4.5k citations · 5.9k across the 15 of their papers we have counts for
16 papers
Making Sense of Data in the Wild: Data Analysis Automation at Scale
Mara Graziani, Malina Molnar, Irina Espejo Morales +2
As the volume of publicly available data continues to grow, researchers face the challenge of limited diversity in benchmarking machine learning tasks. Although thousands of datase…
Quantum-centric Supercomputing for Materials Science: A Perspective on Challenges and Future Directions
Yuri Alexeev, Maximilian Amsler, Paul Baity +124
Computational models are an essential tool for the design, characterization, and discovery of novel materials. Hard computational tasks in materials science stretch the limits of e…
Predicting polymerization reactions via transfer learning using chemical language models
Brenda S. Ferrari, Matteo Manica, Ronaldo Giro +2
Polymers are candidate materials for a wide range of sustainability applications such as carbon capture and energy storage. However, computational polymer discovery lacks automated…
Language models in molecular discovery
Nikita Janakarajan, Tim Erdmann, Sarath Swaminathan +2
The success of language models, especially transformer-based architectures, has trickled into other domains giving rise to "scientific language models" that operate on small molecu…
Unifying Molecular and Textual Representations via Multi-task Language Modelling
Dimitrios Christofidellis, Giorgio Giannone, Jannis Born +3
The recent advances in neural language models have also been successfully applied to the field of chemistry, offering generative solutions for classical problems in molecular desig…
Unassisted Noise Reduction of Chemical Reaction Data Sets
Alessandra Toniato, Philippe Schwaller, Antonio Cardinale +2
Existing deep learning models applied to reaction prediction in organic chemistry can reach high levels of accuracy (> 90% for Natural Language Processing-based ones). With no chem…