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SUMMARY:Modelling QM-accurate physical phenomena in alloys and polymers us
 ing Atomic Cluster Expansion (ACE) &amp\; Hyperactive Learning (HAL) - Cas
  van der Oord\, University of Cambridge
DTSTART:20221003T133000Z
DTEND:20221003T140000Z
UID:TALK182765@talks.cam.ac.uk
CONTACT:Dr Christoph Schran
DESCRIPTION: Data-driven interatomic potentials have become an indispensab
 le tool in the approximation of the potential energy surface with quantum 
 mechanical accuracy. One of the latest frameworks\, Atomic Cluster Expansi
 on (ACE)\, has reduced evaluation times to 0.1 ms/atom/core\, enabling res
 earchers to investigate length and time scales previously unfeasible while
  respecting the fundamental laws of nature. The ACE framework also allows 
 for excellent interpretability of the potential and allows modellers to ca
 refully study the relevance of body-ordered interactions in quantum many-b
 ody atomic systems.\nCentral to the development of interatomic potentials 
 is the generation of the training database. In order to aid this task Hype
 ractive Learning (HAL) has been developed to rapidly build ACE potentials 
 from scratch. HAL generates valuable training configurations by adding a b
 iasing term towards uncertainty\, in turn accelerating database generation
  by up to an order of magnitude compared to standard AL. General HAL proto
 cols for building ACE potentials for polymers and alloys will be presented
  and applications such as determining polymer density and predicting alloy
  phase transitions discussed. These studies will give insight into physica
 l phenomena such as precipitate formation and chemical ordering in alloys.
LOCATION:Goldsmiths 1\, Department of Materials Science &amp\; Metallurgy
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