Thursday

Javier Heras-Domingo
Unlocking the Potential of EXAFS: Machine Learning Approaches for Spectroscopic Data

A rooted knowledge and understanding of the material and its properties stems from a holistic perspective. Indeed, when discussing the properties of a newly engineered material, it is common to present:
- a text-based description of the sequence of actions through which such material was obtained, listing key variables as scalars.
- a characterization of its structure by means of advanced microscopy (e.g., 2D images, 3D tomographies, 4D spatio-temporal analysis) and spectroscopy (e.g., adsorption spectra, NMR spectra), also with the aid of atomistic and electronic structure simulations.
- a list of key performance indicators, in the form of scalar variables (e.g. the mechanical properties of an alloy or the Seebeck coefficient of a thermoelectric) or a time-series (e.g., activity of a catalyst over time, the capacity of a battery over time).
- a mechanistic discussion of the relationships that link structure-to-property, often through quantities extracted from electronic structure and atomistic scale simulations.

Machine learning methods are revolutionizing the way we approach materials design, making an impact in each, yet, it is rare that they fully exploit information and data from different modalities and sources. The aim of this workshop is thus two-fold:
- Young researchers will have the opportunity to grow solid foundations and a complete overview of cutting-edge machine-learning methods that enable the community to tackle outstanding challenges across diverse domains in materials design and discovery.
- Attendees will have the chance to discuss and identify routes on how to best combine information of different nature towards a unified vision (and solution) of the material design and discovery problem. To this end, during the workshop part, invited and contributed speakers, and panel discussions will take place, with a focus on multi-modal, multi-objective, and multi-fidelity machine learning methods in materials science.

Nejc Hodnik
Exploring Big Data for a Deeper Understanding of Electrocatalyst Behavior

A rooted knowledge and understanding of the material and its properties stems from a holistic perspective. Indeed, when discussing the properties of a newly engineered material, it is common to present:
- a text-based description of the sequence of actions through which such material was obtained, listing key variables as scalars.
- a characterization of its structure by means of advanced microscopy (e.g., 2D images, 3D tomographies, 4D spatio-temporal analysis) and spectroscopy (e.g., adsorption spectra, NMR spectra), also with the aid of atomistic and electronic structure simulations.
- a list of key performance indicators, in the form of scalar variables (e.g. the mechanical properties of an alloy or the Seebeck coefficient of a thermoelectric) or a time-series (e.g., activity of a catalyst over time, the capacity of a battery over time).
- a mechanistic discussion of the relationships that link structure-to-property, often through quantities extracted from electronic structure and atomistic scale simulations.

Machine learning methods are revolutionizing the way we approach materials design, making an impact in each, yet, it is rare that they fully exploit information and data from different modalities and sources. The aim of this workshop is thus two-fold:
- Young researchers will have the opportunity to grow solid foundations and a complete overview of cutting-edge machine-learning methods that enable the community to tackle outstanding challenges across diverse domains in materials design and discovery.
- Attendees will have the chance to discuss and identify routes on how to best combine information of different nature towards a unified vision (and solution) of the material design and discovery problem. To this end, during the workshop part, invited and contributed speakers, and panel discussions will take place, with a focus on multi-modal, multi-objective, and multi-fidelity machine learning methods in materials science.

Panel discussion

A rooted knowledge and understanding of the material and its properties stems from a holistic perspective. Indeed, when discussing the properties of a newly engineered material, it is common to present:
- a text-based description of the sequence of actions through which such material was obtained, listing key variables as scalars.
- a characterization of its structure by means of advanced microscopy (e.g., 2D images, 3D tomographies, 4D spatio-temporal analysis) and spectroscopy (e.g., adsorption spectra, NMR spectra), also with the aid of atomistic and electronic structure simulations.
- a list of key performance indicators, in the form of scalar variables (e.g. the mechanical properties of an alloy or the Seebeck coefficient of a thermoelectric) or a time-series (e.g., activity of a catalyst over time, the capacity of a battery over time).
- a mechanistic discussion of the relationships that link structure-to-property, often through quantities extracted from electronic structure and atomistic scale simulations.

Machine learning methods are revolutionizing the way we approach materials design, making an impact in each, yet, it is rare that they fully exploit information and data from different modalities and sources. The aim of this workshop is thus two-fold:
- Young researchers will have the opportunity to grow solid foundations and a complete overview of cutting-edge machine-learning methods that enable the community to tackle outstanding challenges across diverse domains in materials design and discovery.
- Attendees will have the chance to discuss and identify routes on how to best combine information of different nature towards a unified vision (and solution) of the material design and discovery problem. To this end, during the workshop part, invited and contributed speakers, and panel discussions will take place, with a focus on multi-modal, multi-objective, and multi-fidelity machine learning methods in materials science.

