Machine learning for composite materials

Wear test of composite restorative materials by a polishing machine.

The wear resistance of microfilled and conventional composite resins, was examined, using two sizes of abrasive particles for polishing. The mechanical properties of the composite resins were also studied and evaluated in relation to wear. Conventional composites showed much lower wear resistance than the microfilled resins. The amalgam and human enamel abraded with 10ƒÊm SiC particles, had str...

Machine learning reveals orbital interaction in materials

We propose a novel representation of materials named an 'orbital-field matrix (OFM)', which is based on the distribution of valence shell electrons. We demonstrate that this new representation can be highly useful in mining material data. Experimental investigation shows that the formation energies of crystalline materials, atomization energies of molecular materials, and local magnetic moments...

Accelerating materials property predictions using machine learning

The materials discovery process can be significantly expedited and simplified if we can learn effectively from available knowledge and data. In the present contribution, we show that efficient and accurate prediction of a diverse set of properties of material systems is possible by employing machine (or statistical) learning methods trained on quantum mechanical computations in combination with...

Theory-Guided Machine Learning in Materials Science

Materials scientists are increasingly adopting the use of machine learning tools to discover hidden trends in data and make predictions. Applying concepts from data science without foreknowledge of their limitations and the unique qualities of materials data, however, could lead to errant conclusions. The differences that exist between various kinds of experimental and calculated data require c...

Data Management for Composite Materials