, the MLD-tuned FFs are found to be transferable to a wide range of properties not used in tuning them. The MLD-tuned FFs were ranked. The FFs labeled R32_a and R125_b were found to be the “best” FFs for R32 and R125, respectively, across a broad range of properties. The MLD-tuned FFs were found to be superior to previously developed literature FFs.

中文翻译：

使用跨多个状态点的几种热物理和传输特性对二氟甲烷 (R32) 和五氟乙烷 (R125) 原子间势进行评估和排序

二氟甲烷 (R32) 和五氟乙烷 (R125) 是两种常见的氢氟碳制冷剂，通常以称为 R410A 的混合物形式使用。许多制冷剂，包括 R32，尤其是 R125，具有很高的全球变暖潜力，因此正在逐步淘汰。人们希望开发能够分离和回收这些材料的工艺，这意味着需要确定这些流体的热力学和传输特性。在这项工作中，我们评估了分子动力学模拟确定这两种流体的关键热力学和输运特性的能力。我们测试使用机器学习导向（MLD）方法根据气液平衡（VLE）数据参数化的经典原子间力场（FF）是否也可以产生对其他关键属性的准确估计。根据 VLE 结果，根据 VLE 数据调整的性能最佳的 MLD FF 几乎无法区分。这项工作旨在研究这些 MLD 调整的 FF 是否可以转移到调整它们时未使用的其他属性，以及是否可以对它们进行排名以识别“最佳”FF。研究中包括 R32 和 R125 各一个的文献 FF。总共测试了十个 FF。导热系数(λ)、粘度(η)、自扩散系数( D )、液体密度(ρ)、等压热容( C _P )、等容热容( C _V )、热膨胀系数(α _P )、热压力系数(γ _ρ )、等温压缩性 (β _T )、声速 ( c _sound )、焦耳-汤姆逊系数 (μ _JT ) 和质心径向分布函数 ( gr ₎使用分子动力学计算，并与实验进行比较可能的。有点令人惊讶的是，MLD 调整的 FF 被发现可以转移到调整它们时未使用的广泛属性。对 MLD 调整的 FF 进行了排名。研究发现，在广泛的性能范围内，标记为 R32 _a和 R125 _{b 的}FF分别是 R32 和 R125 的“最佳”FF。经 MLD 调整的 FF 被发现优于之前开发的文献 FF。

更新日期：2023-09-19

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