研究人员使用FermiNet神经网络架构解决了量子力学的基本方程，为实际系统提供准确的计算能力。这项工作对于理解化学键和原子能量等问题具有重要意义，并有望在计算机模拟中为新材料和化学合成提供实用性应用。研究人员希望通过开源代码和人工智能研究成果，解决基础科学问题。

Using deep learning to solve fundamental problems in computational quantum chemistry and explore how matter interacts with light

Using deep learning to solve fundamental problems in computational quantum chemistry and explore how matter interacts with light

Using deep learning to solve fundamental problems in computational quantum chemistry and explore how matter interacts with light

Using deep learning to solve fundamental problems in computational quantum chemistry and explore how matter interacts with light

Using deep learning to solve fundamental problems in computational quantum chemistry and explore how matter interacts with light

Using deep learning to solve fundamental problems in computational quantum chemistry and explore how matter interacts with light

Using deep learning to solve fundamental problems in computational quantum chemistry and explore how matter interacts with light

Using deep learning to solve fundamental problems in computational quantum chemistry and explore how matter interacts with light

Using deep learning to solve fundamental problems in computational quantum chemistry and explore how matter interacts with light

Using deep learning to solve fundamental problems in computational quantum chemistry and explore how matter interacts with light

研究者开发了FermiNet神经网络，利用深度学习有效模拟电子的量子状态，特别是在计算分子能量和处理激发态时表现出色。这一方法为材料科学和化学合成提供了新的计算工具，推动了量子化学的发展。