108 citations · 108 across the 1 of their papers we have counts for
3 papers
Training atomic neural networks using fragment-based data generated in virtual reality
Silvia Amabilino, Lars A. Bratholm, Simon J. Bennie +2
The ability to understand and engineer molecular structures relies on having accurate descriptions of the energy as a function of atomic coordinates. Here we outline a new paradigm…
Training neural nets to learn reactive potential energy surfaces using interactive quantum chemistry in virtual reality
Silvia Amabilino, Lars A. Bratholm, Simon J. Bennie +3
Whilst the primary bottleneck to a number of computational workflows was not so long ago limited by processing power, the rise of machine learning technologies has resulted in a pa…
Adaptively accelerating reactive molecular dynamics using boxed molecular dynamics in energy space
Robin J Shannon, Silvia Amabilino, Mike OConnor +2
The problem of observing rare events is pervasive among the molecular dynamics community and an array of different types of methods are commonly used to accelerate these long times…