7 citations · 16 across the 9 of their papers we have counts for
9 papers
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…
Deep Learning Models of the Discrete Component of the Galactic Interstellar Gamma-Ray Emission
Alexander Shmakov, Mohammadamin Tavakoli, Pierre Baldi +3
A significant point-like component from the small scale (or discrete) structure in the H2 interstellar gas might be present in the Fermi-LAT data, but modeling this emission relies…
Improved Modeling of the Discrete Component of the Galactic Interstellar Gamma-ray Emission and Implications for the Fermi-LAT Galactic Center Excess
Christopher M. Karwin, Alex Broughton, Simona Murgia +3
The aim of this work is to improve models for the gamma-ray discrete or small-scale structure related to H2 interstellar gas. Reliably identifying this contribution is important to…
Rxn Hypergraph: a Hypergraph Attention Model for Chemical Reaction Representation
Mohammadamin Tavakoli, Alexander Shmakov, Francesco Ceccarelli +1
It is fundamental for science and technology to be able to predict chemical reactions and their properties. To achieve such skills, it is important to develop good representations…
Tourbillon: a Physically Plausible Neural Architecture
Mohammadamin Tavakoli, Peter Sadowski, Pierre Baldi
In a physical neural system, backpropagation is faced with a number of obstacles including: the need for labeled data, the violation of the locality learning principle, the need fo…
Quantum Mechanics and Machine Learning Synergies: Graph Attention Neural Networks to Predict Chemical Reactivity
Mohammadamin Tavakoli, Aaron Mood, David Van Vranken +1
There is a lack of scalable quantitative measures of reactivity for functional groups in organic chemistry. Measuring reactivity experimentally is costly and time-consuming and doe…
SPLASH: Learnable Activation Functions for Improving Accuracy and Adversarial Robustness
Mohammadamin Tavakoli, Forest Agostinelli, Pierre Baldi
We introduce SPLASH units, a class of learnable activation functions shown to simultaneously improve the accuracy of deep neural networks while also improving their robustness to a…
Continuous Representation of Molecules Using Graph Variational Autoencoder
Mohammadamin Tavakoli, Pierre Baldi
In order to continuously represent molecules, we propose a generative model in the form of a VAE which is operating on the 2D-graph structure of molecules. A side predictor is empl…