SciML Highlights Archive
13 Jul 2021





2022-02-02 SciML Software

Work led by Kuangdai Leng developing new software for small angle scattering data analysis has recently been released as the ffsas Python package. FFSAS is a python library for the inversion of parameter distributions of a polydisperse system in small-angle scattering (SAS) experiments. In FFSAS, the formulation of the inverse problem is physics-independent, covering SAS models with an arbitrary number of polydisperse parameters and both 1D and 2D intensity observations. Employing a versatile trust-region method as the underlying NLP solver, it simultaneously optimises all the polydisperse parameters in free form, achieving high accuracy and efficiency based on a series of theoretical and computational enhancements. The figure shows a large-scale synthetic test on polydisperse cylinders, where we accurately recover the four model parameters on the left using the theoretically-predicted intensity image on the right.

2021-11-10 SciML Publication

 Our latest research looks at combining graph neural networks (GNNs) with Gaussian processes to perform active learning of materials properties. GNNs provide a powerful intuitive route to featurising material structures, while Gaussian processes provide estimates and uncertanties that allow us to identify the optimal next material to investigate in order to improve the model generalisability. Read more in J. Chem. Phys.


2021-10-21 SciML at I2NS

SciML work with ISIS was recently presented at the Innovative Inelastic Neutron Scattering workshop in Autrans, France. Andy Sode Anker and Keith Butler presented the latest work on using machine learning to analyse and understand inelastic neutron scattering data. Read more about the work that we are doing with ISIS in the SciML and ISIS projects page.

2021-09-01 Welcoming new group members

The SciML team are delighted to welocme our latest group members. Hattie Stewart, Michael Norman and Ben White from the Data Intensive CDT, as well as Andy Sode Anker from Copenhagen University are all joining us for 6 month projects to work on a varity of exciting topics. We are really looking forward to the exciting work that we will do over the next 6 months.

2021-07-28 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.

2021-06-28 - 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.

2021-06-01 - SciML @ BNL ​​​​​

Keith 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.
Contact: Butler, Keith (STFC,RAL,SC)