MolMiner: Toward Controllable, Three‐Dimensional‐Aware, Fragment‐Based Molecular Design
MolMiner is a fragment‐based, geometry‐aware, and order‐agnostic generative model for molecular design with strong inductive biases. Using symmetry‐aware fragment assembly, dynamic three‐dimensional geometry, and multi‐property conditioning, MolMiner enables interpretable and controllable molecular generation.
Raul Ortega‐Ochoa +2 more
wiley +1 more source
Comparing implicit boundary and hierarchical plurigaussian simulation for modeling geological domains using hard and soft data. [PDF]
Veliz V +5 more
europepmc +1 more source
Multimodal Learning with Rashomon Analysis for Battery Discharge Capacity Prediction
Multimodal fusion integrates composition, crystal‐structure, and radial‐distribution descriptors to predict battery discharge capacity. Rashomon analysis across near‐optimal models reveals that explanatory variation is structured rather than arbitrary, separating stable mechanistic signals from model‐contingent attributions and providing a more ...
Jue Gong +4 more
wiley +1 more source
From information to action- a phase-based analysis of patient engagement in inpatient fall prevention: A scoping review. [PDF]
Nishihara S +6 more
europepmc +1 more source
Materials Representation Learning Based on a Material–Motif Network and Heterogeneous Graphs
Structure motifs in materials are used to construct a bipartite material–motif network that links each material to its constituent motifs and establishes connectivity among materials sharing common motifs. Network analysis reveals material clusters associated with different functional applications and supports motif‐guided screening of materials.
Anoj Aryal +3 more
wiley +1 more source
How individuals formulate their beliefs about chronic musculoskeletal pain: introducing the dual implicit-explicit processing (DIP) model of pain belief formation - a qualitative exploration. [PDF]
Dunn M +3 more
europepmc +1 more source
Data‐Efficient Cycle‐Level Capacity Prediction Using 1D Deep Convolutional Network
We introduce DeepBat, a deep learning framework featuring a 1D convolutional backbone designed to extract latent degradation patterns from a microstructurally diverse electrode dataset. By learning complex formulation–performance relationships, the model accurately predicts long‐term specific discharge capacity using limited early‐cycle data, providing
Tao Huang +16 more
wiley +1 more source
Reconciling the Formal and Hidden Curricula in General Practice Training. [PDF]
Munro R.
europepmc +1 more source
Crystal Structure Prediction of Inorganic Materials: A Benchmark and Modern Evaluation
Predicting a crystal’s structure from composition alone is a long‐standing challenge in materials discovery. The CSP180 benchmark of 180 inorganic crystals evaluates thirteen crystal structure prediction algorithms requiring no density functional theory (DFT) against DFT‐based baselines across twelve metrics.
Lai Wei +9 more
wiley +1 more source
Theatre-Neuroscience: a brief perspective on the use of neuroscientific techniques to measure interpersonal coordination and the sense of self in theatrical contexts. [PDF]
Greaves DA.
europepmc +1 more source