Vinko Sršan
Deep learning-based drift correction in atomically resolved STEM images

A rooted knowledge and understanding of the material and its properties stems from a holistic perspective. Indeed, when discussing the properties of a newly engineered material, it is common to present:
- a text-based description of the sequence of actions through which such material was obtained, listing key variables as scalars.
- a characterization of its structure by means of advanced microscopy (e.g., 2D images, 3D tomographies, 4D spatio-temporal analysis) and spectroscopy (e.g., adsorption spectra, NMR spectra), also with the aid of atomistic and electronic structure simulations.
- a list of key performance indicators, in the form of scalar variables (e.g. the mechanical properties of an alloy or the Seebeck coefficient of a thermoelectric) or a time-series (e.g., activity of a catalyst over time, the capacity of a battery over time).
- a mechanistic discussion of the relationships that link structure-to-property, often through quantities extracted from electronic structure and atomistic scale simulations.

Machine learning methods are revolutionizing the way we approach materials design, making an impact in each, yet, it is rare that they fully exploit information and data from different modalities and sources. The aim of this workshop is thus two-fold:
- Young researchers will have the opportunity to grow solid foundations and a complete overview of cutting-edge machine-learning methods that enable the community to tackle outstanding challenges across diverse domains in materials design and discovery.
- Attendees will have the chance to discuss and identify routes on how to best combine information of different nature towards a unified vision (and solution) of the material design and discovery problem. To this end, during the workshop part, invited and contributed speakers, and panel discussions will take place, with a focus on multi-modal, multi-objective, and multi-fidelity machine learning methods in materials science.

Helge Stein
Beyond acceleration? & What got us here?

A rooted knowledge and understanding of the material and its properties stems from a holistic perspective. Indeed, when discussing the properties of a newly engineered material, it is common to present:
- a text-based description of the sequence of actions through which such material was obtained, listing key variables as scalars.
- a characterization of its structure by means of advanced microscopy (e.g., 2D images, 3D tomographies, 4D spatio-temporal analysis) and spectroscopy (e.g., adsorption spectra, NMR spectra), also with the aid of atomistic and electronic structure simulations.
- a list of key performance indicators, in the form of scalar variables (e.g. the mechanical properties of an alloy or the Seebeck coefficient of a thermoelectric) or a time-series (e.g., activity of a catalyst over time, the capacity of a battery over time).
- a mechanistic discussion of the relationships that link structure-to-property, often through quantities extracted from electronic structure and atomistic scale simulations.

Machine learning methods are revolutionizing the way we approach materials design, making an impact in each, yet, it is rare that they fully exploit information and data from different modalities and sources. The aim of this workshop is thus two-fold:
- Young researchers will have the opportunity to grow solid foundations and a complete overview of cutting-edge machine-learning methods that enable the community to tackle outstanding challenges across diverse domains in materials design and discovery.
- Attendees will have the chance to discuss and identify routes on how to best combine information of different nature towards a unified vision (and solution) of the material design and discovery problem. To this end, during the workshop part, invited and contributed speakers, and panel discussions will take place, with a focus on multi-modal, multi-objective, and multi-fidelity machine learning methods in materials science.

Dušan Strmčnik
Machine Learning for Investigation of Nickel Surface Chemistry in Electrocatalytic Production of Hydrogen

A rooted knowledge and understanding of the material and its properties stems from a holistic perspective. Indeed, when discussing the properties of a newly engineered material, it is common to present:
- a text-based description of the sequence of actions through which such material was obtained, listing key variables as scalars.
- a characterization of its structure by means of advanced microscopy (e.g., 2D images, 3D tomographies, 4D spatio-temporal analysis) and spectroscopy (e.g., adsorption spectra, NMR spectra), also with the aid of atomistic and electronic structure simulations.
- a list of key performance indicators, in the form of scalar variables (e.g. the mechanical properties of an alloy or the Seebeck coefficient of a thermoelectric) or a time-series (e.g., activity of a catalyst over time, the capacity of a battery over time).
- a mechanistic discussion of the relationships that link structure-to-property, often through quantities extracted from electronic structure and atomistic scale simulations.

Machine learning methods are revolutionizing the way we approach materials design, making an impact in each, yet, it is rare that they fully exploit information and data from different modalities and sources. The aim of this workshop is thus two-fold:
- Young researchers will have the opportunity to grow solid foundations and a complete overview of cutting-edge machine-learning methods that enable the community to tackle outstanding challenges across diverse domains in materials design and discovery.
- Attendees will have the chance to discuss and identify routes on how to best combine information of different nature towards a unified vision (and solution) of the material design and discovery problem. To this end, during the workshop part, invited and contributed speakers, and panel discussions will take place, with a focus on multi-modal, multi-objective, and multi-fidelity machine learning methods in materials science.

