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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
Low-cost machine learning approach to the prediction of transition metal phosphor excited state properties—0
Lyra: An Efficient and Expressive Subquadratic Architecture for Modeling Biological Sequences—0
Machine Learning - Driven Materials Discovery: Unlocking Next-Generation Functional Materials -- A minireview—0
Machine learning for accelerating effective property prediction for poroelasticity problem in stochastic media—0
Machine Learning for Material Characterization with an Application for Predicting Mechanical Properties—0
Material Microstructure Design Using VAE-Regression with Multimodal Prior—0
Material Property Prediction with Element Attribute Knowledge Graphs and Multimodal Representation Learning—0
MaterialsAtlas.org: A Materials Informatics Web App Platform for Materials Discovery and Survey of State-of-the-Art—0
A Materials Map Integrating Experimental and Computational Data via Graph-Based Machine Learning for Enhanced Materials Discovery—0
MatterChat: A Multi-Modal LLM for Material Science—0
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