Abstract
After developing the simulation-based design approach of materials for decades, computational materials science/engineering present the power in accelerating the discoveries and the applications of novel advanced materials through a digital-twin design paradigm of integrated computational materials engineering (ICME). While the short goals of ICME are almost accomplished, those long goals are on the right way, highlighting the concept/strategy of materials by design. In this brief review, the recent frameworks of data-driven ICME in the last two years are discussed, presenting key aspects of principles, benchmarks, standards, databases, platforms and toolkits via various case studies. The author and his collaborators display a routine of data-driven ICME utilized in the investigations of Mg alloys through integrating the high-throughput first-principles calculations and the CALPHAD approach. It is highlighted that the bonding charge density could not only provide an atomic and electronic insight into the physical nature of chemical bond of pure elements, alloys, metal melts, oxides and semiconductors, surface and interfaces, but also reveal the fundamental solid-solution strengthening/embrittlement mechanism and the grain refinement mechanism, paving a path in accelerating the development of advanced materials. It is believed that the combinations of high-throughput multi-scale computations and fast experiments/manufacturing will build the advanced algorithms in the development of a promising digital fabricating approach to overcome the present and future challenges, illuminating the way toward the digital-twin intelligent manufacturing era.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 42-48 |
| Number of pages | 7 |
| Journal | Computational Materials Science |
| Volume | 158 |
| DOIs | |
| State | Published - Feb 15 2019 |
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All Science Journal Classification (ASJC) codes
- Computer Science(all)
- Chemistry(all)
- Materials Science(all)
- Mechanics of Materials
- Physics and Astronomy(all)
- Computational Mathematics
Cite this
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Integrated computational materials engineering for advanced materials : A brief review. / Yi Wang, William; Li, Jinshan; Liu, Weimin; Liu, Zi Kui.
In: Computational Materials Science, Vol. 158, 15.02.2019, p. 42-48.Research output: Contribution to journal › Article
TY - JOUR
T1 - Integrated computational materials engineering for advanced materials
T2 - A brief review
AU - Yi Wang, William
AU - Li, Jinshan
AU - Liu, Weimin
AU - Liu, Zi Kui
PY - 2019/2/15
Y1 - 2019/2/15
N2 - After developing the simulation-based design approach of materials for decades, computational materials science/engineering present the power in accelerating the discoveries and the applications of novel advanced materials through a digital-twin design paradigm of integrated computational materials engineering (ICME). While the short goals of ICME are almost accomplished, those long goals are on the right way, highlighting the concept/strategy of materials by design. In this brief review, the recent frameworks of data-driven ICME in the last two years are discussed, presenting key aspects of principles, benchmarks, standards, databases, platforms and toolkits via various case studies. The author and his collaborators display a routine of data-driven ICME utilized in the investigations of Mg alloys through integrating the high-throughput first-principles calculations and the CALPHAD approach. It is highlighted that the bonding charge density could not only provide an atomic and electronic insight into the physical nature of chemical bond of pure elements, alloys, metal melts, oxides and semiconductors, surface and interfaces, but also reveal the fundamental solid-solution strengthening/embrittlement mechanism and the grain refinement mechanism, paving a path in accelerating the development of advanced materials. It is believed that the combinations of high-throughput multi-scale computations and fast experiments/manufacturing will build the advanced algorithms in the development of a promising digital fabricating approach to overcome the present and future challenges, illuminating the way toward the digital-twin intelligent manufacturing era.
AB - After developing the simulation-based design approach of materials for decades, computational materials science/engineering present the power in accelerating the discoveries and the applications of novel advanced materials through a digital-twin design paradigm of integrated computational materials engineering (ICME). While the short goals of ICME are almost accomplished, those long goals are on the right way, highlighting the concept/strategy of materials by design. In this brief review, the recent frameworks of data-driven ICME in the last two years are discussed, presenting key aspects of principles, benchmarks, standards, databases, platforms and toolkits via various case studies. The author and his collaborators display a routine of data-driven ICME utilized in the investigations of Mg alloys through integrating the high-throughput first-principles calculations and the CALPHAD approach. It is highlighted that the bonding charge density could not only provide an atomic and electronic insight into the physical nature of chemical bond of pure elements, alloys, metal melts, oxides and semiconductors, surface and interfaces, but also reveal the fundamental solid-solution strengthening/embrittlement mechanism and the grain refinement mechanism, paving a path in accelerating the development of advanced materials. It is believed that the combinations of high-throughput multi-scale computations and fast experiments/manufacturing will build the advanced algorithms in the development of a promising digital fabricating approach to overcome the present and future challenges, illuminating the way toward the digital-twin intelligent manufacturing era.
UR - http://www.scopus.com/inward/record.url?scp=85056485901&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85056485901&partnerID=8YFLogxK
U2 - 10.1016/j.commatsci.2018.11.001
DO - 10.1016/j.commatsci.2018.11.001
M3 - Article
AN - SCOPUS:85056485901
VL - 158
SP - 42
EP - 48
JO - Computational Materials Science
JF - Computational Materials Science
SN - 0927-0256
ER -