Real Materials in Action: Everything, Everywhere, All at Once
Simon Billinge a
a Columbia University
Proceedings of MATSUS Spring 2026 Conference (MATSUSSpring26)
C1 Structural Foundations of Nanomaterials Properties
Barcelona, Spain, 2026 March 23rd - 27th
Organizers: Nicola Dengo, Nadine Schrenker and Stefano Toso
Invited Speaker, Simon Billinge, presentation 784
Publication date: 15th December 2025

At the heart of materials science studies for next generation materials is an idea that we want to be studying real materials doing real things, often in real devices.  A key to making progress is to be able to understand the nanoparticle structure, the arrangements of atoms in the nanoparticles and nanoscale structures. Also critical is understanding the distribution of the nanoparticles and how they change in time as devices run and reactions take place.  We use advanced x-ray, neutron and electron scattering methods to get at this problem. In practice, this presents a number of key data analysis and interpretation challenges because it implies we are studying ever more complicated samples, often in complex heterogeneous environments and in time-resolved operando setups, and we are interrogating our data for more and more subtle effects such as microstructures and evolving defects and local structures. We use advanced x-ray, neutron and electron scattering methods to get at this problem.  I will talk about these methods and show some recent success-stories in the fields of sustainable energy, environmental remediation and cultural heritage preservation.  In particular, we need to study not only the atomic-scale structure (everything), but how it varies with position in the device (everywhere), and how it varies with time (all at once). I will also discuss the fundamental challenges on our ability to extract information from the data and how we are now turning to machine learning and artificial intelligence techniques to give more insights. Some of these powerful tools are clearly ready to be applied more broadly in the community and others are still in the future but look very promising.  They include unsupervised and supervised machine learning approaches, conventional ML and deep neural networks, including generative models, as well as approaches for autonomous time-resolved experimentation.

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