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AI−Empowered materials science and multidisciplinary integration: Cornerstones and pathways from the materials genome to AI4M2
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Tao LÜ1, Jie XIONG1, 2, Shuai CHEN1, 2, Sheng SUN1, Tongyi ZHANG1, *
Science & Technology Review | 2026, 44(14) : 80 - 90
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Science & Technology Review | 2026, 44(14): 80-90
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AI−Empowered materials science and multidisciplinary integration: Cornerstones and pathways from the materials genome to AI4M2
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Tao LÜ1, Jie XIONG1, 2, Shuai CHEN1, 2, Sheng SUN1, Tongyi ZHANG1, *
Affiliations
  • 1Materials Genome Institute, Shanghai University, Shanghai 200444, China
  • 2State Key Laboratory of Advanced Nuclear Materials for Key Equipment, Shanghai University, Shanghai 200444, China
Published: 2026-07-28 doi: 10.3981/j.issn.1000-7857.2026.07.00034
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High−throughput computation, high−throughput experimentation, and database development under the Materials Genome initiative have provided the data and platform foundations for artificial intelligence (AI) in materials science. From the interdisciplinary perspective of materials science, mechanics, computational science, automation, and engineering, this article defines materials/mechanics informatics (AI for Materials and Mechanics, AI4M2) and reviews progress in materials design, microstructure characterization, process optimization, mechanics modeling, self−driving experimentation, and scientific agents. The hard cornerstones of AI4M2 are AI−enabled experimental robots, high−throughput platforms, intelligent characterization equipment, and self−driving laboratories; its soft cornerstones are AI algorithms, multiscale computation, scientific foundation models, digital twins, and scientific software. Data infrastructure connects the two, while scientific agents link models, tools, experiments, and engineering scenarios. In view of insufficient high−quality data, limited physical interpretability and cross−scale generalization, weak experimental closed loops and indigenous software and equipment, and a shortage of interdisciplinary talent, representative practices in China and abroad are compared. Actionable pathways are proposed, including common data standards and interfaces, shared pilot−scale intelligent laboratories, verifiable indigenous software toolchains, engineering−led demonstration projects, and integrated undergraduate−to−doctoral training.

artificial intelligence  /  materials informatics  /  mechanics informatics  /  materials genome  /  scientific agents  /  multidisciplinary integration
Tao LÜ, Jie XIONG, Shuai CHEN, Sheng SUN, Tongyi ZHANG. AI−Empowered materials science and multidisciplinary integration: Cornerstones and pathways from the materials genome to AI4M2[J]. Science & Technology Review, 2026 , 44 (14) : 80 -90 . DOI: 10.3981/j.issn.1000-7857.2026.07.00034
Year 2026 volume 44 Issue 14
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Article Info
doi: 10.3981/j.issn.1000-7857.2026.07.00034
  • Receive Date:2026-07-07
  • Online Date:2026-08-19
  • Published:2026-07-28
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  • Received:2026-07-07
  • Revised:2026-07-20
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    1Materials Genome Institute, Shanghai University, Shanghai 200444, China
    2State Key Laboratory of Advanced Nuclear Materials for Key Equipment, Shanghai University, Shanghai 200444, 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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