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A new data−driven paradigm for materials research and design: Integrating high−throughput computing and machine learning
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Baisheng SA1, Lintao CHEN1, Linggang ZHU2, Mingli YANG3, Yanjing SU4, Jian ZHOU2, Zhimei SUN2, *, Jianxin XIE4
Science & Technology Review | 2026, 44(14) : 91 - 103
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Science & Technology Review | 2026, 44(14): 91-103
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A new data−driven paradigm for materials research and design: Integrating high−throughput computing and machine learning
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Baisheng SA1, Lintao CHEN1, Linggang ZHU2, Mingli YANG3, Yanjing SU4, Jian ZHOU2, Zhimei SUN2, *, Jianxin XIE4
Affiliations
  • 1College of Materials Science and Engineering, Fuzhou University, Fuzhou 350108, China
  • 2School of Materials Science and Engineering, Beihang University, Beijing 100083, China
  • 3Research Center for Materials Genome Engineering, Sichuan University, Chengdu 610065, China
  • 4School of Advanced Materials Innovation, University of Science and Technology Beijing, Beijing 100083, China
Published: 2026-07-28 doi: 10.3981/j.issn.1000-7857.2026.05.00035
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The rapid development of artificial intelligence is driving a new wave of scientific and technological revolution. In materials science, it is promoting the transformation of data−driven materials R&D systems, represented by Materials Genome Engineering, from an experience−driven model toward an integrated mode combining data−driven approaches with intelligent design. Focusing on the paradigm shift in materials R&D driven by the integration of high−throughput computing and machine learning, this review systematically summarizes the evolution of materials R&D paradigms, the key technological systems of high−throughput computing and machine learning, strategic layouts and progress in platform construction in China and abroad, and analyzes their impacts on the ecosystem of the advanced materials industry, scientific research organization models, and R&D pathways for key materials. High−throughput computing supports materials data generation and large−scale screening, while machine learning facilitates closed−loop iteration in intelligent materials design through rapid pre−screening and knowledge discovery, shifting competition in materials innovation from individual technological capabilities to the integrated capabilities of data resources, computational tools, R&D platforms, and organizational systems. China has initially established a relevant foundation and competitiveness in this field, and future efforts may further focus on improving the autonomy of core software, accumulating high−quality data, refining standard systems, and strengthening the integration of the R&D chain with the industrial chain. The deep integration of high−throughput computing and machine learning is driving the evolution of materials R&D from the fourth paradigm to the fifth paradigm and is continuously reshaping the technological system, industrial ecosystem, and research organization logic of advanced materials innovation.

materials genome engineering  /  high−throughput computation  /  machine learning  /  materials R&D paradigm  /  materials database
Baisheng SA, Lintao CHEN, Linggang ZHU, Mingli YANG, Yanjing SU, Jian ZHOU, Zhimei SUN, Jianxin XIE. A new data−driven paradigm for materials research and design: Integrating high−throughput computing and machine learning[J]. Science & Technology Review, 2026 , 44 (14) : 91 -103 . DOI: 10.3981/j.issn.1000-7857.2026.05.00035
Year 2026 volume 44 Issue 14
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Article Info
doi: 10.3981/j.issn.1000-7857.2026.05.00035
  • Receive Date:2026-05-11
  • Online Date:2026-08-19
  • Published:2026-07-28
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  • Received:2026-05-11
  • Revised:2026-07-06
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Affiliations
    1College of Materials Science and Engineering, Fuzhou University, Fuzhou 350108, China
    2School of Materials Science and Engineering, Beihang University, Beijing 100083, China
    3Research Center for Materials Genome Engineering, Sichuan University, Chengdu 610065, China
    4School of Advanced Materials Innovation, University of Science and Technology Beijing, Beijing 100083, China
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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