Summary
"Thermal conductivity (TC) is a key characteristic of many materials, particularly those used in the energy and environment sectors (thermoelectrics, thermal-barrier coatings, catalysis, etc.). However, TC is largely unknown − of the 225,000 identified inorganic semiconductor and insulator crystals, only 100 have any TC data available.
By combining a novel ab initio molecular dynamics TC theory and big-data analytics (machine learning, compressed sensing, subgroup discovery), we will generate quantitative values and understanding of TC (and electrical conductivity (EC)) for most of these 225,000 materials, as well as for materials not yet discovered.
TEC1p will develop and deploy five key approaches. Individually these are already novel for materials science, but their combination in TEC1p enables a true breakthrough.
These five components are:
1) Ab initio theory of TC 3 (advanced density-functional theory, seamlessly linked to size- and time-converged statistical mechanics).
2) Ab initio theory of EC (advanced …; see #1).
3) Compressed sensing to identify a set of physical parameters that describe the TC and EC behaviour and to derive predictive equations that work for all materials.5
4) Active learning to build a systematic big-data database of materials, their TCs and ECs.
5) Subgroup discovery to recognise trends and anomalies in the big data, enhance the active learning, and elucidate the underlying physical mechanisms.
In analogy to Mendeleev’s table of the elements, we will build maps that arrange existing and predicted materials according to their TC and EC properties.
The methods that we will develop and the extensive calculations that we will execute are both innovative and timely. They will greatly progress scientific knowledge of the physical properties of materials. The impact of the concepts, methodology, and results will be far reaching for materials science, novel materials discovery, engineering,"
By combining a novel ab initio molecular dynamics TC theory and big-data analytics (machine learning, compressed sensing, subgroup discovery), we will generate quantitative values and understanding of TC (and electrical conductivity (EC)) for most of these 225,000 materials, as well as for materials not yet discovered.
TEC1p will develop and deploy five key approaches. Individually these are already novel for materials science, but their combination in TEC1p enables a true breakthrough.
These five components are:
1) Ab initio theory of TC 3 (advanced density-functional theory, seamlessly linked to size- and time-converged statistical mechanics).
2) Ab initio theory of EC (advanced …; see #1).
3) Compressed sensing to identify a set of physical parameters that describe the TC and EC behaviour and to derive predictive equations that work for all materials.5
4) Active learning to build a systematic big-data database of materials, their TCs and ECs.
5) Subgroup discovery to recognise trends and anomalies in the big data, enhance the active learning, and elucidate the underlying physical mechanisms.
In analogy to Mendeleev’s table of the elements, we will build maps that arrange existing and predicted materials according to their TC and EC properties.
The methods that we will develop and the extensive calculations that we will execute are both innovative and timely. They will greatly progress scientific knowledge of the physical properties of materials. The impact of the concepts, methodology, and results will be far reaching for materials science, novel materials discovery, engineering,"
Unfold all
/
Fold all
More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/740233 |
| Start date: | 01-10-2017 |
| End date: | 30-09-2024 |
| Total budget - Public funding: | 2 048 183,00 Euro - 2 048 183,00 Euro |
Cordis data
Original description
"Thermal conductivity (TC) is a key characteristic of many materials, particularly those used in the energy and environment sectors (thermoelectrics, thermal-barrier coatings, catalysis, etc.). However, TC is largely unknown − of the 225,000 identified inorganic semiconductor and insulator crystals, only 100 have any TC data available.By combining a novel ab initio molecular dynamics TC theory and big-data analytics (machine learning, compressed sensing, subgroup discovery), we will generate quantitative values and understanding of TC (and electrical conductivity (EC)) for most of these 225,000 materials, as well as for materials not yet discovered.
TEC1p will develop and deploy five key approaches. Individually these are already novel for materials science, but their combination in TEC1p enables a true breakthrough.
These five components are:
1) Ab initio theory of TC 3 (advanced density-functional theory, seamlessly linked to size- and time-converged statistical mechanics).
2) Ab initio theory of EC (advanced …; see #1).
3) Compressed sensing to identify a set of physical parameters that describe the TC and EC behaviour and to derive predictive equations that work for all materials.5
4) Active learning to build a systematic big-data database of materials, their TCs and ECs.
5) Subgroup discovery to recognise trends and anomalies in the big data, enhance the active learning, and elucidate the underlying physical mechanisms.
In analogy to Mendeleev’s table of the elements, we will build maps that arrange existing and predicted materials according to their TC and EC properties.
The methods that we will develop and the extensive calculations that we will execute are both innovative and timely. They will greatly progress scientific knowledge of the physical properties of materials. The impact of the concepts, methodology, and results will be far reaching for materials science, novel materials discovery, engineering,"
Status
SIGNEDCall topic
ERC-2016-ADGUpdate Date
27-04-2024
Images
No images available.
Geographical location(s)
