Article(id=1148708270006657946, tenantId=1146029695717560320, journalId=1146032081894723586, issueId=1148708265585865399, articleNumber=null, orderNo=null, doi=10.3981/j.issn.2097-0781.2025.01.002, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1736438400000, receivedDateStr=2025-01-10, revisedDate=1739808000000, revisedDateStr=2025-02-18, acceptedDate=null, acceptedDateStr=null, onlineDate=1751802993537, onlineDateStr=2025-07-06, pubDate=1742400000000, pubDateStr=2025-03-20, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1743004800000, onlineIssueDateStr=2025-03-27, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1751802993537, creator=13701087609, updateTime=1774072690746, updator=sys-migrate, issue=Issue{id=1148708265585865399, tenantId=1146029695717560320, journalId=1146032081894723586, year='2025', volume='4', issue='1', pageStart='100', pageEnd='167', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=1, createTime=1751802992481, creator=13701087609, updateTime=1776075019034, updator=13041195026, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1250512523708023313, tenantId=1146029695717560320, journalId=1146032081894723586, issueId=1148708265585865399, language=EN, specialIssueTitle=, coverIllustrator=, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1250512523708023314, tenantId=1146029695717560320, journalId=1146032081894723586, issueId=1148708265585865399, language=CN, specialIssueTitle=新材料前沿：技术创新与未来展望专刊, coverIllustrator=, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=16, endPage=27, ext={EN=ArticleExt(id=1149664174671839395, articleId=1148708270006657946, tenantId=1146029695717560320, journalId=1146032081894723586, language=EN, title=Research and Development Progress of AI-driven Materials and Suggestions, columnId=1149656489310208610, journalTitle=Science and Technology Foresight, columnName=Review and Commentary, runingTitle=null, highlight=null, articleAbstract=

As an important means to accelerate the research and development of new materials and industrial innovation, the intelligent research and development of materials during the whole process promotes the revolutionary transformation of experience-driven material science to the emergence of machine intelligence. This paper examined the technical synergy framework composed of intelligent computing, autonomous experiments, multimodal databases, and domain-specific large models and analyzed the strategic initiatives and divergent implementation paths for research and development of AI-driven materials across different countries. By deconstructing the pivotal roles of large-scale scientific infrastructure, breakthroughs in cross-scale modeling algorithms, and human-machine collaborative knowledge discovery mechanisms, this paper proposed a strategy for intelligence-enhanced discovery grounded in the triadic integration of data, algorithm, and computing power. This strategy offers a systematic solution and a technological evolution framework for establishing an autonomous and controllable paradigm for the research and development of new materials.

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材料全流程智能化研发作为加速新材料研发和产业创新的重要手段，正推动材料科学从经验驱动向机器智能涌现的革命性转变。文章剖析了智能计算、自主实验、多模态数据库与领域大模型构成的技术协同体系，分析了各国在智能驱动的材料研发领域的战略布局特征与实施路径差异。通过解构大科学装置建设、跨尺度建模算法突破及人机协同知识发现机制的核心作用，提出基于“数据-算法-算力”三元融合的智能增强发现战略，以期为构建自主可控的新型材料研发范式提供系统性解决方案与技术演进框架。

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温李阳，博士，助理研究员。主要从事材料数据库构建和材料领域通用大语言模型开发工作。入选江苏省“双创博士”项目、江苏省卓越博士后计划。发表论文10余篇，授权发明专利1件。电子信箱：wenly@szlab.ac.cn。

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温李阳，博士，助理研究员。主要从事材料数据库构建和材料领域通用大语言模型开发工作。入选江苏省“双创博士”项目、江苏省卓越博士后计划。发表论文10余篇，授权发明专利1件。电子信箱：wenly@szlab.ac.cn。

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陈忻，研究员。材料智能研发平台负责人。享受国务院政府特殊津贴专家。主要从事融合材料计算、材料大数据和人工智能大模型的新方法等研究。主持国家科技创新2030—新一代人工智能重大项目、江苏省前沿引领技术基础研究重大项目等。获Stanford Graduate Fellow、Boston University Young Investigato、江苏省“双创人才”等荣誉。发表论文60余篇。授权发明专利30余件。电子信箱：mail.xinchen@gmail.com。

