Accounting for the vibrational contribution to the configurational entropy in disordered solids with machine learned forcefields: a case study of garnet electrolyte Li7La3Zr2O12†

IF 2.9 3区 化学 Q3 CHEMISTRY, PHYSICAL
Jack Yang, Ziqi Yin and Sean Li
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Abstract

Accounting for lattice vibrations to accurately determine the phase stabilities of site-disordered solids is a long-standing challenge in computational material designs, due to the high computational cost associated with sampling the vast configurational space to obtain the converged thermodynamic quantities. One example is the garnet electrolyte Li7La3Zr2O12, the high-temperature and high-ion-mobility cubic phase of which is disordered in its Li+ site occupations, such that both the vibrational and configurational entropic contributions to its phase stability cannot be ignored. Understanding the subtle interplay between vibrational and configurational entropies in this material will therefore play a critical role in the rational manipulation of dopants and defects to stabilise cubic Li7La3Zr2O12 at room temperature for practical applications. Here, by developing machine learned forcefields based on an equivariant message-passing neural network SO3KRATES, we follow a strict statistical thermodynamic protocol to quantify the phase stability of cubic Li7La3Zr2O12 through structural optimisations, as well as molecular dynamics simulations at 300 and 1500 K, for a total of 70 120 configurations of cubic Li7La3Zr2O12. Although this only covers a tiny fraction of the configurational space (∼7 × 1034 configurations in total), we are able to deterministically show that the vibrational contributions to the total configurational free energy at 1500 K are significant (on the order of 1 eV per atom) in correctly ordering the stability of the cubic Li7La3Zr2O12 over its tetragonal counterpart, thanks to the high data efficiency, accuracy, stability and good transferability of the transformer-based equivariant network architecture behind SO3KRATES. Therefore, our work opens up new avenues to accelerate the accurate computational designs of disordered solids, such as solid electrolytes, for technologically important applications.

Abstract Image

用机器学习力场计算无序固体中振动对构型熵的贡献:以石榴石电解质Li7La3Zr2O12为例
计算晶格振动以准确确定位置无序固体的相稳定性是计算材料设计中一个长期存在的挑战,因为对巨大的构型空间进行采样以获得收敛的热力学量需要高昂的计算成本。其中一个例子是石榴石电解质Li7La3Zr2O12,其高温、高离子迁移率的立方相在Li+位置占据上是无序的,因此振动熵和构型熵对其相稳定性的贡献是不可忽视的。因此,了解这种材料中振动熵和构型熵之间的微妙相互作用,将在合理操纵掺杂剂和缺陷以在室温下稳定立方Li7La3Zr2O12的实际应用中发挥关键作用。本文基于等变消息传递神经网络SO3krates开发机器学习力场,遵循严格的统计热力学协议,通过结构优化以及300和1500 K下的分子动力学模拟,量化了立方Li7La3Zr2O12的70,120种构型。虽然这只覆盖了构型空间的一小部分(总共约7x10^34个构型),但我们能够确定地表明,在1500 K时,振动对总构型自由能的贡献是显著的(在1 eV/原子的数量级上),在正确排序立方Li7La3Zr2O12相对于其四边形对应物的稳定性方面,由于高数据效率,精度,SO3krates背后基于变压器的等变网络架构的稳定性和良好的可移植性。因此,我们的工作为加速无序固体(如固体电解质)的精确计算设计开辟了新的途径,用于技术上重要的应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Physical Chemistry Chemical Physics
Physical Chemistry Chemical Physics 化学-物理:原子、分子和化学物理
CiteScore
5.50
自引率
9.10%
发文量
2675
审稿时长
2.0 months
期刊介绍: Physical Chemistry Chemical Physics (PCCP) is an international journal co-owned by 19 physical chemistry and physics societies from around the world. This journal publishes original, cutting-edge research in physical chemistry, chemical physics and biophysical chemistry. To be suitable for publication in PCCP, articles must include significant innovation and/or insight into physical chemistry; this is the most important criterion that reviewers and Editors will judge against when evaluating submissions. The journal has a broad scope and welcomes contributions spanning experiment, theory, computation and data science. Topical coverage includes spectroscopy, dynamics, kinetics, statistical mechanics, thermodynamics, electrochemistry, catalysis, surface science, quantum mechanics, quantum computing and machine learning. Interdisciplinary research areas such as polymers and soft matter, materials, nanoscience, energy, surfaces/interfaces, and biophysical chemistry are welcomed if they demonstrate significant innovation and/or insight into physical chemistry. Joined experimental/theoretical studies are particularly appreciated when complementary and based on up-to-date approaches.
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