耦合簇与莫勒：用于神经波函数的分子轨道学习
Coupled Cluster con MōLe: Molecular Orbital Learning for Neural Wavefunctions
AI总结：
MōLe通过直接预测耦合簇的核心数学对象，实现高效分子轨道学习，提升量子化学计算的准确性和泛化能力。
摘要：
密度泛函理论（DFT）是计算分子性质最广泛使用的方法；然而，其准确性往往不足以进行定量预测。耦合簇（CC）理论是实现超越DFT准确性的最成功的方法，并且能够预测与实验高度一致的性质。它被称为量子化学的“黄金标准”。不幸的是，CC的高计算成本限制了其广泛应用。在本工作中，我们提出了分子轨道学习（MōLe）架构，这是一种等变机器学习模型，可以直接从平均场Hartree-Fock分子轨道作为输入预测CC的核心数学对象，即激发幅度。我们测试了模型的各种方面，并展示了其卓越的数据效率和对更大分子和非平衡几何结构的泛化能力，尽管仅在小平衡几何结构上进行训练。最后，我们还检查了其减少CC计算收敛所需循环次数的能力。MōLe可以为基于高精度波函数的机器学习架构奠定基础，以加速分子设计并补充力场方法。
英文摘要：
Density functional theory (DFT) is the most widely used method for calculating molecular properties; however, its accuracy is often insufficient for quantitative predictions. Coupled-cluster (CC) theory is the most successful method for achieving accuracy beyond DFT and for predicting properties that closely align with experiment. It is known as the ''gold standard'' of quantum chemistry. Unfortunately, the high computational cost of CC limits its widespread applicability. In this work, we present the Molecular Orbital Learning (MōLe) architecture, an equivariant machine learning model that directly predicts CC's core mathematical objects, the excitation amplitudes, from the mean-field Hartree-Fock molecular orbitals as inputs. We test various aspects of our model and demonstrate its remarkable data efficiency and out-of-distribution generalization to larger molecules and off-equilibrium geometries, despite being trained only on small equilibrium geometries. Finally, we also examine its ability to reduce the number of cycles required to converge CC calculations. MōLe can set the foundations for high-accuracy wavefunction-based ML architectures to accelerate molecular design and complement force-field approaches.
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