Accelerating Ionizable Lipid Discovery for mRNA Delivery Using Machine Learning and Combinatorial Chemistry

Chemistry, Computational Chemistry, Information Science, Artificial Intelligence, Life Sciences, Biochemistry, Bioengineering,
mRNA delivery   machine learning   ionizable lipids   combinatorial chemistry   lipid nanoparticles  

Accelerating the Discovery of Ionizable Lipids for mRNA Delivery using Machine Learning and Combinatorial Chemistry Research Background To fully realize the potential of mRNA therapies, it is essential to expand the toolkit of lipid nanoparticles (LNPs). However, a key bottleneck in LNP development is identifying new ionizable lipids. Previous stud...

Correlated Electron-Nuclear Dynamics of Photoinduced Water Dissociation on Rutile TiO2

Correlated Electron-Nuclear Dynamics of Photoinduced Water Dissociation on Rutile TiO2

Physics, Condensed Matter Physics, Chemistry, Physical Chemistry, Computational Chemistry, Materials Science, Inorganic Non-Metallic Materials,
electron-nuclear dynamics   photocatalysis   water dissociation   rutile TiO2   first-principles dynamics   photoexcited carriers  

Electron-Nucleus Dynamics Study of Photocatalytic Water Splitting on Rutile Titanium Dioxide Surface Background and Motivation Photocatalytic water splitting is one of the important applications of photocatalytic technology, while titanium dioxide (TiO₂) is a photocatalytic material with significant application potential. Although TiO₂ performs rem...

Exciton Polaron Formation and Hot-Carrier Relaxation in Rigid Dion–Jacobson-Type Two-Dimensional Perovskites

Physics, Optics, Condensed Matter Physics, Chemistry, Physical Chemistry, Computational Chemistry, Materials Science, Inorganic Non-Metallic Materials,
Dion–Jacobson type   two-dimensional perovskites   exciton polaron   hot-carrier   exciton-phonon coupling   time-resolved spectroscopy   functional materials  

Study Report on the Formation of Exciton Polarons and High Carrier Relaxation in Rigid Dion–Jacobson Type Two-Dimensional Perovskite Two-dimensional organic-inorganic hybrid perovskites (HOIPs) have garnered widespread attention due to their strongly confined exciton states and reduced dielectric screening effects resulting from their two-dimension...

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...