AI Medical Compendium Journal:
The Journal of chemical physics

Showing 41 to 50 of 66 articles

Assessing the persistence of chalcogen bonds in solution with neural network potentials.

The Journal of chemical physics
Non-covalent bonding patterns are commonly harvested as a design principle in the field of catalysis, supramolecular chemistry, and functional materials to name a few. Yet, their computational description generally neglects finite temperature and env...

Desynchronous learning in a physics-driven learning network.

The Journal of chemical physics
In a neuron network, synapses update individually using local information, allowing for entirely decentralized learning. In contrast, elements in an artificial neural network are typically updated simultaneously using a central processor. Here, we in...

Machine learned calibrations to high-throughput molecular excited state calculations.

The Journal of chemical physics
Understanding the excited state properties of molecules provides insight into how they interact with light. These interactions can be exploited to design compounds for photochemical applications, including enhanced spectral conversion of light to inc...

AutoSolvate: A toolkit for automating quantum chemistry design and discovery of solvated molecules.

The Journal of chemical physics
The availability of large, high-quality datasets is crucial for artificial intelligence design and discovery in chemistry. Despite the essential roles of solvents in chemistry, the rapid computational dataset generation of solution-phase molecular pr...

Identifying nonadditive contributions to the hydrophobicity of chemically heterogeneous surfaces via dual-loop active learning.

The Journal of chemical physics
Hydrophobic interactions drive numerous biological and synthetic processes. The materials used in these processes often possess chemically heterogeneous surfaces that are characterized by diverse chemical groups positioned in close proximity at the n...

Progress in deep Markov state modeling: Coarse graining and experimental data restraints.

The Journal of chemical physics
Recent advances in deep learning frameworks have established valuable tools for analyzing the long-timescale behavior of complex systems, such as proteins. In particular, the inclusion of physical constraints, e.g., time-reversibility, was a crucial ...

A deep autoencoder framework for discovery of metastable ensembles in biomacromolecules.

The Journal of chemical physics
Biomacromolecules manifest dynamic conformational fluctuation and involve mutual interconversion among metastable states. A robust mapping of their conformational landscape often requires the low-dimensional projection of the conformational ensemble ...

Improving the accuracy and convergence of drug permeation simulations via machine-learned collective variables.

The Journal of chemical physics
Understanding the permeation of biomolecules through cellular membranes is critical for many biotechnological applications, including targeted drug delivery, pathogen detection, and the development of new antibiotics. To this end, computer simulation...

Deep learning for nanopore ionic current blockades.

The Journal of chemical physics
DNA molecules can electrophoretically be driven through a nanoscale opening in a material, giving rise to rich and measurable ionic current blockades. In this work, we train machine learning models on experimental ionic blockade data from DNA nucleot...

Confronting pitfalls of AI-augmented molecular dynamics using statistical physics.

The Journal of chemical physics
Artificial intelligence (AI)-based approaches have had indubitable impact across the sciences through the ability to extract relevant information from raw data. Recently, AI has also found use in enhancing the efficiency of molecular simulations, whe...