AI Medical Compendium Journal:
The Journal of chemical physics

Showing 1 to 10 of 66 articles

Emergence of accurate atomic energies from machine-learned noble-gas potentials.

The Journal of chemical physics
The quantum theory of atoms in molecules gives access to well-defined local atomic energies. Due to their locality, these energies are potentially interesting in fitting atomistic machine learning models as they inform about physically relevant prope...

Precision, intelligence, and a new paradigm for chemical research.

The Journal of chemical physics
Chemists have long struggled to precisely regulate and create substances, often relying on trial-and-error methods that are inefficient for complex, high-dimensional research challenges. However, recent advancements in computational and experimental ...

Learning transition path and membrane topological signatures in the folding pathway of bacteriorhodopsin (BR) fragment with artificial intelligence.

The Journal of chemical physics
Membrane protein folding in the viscous microenvironment of a lipid bilayer is an inherently slow process that challenges experiments and computational efforts alike. The folding kinetics is moreover associated with topological modulations of the bio...

Grand canonical Monte Carlo and deep learning assisted enhanced sampling to characterize the distribution of Mg2+ and influence of the Drude polarizable force field on the stability of folded states of the twister ribozyme.

The Journal of chemical physics
Molecular dynamics simulations are crucial for understanding the structural and dynamical behavior of biomolecular systems, including the impact of their environment. However, there is a gap between the time scale of these simulations and that of rea...

Machine learning assisted sorting of active microswimmers.

The Journal of chemical physics
Active matter systems, being in a non-equilibrium state, exhibit complex behaviors, such as self-organization, giving rise to emergent phenomena. There are many examples of active particles with biological origins, including bacteria and spermatozoa,...

Deep learning path-like collective variable for enhanced sampling molecular dynamics.

The Journal of chemical physics
Several enhanced sampling techniques rely on the definition of collective variables to effectively explore free energy landscapes. The existing variables that describe the progression along a reactive pathway offer an elegant solution but face a numb...

Emulating biological synaptic characteristics of HfOx/AlN-based 3D vertical resistive memory for neuromorphic systems.

The Journal of chemical physics
Here, we demonstrate double-layer 3D vertical resistive random-access memory with a hole-type structure embedding Pt/HfOx/AlN/TiN memory cells, conduct analog resistive switching, and examine the potential of memristors for use in neuromorphic system...

Neural potentials of proteins extrapolate beyond training data.

The Journal of chemical physics
We evaluate neural network (NN) coarse-grained (CG) force fields compared to traditional CG molecular mechanics force fields. We conclude that NN force fields are able to extrapolate and sample from unseen regions of the free energy surface when trai...

Toward a general neural network force field for protein simulations: Refining the intramolecular interaction in protein.

The Journal of chemical physics
Molecular dynamics (MD) is an extremely powerful, highly effective, and widely used approach to understanding the nature of chemical processes in atomic details for proteins. The accuracy of results from MD simulations is highly dependent on force fi...

Combining machine learning and molecular simulations to predict the stability of amorphous drugs.

The Journal of chemical physics
Amorphous drugs represent an intriguing option to bypass the low solubility of many crystalline formulations of pharmaceuticals. The physical stability of the amorphous phase with respect to the crystal is crucial to bring amorphous formulations into...