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Research 12:00 pm - 12:40 pm 15th June 2021
Timezone: BST (Europe/London)
In-Person Session
Curated with: Springer Nature

Machine learning & the quantum mechanics of materials and molecules

Determining the stability of molecules and condensed phases is the cornerstone of atomistic modeling, underpinning our understanding of chemical and materials properties and transformations. What if a machine-learning model can provide a unified framework to predict atomic-scale properties?

A universal framework provides new insight into the potential energy surface of materials and molecules.In this session we explore what the advance in ML means for different scientific areas in the next 10 years.

Gabor CsanyiProfessor of Molecular ModellingCambridge UniversityEdward GrantChief Science OfficerRahko
Alice AllenPostdoctoral Research AssociateUniversity of Luxembourg
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