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Property Prediction

Property prediction involves forecasting or estimating a molecule's inherent physical and chemical properties based on information derived from its structural characteristics. It facilitates high-throughput evaluation of an extensive array of molecular properties, enabling the virtual screening of compounds. Additionally, it provides the means to predict the unknown attributes of new molecules, thereby bolstering research efficiency and reducing development times.

Papers

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Showing 551–560 of 691 papers

TitleStatusHype
Why Deep Models Often cannot Beat Non-deep Counterparts on Molecular Property Prediction?—0
Xtal2DoS: Attention-based Crystal to Sequence Learning for Density of States Prediction—0
PDDFormer: Pairwise Distance Distribution Graph Transformer for Crystal Material Property Prediction—0
Reflections from the 2024 Large Language Model (LLM) Hackathon for Applications in Materials Science and ChemistryCode0
Contextualized Messages Boost Graph RepresentationsCode0
Equivariant Parameter Sharing for Porous Crystalline MaterialsCode0
Temporal graph models fail to capture global temporal dynamicsCode0
Equivariant Message Passing Neural Network for Crystal Material DiscoveryCode0
Enhancing Molecular Property Prediction with Auxiliary Learning and Task-Specific AdaptationCode0
Enhancing Molecular Property Prediction via Mixture of Collaborative ExpertsCode0
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