Wednesday

Christoph Koch
Machine learning in electron microscopy and spectroscopy

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.

Christoph Koch
FAIRmat is building a FAIR data infrastructure for condensed-matter physics and the chemical physics of solids

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 Džeroski
Boris Turk
ML Modalities for Materials Science: Opening of Workshop Part

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.
Vidnost
Javen
Jezik
angleščina
Avtor
Institut "Jožef Stefan"
Licenca
© Vse pravice pridržane
Datum dogodka
15. 05. 2024

Andy Sode Anker
Machine learning for analysis of experimental scattering data in materials chemistry

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.

Andreja Benčan Golob
Addressing Challenges in 4D STEM Data of Ferroelectrics Using Machine Learning

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.

Martin Uhrin
Symmetry-aware generative model for glassy motifs

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.

Christer Söderholm
Materials design of inorganic crystals with 3D transformers

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.

Kevin Rossi
DAEMON COST Action

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.