The Flow has not summarised this story yet — read it at arXiv Machine Learning.
arXiv:2609.21151v1 Announce Type: cross Abstract: Molecular solubility directly affects key aspects of molecular development such as reaction feasibility, formulation performance, separation efficien...
The paper presents a hierarchical deep‑learning framework that integrates compositional, structural, and transport models to screen solid‑state electrolytes. Four modules—L‑G‑DCNN, DenseGNN, MatterSim, and DeePMD—coordinate to evaluate thermodynamics, multi‑property performance, and kinetic transport, outperforming existing methods. Applied to over 30 million candidates, the workflow identifies 97 high‑performance materials, mainly halides, and links Li⁺ jump‑network connectivity to ionic conductivity while highlighting limits for oxide electrolytes.
arXiv:2606. 24983v1 Announce Type: cross Abstract: Implicit solvent machine learning potentials (MLPs) offer a powerful route to bridging the gap between accuracy and efficiency in molecular simulations.
arXiv:2610.02574v1 Announce Type: new Abstract: High-fidelity solubility prediction is fundamental to pharmaceutical development and environmental partitioning, where accurate modeling must couple mo...
arXiv:2608.23874v1 Announce Type: cross Abstract: Predicting thermal stability during handling and storage is essential for the design of safe and reliable energetic materials. However, experimental...
High-fidelity solubility prediction is fundamental to pharmaceutical development and environmental partitioning, where accurate modeling must couple molecular structure with thermodynamic behavior acr...