As such, the mission of the group is to explore how the use of Machine Learning and other AI technologies can help scientists analyse the vast amounts of experimental data now being routinely generated by the large experimental facilities at the Rutherford Appleton Laboratory (RAL) and STFC’s Harwell campus. With the rapid development in detector technologies and the emergence of new experimental techniques, such as cryoEM, and accelerated detection rates, there are now major challenges for scientists to manage and process very large datasets. We develop and apply various state-of-the-art machine learning or AI technologies to accelerate the process of scientific discovery. SciML routinely works with beamline scientists from various facilities at RAL, such as
Diamond Light Source,
Central Laser Facility ,
ISIS Neutron and Muon Source,
Technology Department, and
NERC's Centre for Environmental Data Analysis and their
JASMIN service. The group provides a national AI computing service for the Alan Turning Centre and STFC researchers working on AI for Science along with various training courses on scientific machine learning.
SciML is a partner with the
Alan Turing Institute in their
‘Data Science for Science’ theme, and is a Turing Hub at Harwell. We have a number of strong links and collaborations with various national labs in the US. Our activities at RAL complement the predominantly industry-focused work of the Data Science Group at the
Hartree Centre, and the two groups collaborate in several joint activities.
GraphCore System & Training Workshop
In anticipation of the arrival of a new Graphcore system at STFC, RAL, the SciML team has collectively attended a three day virtual training on how to effectively leverage the GraphCore's TensorFlow and PyTorch software frameworks to significantly speed up machine learning workflows. Throughout the course, the team was able to road test two GraphCore systems and gain experience scaling ML workflows. This training will be crucial for utilising the forthcoming Graphcore IPU-M2000 system with 4 x Colossus MK2 IPUs. This system will augment SCD’s and STFC’s AI computing capabilities, namely, PEARL and SCD-Cloud, and is expected to provide an additional 1 PetaFlops of computing power towards AI applications.
SciML & Diamond Scientific Software Workshop
Network diagram from the talk "Enhanced Analysis of Diffraction and Microscopy with Machine Learning"
Scientific Machine Learning (SciML) and the Diamond Scientific Software
(SciSoft) groups hosted a joint event to highlight some of the Machine
Learning powered projects that are ongoing on the Rutherford campus.
This event marked the beginning of a series of joint workshops
with the overall aim of sharing information about ongoing Machine
Learning endeavours between the two groups. The first workshop aimed at
providing a broad overview of a number of projects that are of mutual
interest, with follow-up meetings more focussed on project discussions
and knowledge exchange to follow.
SciML @ BNL
Butler from SciML recently gave an invited talk at a workshop held
virtually in Berkeley National Laboratory. The three day workshop on the
theme of autonomous discovery was held in April (website). Keith
presented our collaborative work with ISIS Neutron and Muon Source at
the session devoted to autonomous discovery using neutron scattering.
SciML has a wide range of active projects using machine learing to enhance fundamental scientific research. We work with a diverse range of partners based at the Rutherford Appleton Laboratory and beyond. Find out more about more about our portfolio of projects on our Research Projects Page.
run a weekly seminar series welcoming talks covering a broad range of
topics in machine learning and its applications to science from both
internal and external speakers. Information on past and upcoming
seminars can be found here.
As a group, we offer various training courses focussed on AI for Science. For more information on training, please contact us.
PEARL AI Service
The PEARL service, provided through a collaboration between The Alan Turing Institute and Science and Technology Facilities Council, is a state-of-the-art GPU-based system for AI and machine learning research. The system includes two NVIDIA DGX2 nodes, and a 600TB storage, linked by a fast interconnect. The system has several thousands of GPU cores, and a large amount of memory to support most AI or machine learning operations, such as training on large datasets. You can apply for access to PEARL here.