AI Medical Compendium Journal:
The journal of physical chemistry letters

Showing 31 to 40 of 52 articles

Significance of the Chemical Environment of an Element in Nonadiabatic Molecular Dynamics: Feature Selection and Dimensionality Reduction with Machine Learning.

The journal of physical chemistry letters
Using supervised and unsupervised machine learning (ML) on features generated from nonadiabatic (NA) molecular dynamics (MD) trajectories under the classical path approximation, we demonstrate that mutual information with the NA Hamiltonian can be us...

Proteome-Informed Machine Learning Studies of Cocaine Addiction.

The journal of physical chemistry letters
No anti-cocaine addiction drugs have been approved by the Food and Drug Administration despite decades of effort. The main challenge is the intricate molecular mechanisms of cocaine addiction, involving synergistic interactions among proteins upstrea...

A Frequency Domain Analysis of the Excitability and Bifurcations of the FitzHugh-Nagumo Neuron Model.

The journal of physical chemistry letters
The dynamics of neurons consist of oscillating patterns of a membrane potential that underpin the operation of biological intelligence. The FitzHugh-Nagumo (FHN) model for neuron excitability generates rich dynamical regimes with a simpler mathematic...

Artificial Neural Networks as Propagators in Quantum Dynamics.

The journal of physical chemistry letters
The utilization of artificial neural networks (ANNs) provides strategies for accelerating molecular simulations. Herein, ANNs are implemented as propagators of the time-dependent Schrödinger equation to simulate the quantum dynamics of systems with t...

Machine Learning Approach to Calculate Electronic Couplings between Quasi-diabatic Molecular Orbitals: The Case of DNA.

The journal of physical chemistry letters
Diabatization of one-electron states in flexible molecular aggregates is a great challenge due to the presence of surface crossings between molecular orbital (MO) levels and the complex interaction between MOs of neighboring molecules. In this work, ...

Stacked Ensemble Machine Learning for Range-Separation Parameters.

The journal of physical chemistry letters
Density functional theory-based high-throughput materials and drug discovery has achieved tremendous success in recent decades, but its power on organic semiconducting molecules suffered catastrophically from the self-interaction error until the none...

Targeted Free Energy Perturbation Revisited: Accurate Free Energies from Mapped Reference Potentials.

The journal of physical chemistry letters
We present an approach that extends the theory of targeted free energy perturbation (TFEP) to calculate free energy differences and free energy surfaces at an accurate quantum mechanical level of theory from a cheaper reference potential. The converg...

A Scalable Graph Neural Network Method for Developing an Accurate Force Field of Large Flexible Organic Molecules.

The journal of physical chemistry letters
An accurate force field is the key to the success of all molecular mechanics simulations on organic polymers and biomolecules. Accurate correlated wave function (CW) methods scale poorly with system size, so this poses a great challenge to the develo...

Nuclear Quantum Effect and Its Temperature Dependence in Liquid Water from Random Phase Approximation via Artificial Neural Network.

The journal of physical chemistry letters
We report structural and dynamical properties of liquid water described by the random phase approximation (RPA) correlation together with the exact exchange energy (EXX) within density functional theory. By utilizing thermostated ring polymer molecul...

Learning the Exciton Properties of Azo-dyes.

The journal of physical chemistry letters
The determination of electronic excited state (ES) properties is the cornerstone of theoretical photochemistry. Yet, traditional ES methods become impractical when applied to fairly large molecules, or when used on thousands of systems. Machine lear...