本研究利用势分布理论形式，提出了分子结合Alchemical Transfer Method的解析描述，并在SAMPL8基准集上验证了其预测转移能量概率密度的准确性，证实了alchemical transfer与双解耦过程的等价性。
摘要：
我们利用势分布理论（PDT）形式，提出了分子结合的Alchemical Transfer Method（ATM）的解析描述。ATM通过平移配体坐标来映射复合物的结合态和非结合态，从而对结合自由能进行建模。PDT将自由能和沿alchemical路径的微扰能量概率密度与初始状态的概率密度联系起来，在结合过程中，初始状态即复合物的非结合态。因此，非结合态下转移能量的ATM概率密度，首先通过配体与溶剂耦合及与溶剂化受体耦合的概率密度的卷积运算来关联——这些耦合具有可用的解析描述，其参数通过对双解耦alchemical计算数据的最大似然分析获得。随后，利用PDT将这一解析描述沿alchemical转移路径进行扩展。我们在SAMPL8基准集的TEMOA宿主对五个客体的alchemical结合上测试了该理论。在每个案例中，通过数值计算获得的沿alchemical转移路径的转移微扰能量概率密度，均与理论及双解耦模拟预测的结果相匹配。这项工作为alchemical transfer提供了坚实的理论基础，提供了关于alchemical transfer计算中观察到的概率密度形式的物理见解，并证实了alchemical transfer与双解耦过程在概念和数值上的等价性。
英文摘要：
We present an analytical description of the Alchemical Transfer Method (ATM) for molecular binding using the Potential Distribution Theory (PDT) formalism. ATM models the binding free energy by mapping the bound and unbound states of the complex by translating the ligand coordinates. PDT relates the free energy and the probability densities of the perturbation energy along the alchemical path to the probability density at the initial state, which is the unbound state of the complex in the case of a binding process. Hence, the ATM probability density of the transfer energy at the unbound state is first related by a convolution operation of the probability densities for coupling the ligand to the solvent and coupling it to the solvated receptor--for which analytical descriptions are available--with parameters obtained from maximum likelihood analysis of data from double-decoupling alchemical calculations. PDT is then used to extend this analytical description along the alchemical transfer pathway. We tested the theory on the alchemical binding of five guests to the TEMOA host from the SAMPL8 benchmark set. In each case, the probability densities of the perturbation energy for transfer along the alchemical transfer pathway obtained from numerical calculations match those predicted from the theory and double-decoupling simulations. The work provides a solid theoretical foundation for alchemical transfer, offers physical insights on the form of the probability densities observed in alchemical transfer calculations, and confirms the conceptual and numerical equivalence between the alchemical transfer and double-decoupling processes.
Alchemical harmonic approximation based potential for iso-electronic diatomics: Foundational baseline for $Δ$-machine learning
Technische Universität Berlin（柏林工业大学）；Vector Institute for Artificial Intelligence（向量人工智能研究所）；University of Toronto（多伦多大学）；Berlin Institute for the Foundations of Learning and Data（柏林学习与数据基础研究所）；Acceleration Consortium, University of Toronto（多伦多大学加速联盟）
Machine learning potentials from transfer learning of periodic correlated electronic structure methods: Application to liquid water with AFQMC, CCSD, and CCSD(T)
Quasi-classical Trajectory Calculations on a Two-state Potential Energy Surface Including Nonadiabatic Coupling Terms as Friction for D+ + H2 Collisions
