Pfau David, Axelrod Simon, Sutterud Halvard, von Glehn Ingrid, Spencer James S
Google DeepMind, London N1C 4DJ, UK.
Department of Physics, Imperial College London, South Kensington Campus, London SW7 2AZ, UK.
Science. 2024 Aug 23;385(6711):eadn0137. doi: 10.1126/science.adn0137.
We present an algorithm to estimate the excited states of a quantum system by variational Monte Carlo, which has no free parameters and requires no orthogonalization of the states, instead transforming the problem into that of finding the ground state of an expanded system. Arbitrary observables can be calculated, including off-diagonal expectations, such as the transition dipole moment. The method works particularly well with neural network ansätze, and by combining this method with the FermiNet and Psiformer ansätze, we can accurately recover excitation energies and oscillator strengths on a range of molecules. We achieve accurate vertical excitation energies on benzene-scale molecules, including challenging double excitations. Beyond the examples presented in this work, we expect that this technique will be of interest for atomic, nuclear, and condensed matter physics.
我们提出了一种通过变分蒙特卡罗方法来估计量子系统激发态的算法，该算法没有自由参数，且无需对态进行正交化，而是将问题转化为寻找一个扩展系统基态的问题。可以计算任意可观测量，包括非对角期望值，如跃迁偶极矩。该方法与神经网络波函数特别适配，通过将此方法与费米网络（FermiNet）和Psiformer波函数相结合，我们能够在一系列分子上准确恢复激发能和振子强度。我们在苯尺度的分子上实现了精确的垂直激发能，包括具有挑战性的双激发。除了本工作中给出的例子，我们预计该技术将对原子物理、核物理和凝聚态物理领域具有吸引力。
