Advancing Machine-Learning Augmented Free-Energy Density Functionals for Fast and Accurate Quantum Simulations of Warm Dense Plasmas

密度泛函理论（DFT）是多体物理学中最成功的方法之一，已经成为理解自然界物理和化学以及改善我们日常生活的主要工具之一。DFT的应用范围从指导实验人员发现近室温超导体和其他功能材料，到控制化学反应以获得更好的产品，以及设计治疗疾病的药物。DFT的成功依赖于描述材料中电子如何相互作用的近似的准确性，即所谓的交换相关（XC）自由能密度泛函和无轨道DFT的非相互作用自由能泛函。在这个研究项目中，将开发由机器学习推进的有限温度XC泛函和非相互作用自由能泛函，以显着提高DFT对等离子体物理和材料研究的预测能力。该研究项目的成果预计将在行星科学、天体物理学、聚变能和国防等各种科学领域和应用中产生重大影响，并通过提供工具来加速新材料的发现，对社会产生积极影响。温密物质，压力从数百万到数千亿个大气压，存在于宇宙中的各种物质--从行星核心和棕矮星和白色矮星等天体，到金刚石砧室压缩，再到实验室中产生的冲击和惯性约束聚变内爆。可靠地预测物质在这种极端条件下的静态，输运和光学性质取决于第一原理方法（如DFT）的准确性。该项目建立了一个研究计划，以提高DFT的精度和速度，用于极端材料的量子模拟。本项目的目标包括：（1）发展完全热化的、数值有效的XC自由能泛函，用于从头计算分子动力学模拟：（2）通过发展一类新的无轨道（OF）、无相互作用的自由能泛函，消除基于轨道的Mermin-Kohn-Sham（MKS）方案在高温下昂贵的瓶颈;（3）利用这些发展，加上机器学习技术，实现有效的OF-DFT实现，以加速电子结构计算，保持MKS的准确性;（4）应用这些先进的工具来回答高能量密度等离子体物理和极端材料科学中的关键问题。 该奖项由物理系等离子体物理项目和材料研究系凝聚态物质和材料理论项目共同支持。该奖项反映了NSF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。

Density-functional theory (DFT), one of the most successful methods in many-body physics, has been one of the main tools for understanding the physics and chemistry of nature as well as for improving our daily life. Examples of DFT applications range from guiding experimentalists to discover near-room-temperature superconductors and other functional materials, to controlling chemical reactions for better products, and to designing drugs to cure diseases. The success of DFT relies on the accuracy of approximations describing how electrons in a material interact with each other, the so-called exchange-correlation (XC) free-energy density functional and the non-interacting free-energy functional for orbital-free DFT. In this research project, finite-temperature XC-functionals and non-interacting free-energy functionals, advanced by machine learning, will be developed to significantly improve the predictive capability of DFT for both plasma physics and materials research. The outcome of this research project is expected to make significant impact in a variety of scientific fields and applications such as planetary science, astrophysics, fusion energy and national defense, as well as make a positive impact on the society through delivering tools to speed up discoveries of novel materials.Warm-dense matter, at pressures ranging from millions to hundreds of billions of atmospheres, exists vastly in the universe -- from planetary cores and astrophysical objects such as brown and white dwarfs, to diamond-anvil-cell compression, to shocks and inertial confinement fusion implosions created in a laboratory. Reliably predicting the static, transport and optical properties of matter at such extreme conditions depends on the accuracy of first-principles methods such as DFT. This project establishes a research program to improve the accuracy and speed of DFT for quantum simulations of extreme materials. The objectives of this project include: (1) developing fully thermalized and numerically efficient XC free-energy functionals for ab-initio molecular-dynamics simulations; (2) eliminating the prohibitively expensive bottleneck in the orbital-based Mermin-Kohn-Sham (MKS) scheme at elevated temperature by developing a novel class of orbital-free (OF) non-interacting free-energy functionals; (3) taking these developments, augmented with machine-learning techniques, to enable an efficient OF-DFT implementation for accelerating the electronic structure calculations that preserve the MKS level of accuracy; and (4) applying these advanced tools to answer key questions in high energy density plasma physics and in extreme materials science. This award is jointly supported by the Plasma Physics program in the Division of Physics and the Condensed Matter and Materials Theory program in the Division of Materials Research.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.