Phase Segregation and Nanoconfined Fluid O2 in a Lithium-Rich Oxide Cathode

Chemistry, Nanoscience, Physical Chemistry, Computational Chemistry, Engineering, Energy Engineering, Materials Science, Inorganic Non-Metallic Materials,
Lithium-Rich Oxide Cathode   Phase Segregation   Nanoconfined Fluid   Lithium-Ion Batteries   Oxygen Redox   Structural Rearrangement  

Dynamic and Thermodynamic Study of Structural Changes in Lithium-Ion Battery Cathode Materials Academic Background and Research Motivation Lithium-ion batteries are a crucial power source for modern portable electronic devices and electric vehicles, traditionally using layered LiCoO2 cathode materials. However, the ongoing demand for high energy de...

Geometry-enhanced pretraining on interatomic potentials

Chemistry, Computational Chemistry, Information Science, Computer Science, Artificial Intelligence,
machine learning   molecular dynamics   graph neural networks   self-supervised learning   interatomic potentials  

Geometric Enhanced Pretraining for Interatomic Potentials Introduction Molecular dynamics (MD) simulations play an important role in fields such as physics, chemistry, biology, and materials science, providing insights into atomic-level processes. The accuracy and efficiency of MD simulations depend on the choice of interatomic potential functions ...

Equivariant 3D Conditional Diffusion Model for Molecular Linker Design

Equivariant 3D Conditional Diffusion Model for Molecular Linker Design

Chemistry, Medicinal Chemistry, Computational Chemistry, Information Science, Artificial Intelligence, Medicine, Pharmacy,
molecular linker design   drug design   generative model  

From early drug discovery researchers face a daunting challenge – to find drug-like candidate molecules among approximately 10^60 possible molecular structures. One successful solution is to start from smaller “fragment” molecules, a strategy known as fragment-based drug design (FBDD). In the FBDD process, the first step is to computationally scree...

Tandem mass spectrum prediction for small molecules using graph transformers

Chemistry, Analytical Chemistry, Computational Chemistry, Information Science, Artificial Intelligence,
Mass spectrometry   Graph transformer   Small molecules   Fragmentation patterns   Prediction  

This is a paper about MassFormer, a graph transformer model for small molecule mass spectrometry prediction. This research addresses the problem of molecular identification in mass spectrometry data and proposes a novel deep learning approach to predict mass spectra of small molecules. Background: Mass spectrometry (MS) is an analytical technique w...