11 citations · 15 across the 4 of their papers we have counts for
4 papers
Interpretable Deep Learning for Polar Mechanistic Reaction Prediction
Ryan J. Miller, Alexander E. Dashuta, Brayden Rudisill +2
Accurately predicting chemical reactions is essential for driving innovation in synthetic chemistry, with broad applications in medicine, manufacturing, and agriculture. At the sam…
Full Event Particle-Level Unfolding with Variable-Length Latent Variational Diffusion
Alexander Shmakov, Kevin Greif, Michael James Fenton +3
The measurements performed by particle physics experiments must account for the imperfect response of the detectors used to observe the interactions. One approach, unfolding, stati…
Unraveling the Molecular Magic: AI Insights on the Formation of Extraordinarily Stretchable Hydrogels
Shahriar Hojjati Emmami, Ali Pilehvar Meibody, Lobat Tayebi +2
The deliberate manipulation of ammonium persulfate, methylenebisacrylamide, dimethyleacrylamide, and polyethylene oxide concentrations resulted in the development of a hydrogel wit…
AI for Interpretable Chemistry: Predicting Radical Mechanistic Pathways via Contrastive Learning
Mohammadamin Tavakoli, Yin Ting T. Chiu, Alexander Shmakov +3
Deep learning-based reaction predictors have undergone significant architectural evolution. However, their reliance on reactions from the US Patent Office results in a lack of inte…