深度学习的电子结构计算
Zechen Tang1, Haoxiang Chen2, Yang Li1
1State Key Laboratory of Low Dimensional Quantum Physics and Department of Physics, Tsinghua University, Beijing, China.
深度学习正在克服电子结构计算中的准确性-效率权衡. 像深度学习量子蒙特卡洛和深度学习密度函数理论这样的新方法使材料科学和化学中的更复杂和更大规模的模拟成为可能.
科学领域:
- 物理,化学和材料科学 物理,化学和材料科学
背景情况:
- 第一原则电子结构计算对于科学发现至关重要.
- 他们的进步受到准确性-效率困境的限制.
研究的目的:
- 突出深度学习方法,解决电子结构计算中的准确性-效率挑战.
- 展示这些方法如何扩展量子力学模拟的功能.
主要方法:
- 深度学习量子蒙特卡洛用于准确的相关电子研究.
- 深度学习密度函数理论用于高效的大规模材料模拟.
主要成果:
- 深度学习的突破解决了准确性-效率困境.
- 新的方法使准确和高效的电子结构计算成为可能.
结论:
- 深度学习显著提高了第一原则计算的规模和复杂性.
- 这些进步放大了量子力学在科学发现中的影响.
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