Sašo Šturm
Autonomous laboratory for sustainable research and discovery of new materials

A rooted knowledge and understanding of the material and its properties stems from a holistic perspective. Indeed, when discussing the properties of a newly engineered material, it is common to present:
- a text-based description of the sequence of actions through which such material was obtained, listing key variables as scalars.
- a characterization of its structure by means of advanced microscopy (e.g., 2D images, 3D tomographies, 4D spatio-temporal analysis) and spectroscopy (e.g., adsorption spectra, NMR spectra), also with the aid of atomistic and electronic structure simulations.
- a list of key performance indicators, in the form of scalar variables (e.g. the mechanical properties of an alloy or the Seebeck coefficient of a thermoelectric) or a time-series (e.g., activity of a catalyst over time, the capacity of a battery over time).
- a mechanistic discussion of the relationships that link structure-to-property, often through quantities extracted from electronic structure and atomistic scale simulations.

Machine learning methods are revolutionizing the way we approach materials design, making an impact in each, yet, it is rare that they fully exploit information and data from different modalities and sources. The aim of this workshop is thus two-fold:
- Young researchers will have the opportunity to grow solid foundations and a complete overview of cutting-edge machine-learning methods that enable the community to tackle outstanding challenges across diverse domains in materials design and discovery.
- Attendees will have the chance to discuss and identify routes on how to best combine information of different nature towards a unified vision (and solution) of the material design and discovery problem. To this end, during the workshop part, invited and contributed speakers, and panel discussions will take place, with a focus on multi-modal, multi-objective, and multi-fidelity machine learning methods in materials science.

Austin Zadoks
Spectral Operator Representations

A rooted knowledge and understanding of the material and its properties stems from a holistic perspective. Indeed, when discussing the properties of a newly engineered material, it is common to present:
- a text-based description of the sequence of actions through which such material was obtained, listing key variables as scalars.
- a characterization of its structure by means of advanced microscopy (e.g., 2D images, 3D tomographies, 4D spatio-temporal analysis) and spectroscopy (e.g., adsorption spectra, NMR spectra), also with the aid of atomistic and electronic structure simulations.
- a list of key performance indicators, in the form of scalar variables (e.g. the mechanical properties of an alloy or the Seebeck coefficient of a thermoelectric) or a time-series (e.g., activity of a catalyst over time, the capacity of a battery over time).
- a mechanistic discussion of the relationships that link structure-to-property, often through quantities extracted from electronic structure and atomistic scale simulations.

Machine learning methods are revolutionizing the way we approach materials design, making an impact in each, yet, it is rare that they fully exploit information and data from different modalities and sources. The aim of this workshop is thus two-fold:
- Young researchers will have the opportunity to grow solid foundations and a complete overview of cutting-edge machine-learning methods that enable the community to tackle outstanding challenges across diverse domains in materials design and discovery.
- Attendees will have the chance to discuss and identify routes on how to best combine information of different nature towards a unified vision (and solution) of the material design and discovery problem. To this end, during the workshop part, invited and contributed speakers, and panel discussions will take place, with a focus on multi-modal, multi-objective, and multi-fidelity machine learning methods in materials science.

Franco Pellegrini
LATTE: an atomic environment descriptor based on Cartesian tensor contractions

A rooted knowledge and understanding of the material and its properties stems from a holistic perspective. Indeed, when discussing the properties of a newly engineered material, it is common to present:
- a text-based description of the sequence of actions through which such material was obtained, listing key variables as scalars.
- a characterization of its structure by means of advanced microscopy (e.g., 2D images, 3D tomographies, 4D spatio-temporal analysis) and spectroscopy (e.g., adsorption spectra, NMR spectra), also with the aid of atomistic and electronic structure simulations.
- a list of key performance indicators, in the form of scalar variables (e.g. the mechanical properties of an alloy or the Seebeck coefficient of a thermoelectric) or a time-series (e.g., activity of a catalyst over time, the capacity of a battery over time).
- a mechanistic discussion of the relationships that link structure-to-property, often through quantities extracted from electronic structure and atomistic scale simulations.

Machine learning methods are revolutionizing the way we approach materials design, making an impact in each, yet, it is rare that they fully exploit information and data from different modalities and sources. The aim of this workshop is thus two-fold:
- Young researchers will have the opportunity to grow solid foundations and a complete overview of cutting-edge machine-learning methods that enable the community to tackle outstanding challenges across diverse domains in materials design and discovery.
- Attendees will have the chance to discuss and identify routes on how to best combine information of different nature towards a unified vision (and solution) of the material design and discovery problem. To this end, during the workshop part, invited and contributed speakers, and panel discussions will take place, with a focus on multi-modal, multi-objective, and multi-fidelity machine learning methods in materials science.

