79 citations · 84 across the 5 of their papers we have counts for
5 papers
Understanding and improving transferability in machine-learned activation energy predictors
Joe Gilkes, Mark Storr, Reinhard J. Maurer +1
The calculation of reactive properties is a challenging task in chemical reaction discovery. Machine learning (ML) methods play an important role in accelerating electronic structu…
Structural bias in three-dimensional autoregressive generative machine learning of organic molecules
Zsuzsanna Koczor-Benda, Joe Gilkes, Francesco Bartucca +2
A range of generative machine learning models for the design of novel molecules and materials have been proposed in recent years. Models that can generate three-dimensional structu…
Generative design of functional organic molecules for terahertz radiation detection
Zsuzsanna Koczor-Benda, Shayantan Chaudhuri, Joe Gilkes +3
Plasmonic nanocavities are molecule-nanoparticle junctions that offer a promising approach to upconvert terahertz radiation into visible or near-infrared light, enabling nanoscale…
Predicting long timescale kinetics under variable experimental conditions with Kinetica.jl
Joe Gilkes, Mark Storr, Reinhard J. Maurer +1
Predicting the degradation processes of molecules over long timescales is a key aspect of industrial materials design. However, it is made computationally challenging by the need t…
High-throughput property-driven generative design of functional organic molecules
Julia Westermayr, Joe Gilkes, Rhyan Barrett +1
The design of molecules and materials with tailored properties is challenging, as candidate molecules must satisfy multiple competing requirements that are often difficult to measu…