TY - GEN
T1 - Reviewing the novel machine learning tools for materials design
AU - Mosavi, Amir
AU - Rabczuk, Timon
AU - Varkonyi-Koczy, Annamária R.
N1 - Funding Information:
Acknowledgement. This work has been sponsored by the Research & Development Program for the project “Modernization and Improvement of Technical Infrastructure for Research and Development of J. Selye University in the Fields of Nanotechnology and Intelligent Space”, ITMS 26210120042, co-funded by the European Regional Development Fund.
Publisher Copyright:
© Springer International Publishing AG 2018.
PY - 2018
Y1 - 2018
N2 - Computational materials design is a rapidly evolving field of challenges and opportunities aiming at development and application of multi-scale methods to simulate, predict and select innovative materials with high accuracy. Today the latest advancements in machine learning, deep learning, internet of things (IoT), big data, and intelligent optimization have highly revolutionized the computational methodologies used for materials design innovation. Such novelties in computation enable the development of problem-specific solvers with vast potential applications in industry and business. This paper reviews the state of the art of technological advancements that machine learning tools, in particular, have brought for materials design innovation. Further via presenting a case study the potential of such novel computational tools are discussed for the virtual design and simulation of innovative materials in modeling the fundamental properties and behavior of a wide range of multi-scale materials design problems.
AB - Computational materials design is a rapidly evolving field of challenges and opportunities aiming at development and application of multi-scale methods to simulate, predict and select innovative materials with high accuracy. Today the latest advancements in machine learning, deep learning, internet of things (IoT), big data, and intelligent optimization have highly revolutionized the computational methodologies used for materials design innovation. Such novelties in computation enable the development of problem-specific solvers with vast potential applications in industry and business. This paper reviews the state of the art of technological advancements that machine learning tools, in particular, have brought for materials design innovation. Further via presenting a case study the potential of such novel computational tools are discussed for the virtual design and simulation of innovative materials in modeling the fundamental properties and behavior of a wide range of multi-scale materials design problems.
KW - Machine learning
KW - Materials design
KW - Optimization
UR - http://www.scopus.com/inward/record.url?scp=85029812280&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-67459-9_7
DO - 10.1007/978-3-319-67459-9_7
M3 - Conference contribution
AN - SCOPUS:85029812280
SN - 9783319674582
T3 - Advances in Intelligent Systems and Computing
SP - 50
EP - 58
BT - Recent Advances in Technology Research and Education - Proceedings of the 16th International Conference on Global Research and Education Inter-Academia 2017
A2 - Luca, Dumitru
A2 - Sirghi, Lucel
A2 - Costin, Claudiu
PB - Springer Verlag
T2 - 16th International Conference on Global Research and Education Inter-Academia, 2017
Y2 - 25 September 2017 through 28 September 2017
ER -