Matthias Stosiek
Lignin Carbohydrate Complexes – Learning the Structure-Property Relation with Artificial Intelligence

A rooted knowledge and understanding of the material and its properties stems from a holistic perspective. Indeed, when discussing the properties of a newly engineered material, it is common to present:
- a text-based description of the sequence of actions through which such material was obtained, listing key variables as scalars.
- a characterization of its structure by means of advanced microscopy (e.g., 2D images, 3D tomographies, 4D spatio-temporal analysis) and spectroscopy (e.g., adsorption spectra, NMR spectra), also with the aid of atomistic and electronic structure simulations.
- a list of key performance indicators, in the form of scalar variables (e.g. the mechanical properties of an alloy or the Seebeck coefficient of a thermoelectric) or a time-series (e.g., activity of a catalyst over time, the capacity of a battery over time).
- a mechanistic discussion of the relationships that link structure-to-property, often through quantities extracted from electronic structure and atomistic scale simulations.

Machine learning methods are revolutionizing the way we approach materials design, making an impact in each, yet, it is rare that they fully exploit information and data from different modalities and sources. The aim of this workshop is thus two-fold:
- Young researchers will have the opportunity to grow solid foundations and a complete overview of cutting-edge machine-learning methods that enable the community to tackle outstanding challenges across diverse domains in materials design and discovery.
- Attendees will have the chance to discuss and identify routes on how to best combine information of different nature towards a unified vision (and solution) of the material design and discovery problem. To this end, during the workshop part, invited and contributed speakers, and panel discussions will take place, with a focus on multi-modal, multi-objective, and multi-fidelity machine learning methods in materials science.

Nataliya Lopanitsyna
Revealing Chemical Pathways in Reaction Data through Noctis

A rooted knowledge and understanding of the material and its properties stems from a holistic perspective. Indeed, when discussing the properties of a newly engineered material, it is common to present:
- a text-based description of the sequence of actions through which such material was obtained, listing key variables as scalars.
- a characterization of its structure by means of advanced microscopy (e.g., 2D images, 3D tomographies, 4D spatio-temporal analysis) and spectroscopy (e.g., adsorption spectra, NMR spectra), also with the aid of atomistic and electronic structure simulations.
- a list of key performance indicators, in the form of scalar variables (e.g. the mechanical properties of an alloy or the Seebeck coefficient of a thermoelectric) or a time-series (e.g., activity of a catalyst over time, the capacity of a battery over time).
- a mechanistic discussion of the relationships that link structure-to-property, often through quantities extracted from electronic structure and atomistic scale simulations.

Machine learning methods are revolutionizing the way we approach materials design, making an impact in each, yet, it is rare that they fully exploit information and data from different modalities and sources. The aim of this workshop is thus two-fold:
- Young researchers will have the opportunity to grow solid foundations and a complete overview of cutting-edge machine-learning methods that enable the community to tackle outstanding challenges across diverse domains in materials design and discovery.
- Attendees will have the chance to discuss and identify routes on how to best combine information of different nature towards a unified vision (and solution) of the material design and discovery problem. To this end, during the workshop part, invited and contributed speakers, and panel discussions will take place, with a focus on multi-modal, multi-objective, and multi-fidelity machine learning methods in materials science.

Emma King-Smith
Practical Machine Learning for Organic Small Molecule Modelling


A rooted knowledge and understanding of the material and its properties stems from a holistic perspective. Indeed, when discussing the properties of a newly engineered material, it is common to present:
- a text-based description of the sequence of actions through which such material was obtained, listing key variables as scalars.
- a characterization of its structure by means of advanced microscopy (e.g., 2D images, 3D tomographies, 4D spatio-temporal analysis) and spectroscopy (e.g., adsorption spectra, NMR spectra), also with the aid of atomistic and electronic structure simulations.
- a list of key performance indicators, in the form of scalar variables (e.g. the mechanical properties of an alloy or the Seebeck coefficient of a thermoelectric) or a time-series (e.g., activity of a catalyst over time, the capacity of a battery over time).
- a mechanistic discussion of the relationships that link structure-to-property, often through quantities extracted from electronic structure and atomistic scale simulations.

Machine learning methods are revolutionizing the way we approach materials design, making an impact in each, yet, it is rare that they fully exploit information and data from different modalities and sources. The aim of this workshop is thus two-fold:
- Young researchers will have the opportunity to grow solid foundations and a complete overview of cutting-edge machine-learning methods that enable the community to tackle outstanding challenges across diverse domains in materials design and discovery.
- Attendees will have the chance to discuss and identify routes on how to best combine information of different nature towards a unified vision (and solution) of the material design and discovery problem. To this end, during the workshop part, invited and contributed speakers, and panel discussions will take place, with a focus on multi-modal, multi-objective, and multi-fidelity machine learning methods in materials science.