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SUMMARY:Exploring chemical reactions through automation and machine learni
 ng - Prof. Fernanda Duarte\, Department of Chemistry\, University of Oxfor
 d
DTSTART:20221128T140000Z
DTEND:20221128T143000Z
UID:TALK192950@talks.cam.ac.uk
CONTACT:Dr Christoph Schran
DESCRIPTION:Simulating chemical reactions is essential to developing a fun
 damental understanding of their mechanism and predicting experimental outc
 omes. Machine learned potentials (MLPs) offer an enticing approach to suc
 h simulations\, enabling the efficient mapping between nuclear configurati
 ons and energies. Moreover\, in contrast to classical force fields\, they 
 offer flexibility and systematic improvability. However\, despite the deve
 lopment of Gaussian Approximation Potentials (GAPs) and high dimensional n
 eural network potentials (NNPs) more than ten years ago\, MLPs are still y
 et to find routine use for chemical reaction simulation. This slow uptake 
 is likely due to the computational and time investment required to train r
 eactive potentials for new systems\, with only a handful of examples repor
 ted to date. \n\nIn this talk\, I will present our team’s efforts to tac
 kle these challenges by introducing efficient strategies to generate MLPs 
 to study reaction mechanisms in the gas-phase and solution. Our work demon
 strates that accurate potentials\, achieving ab initio accuracy\, can be
  generated employing only hundreds to a few thousand energy and gradient e
 valuations on a reference potential energy surface. I will also discuss th
 e performance of different methods to obtain reactive MLPs for small to me
 dium size reactions and discuss current limitations. I will finish by illu
 strating the power of the developed strategies in a diverse range of syste
 ms\, including reactions in solution and ambimodal surfaces\, as well as d
 ynamical quantities\, such as product ratios and free energies\, for which
  expensive AIMD simulations would otherwise be needed.  \n\nReferences\n\
 n1. T. A. Young\, T. Johnston-Wood\, V. Deringer\, F. Duarte. Chem. Sci.\,
  2021\,12\, 10944.\n\n2. T. A. Young\, T. Johnston-Wood\, H. Zhang\, F. Du
 arte. Phys. Chem. Chem. Phys.\, 2022\,24\, 20820
LOCATION:Zoom link: https://zoom.us/j/92447982065?pwd=RkhaYkM5VTZPZ3pYSHpt
 UXlRSkppQT09
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