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March 2024 ESRFnews

MACHINE LEARNING

the potential to streamline experiments, reduce data

volumes, speed up data analysis and obtain results that

would otherwise be beyond human insight. “We’ve been

amazed in many ways by the results we could produce,”

says Linus Pithan, a materials and data scientist based at

the German synchrotron DESY who ran an autonomous

crystalgrowth experiment at the ESRFs ID10 beamline

with colleagues last year The quality of the online data

analysis was astonishing

Formerly a member of the ESRFs Beamline

Control Unit where he helped develop the new BLISS

beamline control system see Insight p10 Pithan is

well placed to test the potential of machine learning

in synchrotron science The flexibility of BLISS was

necessary for him and his colleagues to integrate their

own deeplearning algorithm which they had trained

beforehand to reconstruct scatteringlength density

SLD profiles from the Xray reflectivity of molecular

thin films Unlike the forwards operation calculating

a reflectivity curve from an SLD profile – this inverse

problem can be painfully tedious to solve even for an

experienced analyst: the data are inherently ambiguous,

because they do not include the phase of the scattered

X-rays. Indeed, it is a demanding task for a machine too,

which is why at the beamline Pithan’s group made use of

an online service known as VISA to harness the ESRF’s

central computer system.

The success of the automation was immediately

apparent (figure 1). From the reflectivity measurements,

the deep-learning algorithm could output SLD profiles

and thin-film properties such as layer thickness and

surface roughness in real time, and thereby stop in

situ molecular beam deposition at any desired sample

thickness between 80 Å and 640 Å, with an average

accuracy of 2 Å (J. Synchrotron Rad. 30 1064). “The

machine-learning model was able to ‘predict’ results

within milliseconds,” says Pithan. “In a way we

transferred the time that is traditionally needed for the

manual fitting process to the point before the actual

experiment where we trained the model. So by the time of

the experiment, were able to get results instantaneously.”

Strategic development

The ESRF has been anticipating a rise in machine

learning for many years. It forms part of the data strategy,

and is one of the reasons for the ESRF’s engagement

in various European projects that support the

trend: PaNOSC, which is a cloud service to host publicly

funded photon and neutron research data; DAPHNE,

which aims to make photon and neutron data accord

to “FAIR” (reusable) principles; and most recently

OSCARS, which promotes European open science.

Vincent Favre-Nicolin, the head of the ESRF algorithms

and scientific data analysis group, is wary of claiming

that machine learning is always a “magical” solution, and

points out the toll it can take on computing resources.

“But for some areas it makes a real difference,” he says.

Aside from experimental automation, one of these

areas is image segmentation. In daily life humans find

this easy – we have no problem working out where

our fingertips end and the pages of a magazine begin,

for instance – but it can be laborious in certain areas

of synchrotron science, such as tomography. ESRF

postdoc François Cadiou, who is involved in BIG-

MAP (part of the European Commission’s BATTERY

2030 largescale research initiative for sustainable

batteries is developing machinelearning algorithms

to quickly identify the different constituents of porous

electrodes such as the active material the conductive

polymer binder and the electrolyte Accuracy is key

here as researchers need to know the exact conditions

that promote superior battery performance over

potentially catastrophic failure

Cadiou and his colleagues are developing a type of

interactive AI algorithm called active learning They

begin by annotating some images in a tomographic

volume manually to set up their model for training

When its learning slows the model moves on to

E S R F/S T E F C A N D É

Our

goal is to

democratise

the analysis

of Xray

spectra

Machine-learning

algorithms can harness

the ESRF’s central

computing facilities.

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