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Found 5 papers, 3 papers with code
Assessing the Extrapolation Capability of Template-Free Retrosynthesis Models
Despite the acknowledged capability of template-free models in exploring unseen reaction spaces compared to template-based models for retrosynthesis prediction, their ability to venture beyond established boundaries remains relatively uncharted.
A generalized-template-based graph neural network for accurate organic reactivity prediction
In addition to the built-in interpretability of the generalized reaction templates, the high score–accuracy correlation of the model allows users to assess the uncertainty of the machine predictions.
Deep Retrosynthetic Reaction Prediction using Local Reactivity and Global Attention
Our model shows a promising 89. 5 and 99. 2% round-trip accuracy at top-1 and top-5 predictions for the USPTO-50K dataset containing 50 016 reactions.
Ranked #7 on
Single-step retrosynthesis
on USPTO-50k
An invertible crystallographic representation for general inverse design of inorganic crystals with targeted properties
Realizing general inverse design could greatly accelerate the discovery of new materials with user-defined properties.
Catalyst design using actively learned machine with non-ab initio input features towards CO2 reduction reactions
In conventional chemisorption model, the d-band center theory (augmented sometimes with the upper edge of d-band for imporved accuarcy) plays a central role in predicting adsorption energies and catalytic activity as a function of d-band center of the solid surfaces, but it requires density functional calculations that can be quite costly for large scale screening purposes of materials.
