Materials science research begins in laboratories with testing the properties of metals and their alloys, the properties of the material depending on the type of additives and microstructure, as well as the changes in these properties taking place under the influence of processing. The next step is...
Machine learning holds great potential to accelerate materials research. Many domains in materials science are benefiting from its application, but several challenges persist, and it remains to be seen whether the field will live up to the hype that surrounds it. Editorial4 Aug 2021 Nature Reviews...
Here we summarize recent progress in machine learning for the chemical sciences. We outline machine-learning techniques that are suitable for addressing research questions in this domain, as well as future directions for the field. We envisage a future i
This review focused on the basic operational procedures of machine learning in analyzing the properties of materials; it summarized the applications of machine learning algorithms in materials science in recent years, which include material property analysis, materials design, and quantum chemistry; and ...
As the fields of artificial intelligence and machine learning are exploding, their universal nature is becoming more apparent. Machine learning is being leveraged in a huge variety of sub-fields, and…
In this thesis, the use of machine learning in materials science is explored, for two different problems: the optimisation of gallium nitride optoelectronic devices, and the prediction of material failure in the setting of laboratory earthquakes.Light emitting diodes based on III-nitrides quantum ...
Here we summarize recent progress in machine learning for the chemical sciences. We outline machine-learning techniques that are suitable for addressing research questions in this domain, as well as future directions for the field. We envisage a future i
Propelled partly by the Materials Genome Initiative, and partly by the algorithmic developments and the resounding successes of data-driven efforts in other domains, informatics strategies are beginning to take shape within materials science. These approaches lead to surrogate machine learning models that...
2. Description of machine learning methods in materials science 3. The application of machine learning in material property prediction 4. The application of machine learning in new materials discovery 5. The application of machine learning for various other purposes 6. Analysis of and countermeasures ...
This review discussed the dilemma of small data faced by materials machine learning. First, we analyzed the limitations brought by small data. Then, the workflow of materials machine learning has been introduced. Next, the methods of dealing with small d