323 citations · 680 across the 7 of their papers we have counts for
7 papers
An end-to-end attention-based approach for learning on graphs
David Buterez, Jon Paul Janet, Dino Oglic +1
There has been a recent surge in transformer-based architectures for learning on graphs, mainly motivated by attention as an effective learning mechanism and the desire to supersed…
Graph Neural Networks with Adaptive Readouts
David Buterez, Jon Paul Janet, Steven J. Kiddle +2
An effective aggregation of node features into a graph-level representation via readout functions is an essential step in numerous learning tasks involving graph neural networks. T…
Ligand additivity relationships enable efficient exploration of transition metal chemical space
Naveen Arunachalam, Stefan Gugler, Michael G. Taylor +7
To accelerate exploration of chemical space, it is necessary to identify the compounds that will provide the most additional information or value. A large-scale analysis of mononuc…
Representations and Strategies for Transferable Machine Learning Models in Chemical Discovery
Daniel R. Harper, Aditya Nandy, Naveen Arunachalam +3
Strategies for machine-learning(ML)-accelerated discovery that are general across materials composition spaces are essential, but demonstrations of ML have been primarily limited t…
Recovering the flat-plane condition in electronic structure theory at semi-local DFT cost
Akash Bajaj, Jon Paul Janet, Heather J. Kulik
The flat plane condition is the union of two exact constraints in electronic structure theory: i) energetic piecewise linearity with fractional electron removal or addition and ii)…
Resolving transition metal chemical space: feature selection for machine learning and structure-property relationships
Jon Paul Janet, Heather J. Kulik
Machine learning (ML) of quantum mechanical properties shows promise for accelerating chemical discovery. For transition metal chemistry where accurate calculations are computation…