Case study 1. Classical MDS analysis of CH2NH2+ dynamics

The data for classical MDS analysis of CH2NH2+ dynamic

Case study 2. Fréchet distance analysis of phytochromobilin

The data for Fréchet distance analysis of phytochromobili

Inclusion of Machine Learning Kernel Ridge Regression Potential Energy Surfaces in On-the-Fly Nonadiabatic Molecular Dynamics Simulation

We discuss a theoretical approach that employs machine learning potential energy surfaces (ML-PESs) in the nonadiabatic dynamics simulation of polyatomic systems by taking 6-aminopyrimidine as a typical example. The Zhu–Nakamura theory is employed in the surface hopping dynamics, which does not require the calculation of the nonadiabatic coupling vectors. The kernel ridge regression is used in the construction of the adiabatic PESs. In the nonadiabatic dynamics simulation, we use ML-PESs for most geometries and switch back to the electronic structure calculations for a few geometries either near the S₁/S₀ conical intersections or in the out-of-confidence regions. The dynamics results based on ML-PESs are consistent with those based on CASSCF PESs. The ML-PESs are further used to achieve the highly efficient massive dynamics simulations with a large number of trajectories. This work displays the powerful role of ML methods in the nonadiabatic dynamics simulation of polyatomic systems

Inclusion of Machine Learning Kernel Ridge Regression Potential Energy Surfaces in On-the-Fly Nonadiabatic Molecular Dynamics Simulation

We discuss a theoretical approach that employs machine learning potential energy surfaces (ML-PESs) in the nonadiabatic dynamics simulation of polyatomic systems by taking 6-aminopyrimidine as a typical example. The Zhu–Nakamura theory is employed in the surface hopping dynamics, which does not require the calculation of the nonadiabatic coupling vectors. The kernel ridge regression is used in the construction of the adiabatic PESs. In the nonadiabatic dynamics simulation, we use ML-PESs for most geometries and switch back to the electronic structure calculations for a few geometries either near the S₁/S₀ conical intersections or in the out-of-confidence regions. The dynamics results based on ML-PESs are consistent with those based on CASSCF PESs. The ML-PESs are further used to achieve the highly efficient massive dynamics simulations with a large number of trajectories. This work displays the powerful role of ML methods in the nonadiabatic dynamics simulation of polyatomic systems

A Highly Selective Implantable Electrochemical Fiber Sensor for Real-Time Monitoring of Blood Homovanillic Acid

Homovanillic acid (HVA) is a major dopamine metabolite, and blood HVA is considered as central nervous system (CNS) dopamine biomarker, which reflects the progression of dopamine-associated CNS diseases and the behavioral response to therapeutic drugs. However, facing blood various active substances interference, particularly structurally similar catecholamines and their metabolites, real-time and accurate monitoring of blood HVA remains a challenge. Herein, a highly selective implantable electrochemical fiber sensor based on a molecularly imprinted polymer is reported to accurately monitor HVA in vivo. The sensor exhibits high selectivity, with a response intensity to HVA 12.6 times greater than that of catecholamines and their metabolites, achieving 97.8% accuracy in vivo. The sensor injected into the rat caudal vein tracked the real-time changes of blood HVA, which paralleled the brain dopamine fluctuations and indicated the behavioral response to dopamine increase. This study provides a universal design strategy for improving the selectivity of implantable electrochemical sensors