ndustry guided harnessing of materials big-data using machine learning
|Coordinator||Linköpings universitet - Institutionen för ekonomisk och industriell utveckling|
|Funding from Vinnova||SEK 500 000|
|Project duration||November 2018 - November 2019|
|Venture||Banbrytande idéer inom industriell utveckling|
|Call||Banbrytande idéer inom industriell utveckling 2018|
Purpose and goal
Artificial intelligence-based methods will be utilized to accelerate the search of multifunctional coatings. We will develop and train a machine-learning algorithm that connects materials composition, structures etc. with properties obtained from open databases combined with information about materials behavior at industrial conditions. Two world-leading Swedish companies in the field of hard coatings will provide control knowledge for uncovering potentials of nitride, carbide and oxide materials and promising materials combinations for high-entropy alloys.
Expected results and effects
We will develop a machine learning approach for materials exploration and developments for the hard coating industry. The software will be adapted to industrial platforms. We deliver a partition of potential materials in categories for different hard coating applications. The multifunctionalities (piezoelectric, pyroelectric etc. properties) of the materials will be explored by our collaboration with FunMat-II competence center. The project will serve renewal for both, industry and academic research.
Planned approach and implementation
The project is comprised by three work packages: i) Data collection, ii) Knowledge extraction by machine learning and iii) validation. All the collected and newly generated new data will be classified according to industrial control descriptions provided by two world-leading Swedish companies. We capitalize on our broad competence ranging from computational materials science to industrial R&D expertise.