数据+人工智能是材料基因工程的核心
Data + AI: The core of materials genomic engineering
查看参考文献30篇
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文摘
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材料基因工程的工作模式,可大致总结为实验驱动、计算驱动和数据驱动3种。以“数据+人工智能”为标志的数据驱动模式围绕数据产生与数据处理展开,代表了材料基因工程的核心理念与发展方向。材料研究由“试错法”向科学第四范式的根本转变,将更快、更准、更省地获得成分-结构-工艺-性能间的关系。在数据密集型科学时代,快速获取大量材料数据的能力成为关键,而基于高通量实验与高通量计算的“数据工厂”是满足材料基因工程数据需求的重要平台。 |
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其他语种文摘
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The working models of the Materials Genomic Engineering can be roughly classified into those of the experiment-driven, the computation- driven and the data- driven. The last kind of model is consistent with the fourth paradigm of scientific approach of a fundamental change from "trial and error" to "data-intensive". Such a paradigm shift allows one to acquire the composition-structureprocess- performance relationship, as the basis for the rational design of materials, in a faster, cheaper and more accurate way. It represents the core concept and the future direction of the MGI. In this data-centric scientific era, the ability to quickly obtain a large amount of materials data becomes essential. Thus, the "data foundries"-the centralized materials data generation facilities based on high-throughput experiments and high-throughput computations are the key infrastructures for meeting the future data needs. It is contemplated that the data and the artificial intelligence will become the foundation for building the materials science of the future. |
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来源
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科技导报（北京）
,2018,36(14):15-21 【扩展库】
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DOI
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10.3981/j.issn.1000-7857.2018.14.003
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关键词
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材料基因工程
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数据驱动
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高通量实验
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高通量计算
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地址
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1.
上海交通大学材料基因组联合研究中心, 上海, 200240
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上海交通大学材料科学与工程学院, 上海, 200240
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南方科技大学材料科学与工程系, 深圳, 518055
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语种
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中文 |
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文献类型
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研究性论文 |
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ISSN
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1000-7857 |
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学科
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一般工业技术;自动化技术、计算机技术 |
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基金
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国家重点研发计划项目
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上海市科学技术委员会研发平台专项
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文献收藏号
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CSCD:6298024
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参考文献 共
30
共2页
|
|
1.
汪洪. 材料基因组——材料研发新模式.
科技导报,2015,33(10):13-19
|
CSCD被引
29
次
|
|
|
|
|
2.
National Science.
Materials genome initiative for global competitiveness,2018
|
CSCD被引
1
次
|
|
|
|
|
3.
National Science and Technology Council.
MGI strategic plan,2018
|
CSCD被引
1
次
|
|
|
|
|
4.
刘梓葵. 关于材料基因组的基本观点及展望.
科学通报,2013,58(35):3618-3622
|
CSCD被引
22
次
|
|
|
|
|
5.
Green E D. Human Genome Project: Twenty-five years of big biology.
Nature,2015,526(7571):29-31
|
CSCD被引
11
次
|
|
|
|
|
6.
Xiang X D. A combinatorial approach to materials discovery.
Science,1995,268(5218):1738-1740
|
CSCD被引
91
次
|
|
|
|
|
7.
Xing H. Rapid construction of Fe-Co-Ni composition-phase map by combinatorial materials chip approach.
ACS Combinatorial Science,2018,20:127-131
|
CSCD被引
7
次
|
|
|
|
|
8.
Buitrago S A. Organic chemistry, Nanomole-scale high-throughput chemistry for the synthesis of complex molecules.
Science,2015,347(6217):49-53
|
CSCD被引
15
次
|
|
|
|
|
9.
Perera D. A platform for automated nanomole-scale reaction screening and micromole-scale synthesis in flow.
Science,2018,359(6374):429-434
|
CSCD被引
20
次
|
|
|
|
|
10.
Gesmundo N J. Cernak, nanoscale synthesis and affinity ranking.
Nature,2018,557(7704):228-232
|
CSCD被引
6
次
|
|
|
|
|
11.
Ceder G. The stuff of dreams.
Scientific American,2013,6:36-40
|
CSCD被引
1
次
|
|
|
|
|
12.
Allison J. Virtual aluminum castings: An industrial application of ICME.
JOM,2006,58(11):28-35
|
CSCD被引
6
次
|
|
|
|
|
13.
Olson G B. Materials genomics: From CALPHAD to flight.
Scripta Materialia,2014,70(1):25-30
|
CSCD被引
24
次
|
|
|
|
|
14.
Mitchell T. Machine learning and data mining.
Communications of the ACM,1999,42(11):30-36
|
CSCD被引
10
次
|
|
|
|
|
15.
Liu Y. Materials discovery and design using machine learining.
Journal of Materiomics,2017,3(3):159-177
|
CSCD被引
88
次
|
|
|
|
|
16.
Raccuglia P. Machine-learning-assisted materials discovery using failed experiments.
Nature,2013,533(7601):73-76
|
CSCD被引
142
次
|
|
|
|
|
17.
Xue D. Accelerated search for materials with targeted properties by adaptive design.
Nature Communications,2016,7:11241
|
CSCD被引
55
次
|
|
|
|
|
18.
Xue D. Accelerated search for BaTiO_3-based piezoelectrics with vertical morphotropic phase boundary using Bayesian learning.
Proceedings of the National Academy of Sciences,2016,113(47):13301-13306
|
CSCD被引
14
次
|
|
|
|
|
19.
Xue D. An informatics approach to transformation temperatures of NiTi-based shape memory alloys.
Acta Materialia,2017,125:532-541
|
CSCD被引
27
次
|
|
|
|
|
20.
Ren F. Accelerated discovery of metallic glasses through iteration of machine learning and high-throughput experiments.
Science Advances,2018,4:eaaq1566
|
CSCD被引
35
次
|
|
|
|
|
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