AI 中文总结
研究酶促过渡态模拟中因多参考特征带来的挑战，提出专有PexMachina求解器，能在过渡态扫描中再现精确基态能量，解决了单参考方法在特定几何结构下难以达化学精度的问题。
AI 中文摘要
高度成功的药物，如过渡态抑制剂，是通过模拟酶促过渡态的精确几何结构设计的，这需要量子化学能在亚埃分辨率下精确指定分子结构。在这种量子化学模拟中，一个核心挑战是经典单参考方法在强相关过渡态下可能与精确能量有很大偏差。我们通过研究具有多参考过渡态的模型系统来研究多参考特征导致的分解。在这项工作中，我们提出了一种专有的PexMachina求解器，它在整个过渡态扫描中再现了精确的基态能量，而单参考方法在某些几何结构下难以达到化学精度。
英文摘要
Highly successful drugs, such as transition-state inhibitors, were designed by mimicking the precise geometry of enzymatic transition states, a strategy that requires quantum chemistry accurate enough to specify molecular structure at sub-Å resolution. A central challenge during such a quantum chemistry simulation is that classical single-reference method can deviate substantially from the exact energy at strongly correlated transition states. We study the breakdown due to multireference character by studying a model system with a multireference transition state. In this work we present a proprietary PexMachina solver which reproduces the exact ground-state energy throughout the transition state scan, while single-reference methods struggle for certain geometries to achieve chemical accuracy.
Comments6 pages, 4 figures
