Elements: FourPhonon: A Computational Tool for Higher-Order Phonon Anharmonicity and Thermal Properties

材料的导热性在许多新兴应用中都很重要，例如半导体器件的热管理、建筑物的绝缘材料、热障涂层和热电废热回收。热是由声子携带的，这是晶格振动的量子力学描述。传统上，热导率被认为是由涉及三个声子的散射过程控制的，但最近发现，涉及四个声子的散射过程可以发挥重要甚至主导作用。然而，由于复杂的公式和巨大的计算成本，即使对于最简单的材料，预测四声子散射和由此产生的导热性也是极具挑战性的。为了应对这些挑战，该项目旨在开发和优化开源计算包FourPhonon，使感兴趣的用户能够为他们的材料和应用执行此类计算。基于GPU和机器学习的方法也将被开发，以显着加快计算速度。该项目将在未来十年将四声子散射从一项突破转变为学术界和工业界的一项新的常规能力。该项目的目标是增强FourPhonon，这是一个由PI领导的团队部署的开源代码，可用于预测四声子散射率和由此产生的导热性。自从FourPhonon第一版发布以来，它已经被世界各地的许多研究人员用于他们的材料和应用。然而，计算方法的升级需要跟上理论的进步，并且考虑到巨大甚至无法承受的计算成本，计算的加速是必要的。在本提案中，研究者将通过增强四声子来满足这些需求。对于基础版本，该项目将：(1)开发一个可以实现温度相关力常数的界面，这将使声子重正化和相变现象能够包含在内；(2)实现三声子和四声子散射通道的完整迭代方案。对于高级特征，该项目将：(1)通过异构计算使用GPU并行化加速四声子散射的计算，(2)通过基于一小部分散射过程数据集训练的机器学习模型加速计算。改进的FourPhonon封装将能够准确和负担得起的预测大量技术上重要的材料的导热性。该合同由先进网络基础设施办公室授予，并得到了工程理事会化学、生物工程、环境和运输系统部门的联合支持。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Thermal conductivity of materials is important in many emerging applications, such as thermal management of semiconductor devices, insulation materials for buildings, thermal barrier coatings, and thermoelectric waste heat recovery. Heat is carried by phonons, the quantum mechanical description of lattice vibration. Conventionally, thermal conductivity was considered to be controlled by the scattering processes that involve three phonons, but recently it has been discovered that the scattering processes that involve four phonons can play a significant or even leading role. Predicting four-phonon scattering and the resulting thermal conductivity, however, is extremely challenging due to the complex formulation and tremendous computational cost even for the simplest materials. To address these challenges, this project is aimed at the development and optimization of an open-source computational package, FourPhonon, to enable interested users to perform such calculations for their materials and applications. Approaches based on GPU and machine learning will also be developed to significantly accelerate the speed of computation. The project will transform four-phonon scattering from a breakthrough to a new routine capability for academia and industry in the coming decade.The objective of this project is to enhance FourPhonon, an open-source code that was deployed by a team led by the PI and can be used to predict four-phonon scattering rates and the resulting thermal conductivity. Since the release of the first version of FourPhonon, it has been used by many researchers worldwide for their materials and applications. However, upgrades in computational methods are needed to keep up with theoretical advances, and acceleration of computation is necessary considering the large or even unaffordable computational cost. In this proposal, the investigators will fulfill these needs by enhancing FourPhonon. For the base version, the project will: (1) develop an interface that can implement temperature-dependent force constants, which will enable the capability of the inclusion of phonon renormalization and phase transition phenomena, and (2) enable the full iterative scheme of both three- and four-phonon scattering channels. For the advanced features, the project will: (1) accelerate the computation of four-phonon scattering using GPU parallelization via heterogeneous computing, and (2) accelerate the computation via machine learning models that are trained on datasets of a small fraction of the scattering processes. The improved FourPhonon package will enable accurate and affordable prediction of thermal conductivity of a large number of materials that are technologically significant.This award by the Office of Advanced Cyberinfrastructure is jointly supported by the Division of Chemical, Bioengineering, Environmental, and Transport Systems within the Directorate for Engineering.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.