论文标题
通过扩散的蒙特卡洛在神经网络上朝分子的基态
Towards the ground state of molecules via diffusion Monte Carlo on neural networks
论文作者
论文摘要
基于固定节点近似的扩散蒙特卡洛（DMC）在过去几十年中已经取得了重大发展，并在需要准确的分子和材料的基态能量时成为一种首选方法之一。其余的瓶颈是不准确的淋巴结结构的局限性，禁止使用DMC解决更具挑战性的电子相关问题。在这项工作中，我们在固定节点DMC中应用了基于神经网络的试验波函数，该波函数允许精确计算不同电子特性的广泛的原子和分子系统。与使用各种蒙特卡洛（Monte Carlo）的最新神经网络方法相比，我们的方法的准确性和效率都优越。总体而言，该计算框架为准确解决相关电子波函数的解决方案提供了一种新的基准，并阐明了对分子的化学理解。
Diffusion Monte Carlo (DMC) based on fixed-node approximation has enjoyed significant developments in the past decades and become one of the go-to methods when accurate ground state energy of molecules and materials is needed. The remaining bottleneck is the limitations of the inaccurate nodal structure, prohibiting more challenging electron correlation problems to be tackled with DMC. In this work, we apply the neural-network based trial wavefunction in fixed-node DMC, which allows accurate calculation of a broad range of atomic and molecular systems of different electronic characteristics. Our method is superior in both accuracy and efficiency compared to state-of-the-art neural network methods using variational Monte Carlo. Overall, this computational framework provides a new benchmark for accurate solution of correlated electronic wavefunction and also shed light on the chemical understanding of molecules.