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陈忻，研究员。材料智能研发平台负责人。享受国务院政府特殊津贴专家。主要从事融合材料计算、材料大数据和人工智能大模型的新方法等研究。主持国家科技创新2030—新一代人工智能重大项目、江苏省前沿引领技术基础研究重大项目等。获Stanford Graduate Fellow、Boston University Young Investigato、江苏省“双创人才”等荣誉。发表论文60余篇。授权发明专利30余件。电子信箱：mail.xinchen@gmail.com。

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Nature Communications, 2024, 15: 8826, doi: 10.1038/s41467-24-53204-6., articleTitle=Roboticized AI-assisted microfluidic photocatalytic synthesis and screening up to 10, 000 reactions per day, refAbstract=null), Reference(id=1242114162160107613, tenantId=1146029695717560320, journalId=1146032081894723586, articleId=1148708270006657946, doi=null, pmid=null, pmcid=null, year=2023, volume=2, issue=null, pageStart=505, pageEnd=514, url=null, language=null, rfNumber=[12], rfOrder=11, authorNames=Zhao H T, Chen W, Huang H, journalName=Nature Synthesis, refType=null, unstructuredReference=Zhao H T, Chen W, Huang H, et al. A robotic platform for the synthesis of colloidal nanocrystals[J]. Nature Synthesis, 2023, 2: 505-514., articleTitle=A robotic platform for the synthesis of colloidal nanocrystals, refAbstract=null), Reference(id=1242114162235605086, tenantId=1146029695717560320, journalId=1146032081894723586, articleId=1148708270006657946, doi=null, pmid=null, pmcid=null, year=2025, volume=null, issue=null, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=[13], rfOrder=12, authorNames=DeepSeek-AI, Guo D Y, Yang D J, journalName=arXiv preprint: 2501.12948, refType=null, unstructuredReference=DeepSeek-AI Guo D Y, Yang D J, et al. 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DARWIN series: Domain specific large language models for natural science[DB/OL]. arXiv preprint: 2308.13565, 2023., articleTitle=DARWIN series: Domain specific large language models for natural science, refAbstract=null), Reference(id=1242114162478874723, tenantId=1146029695717560320, journalId=1146032081894723586, articleId=1148708270006657946, doi=10.1039/d4sc00966e, pmid=38846388, pmcid=null, year=2024, volume=15, issue=22, pageStart=8380, pageEnd=8389, url=null, language=null, rfNumber=[17], rfOrder=16, authorNames=Livne M, Miftahutdinov Z, Tutubalina E, journalName=Chemical Science, refType=null, unstructuredReference=Livne M, Miftahutdinov Z, Tutubalina E, et al. nach0: Multimodal natural and chemical languages foundation model[J]. Chemical Science, 2024, 15(22): 8380-8389., articleTitle=nach0: Multimodal natural and chemical languages foundation model, refAbstract=Large Language Models (LLMs) have substantially driven scientific progress in various domains, and many papers have demonstrated their ability to tackle complex problems with creative solutions. Our paper introduces a new foundation model, nach0, capable of solving various chemical and biological tasks: biomedical question answering, named entity recognition, molecular generation, molecular synthesis, attributes prediction, and others. nach0 is a multi-domain and multi-task encoder-decoder LLM pre-trained on unlabeled text from scientific literature, patents, and molecule strings to incorporate a range of chemical and linguistic knowledge. We employed instruction tuning, where specific task-related instructions are utilized to fine-tune nach0 for the final set of tasks. To train nach0 effectively, we leverage the NeMo framework, enabling efficient parallel optimization of both base and large model versions. Extensive experiments demonstrate that our model outperforms state-of-the-art baselines on single-domain and cross-domain tasks. Furthermore, it can generate high-quality outputs in molecular and textual formats, showcasing its effectiveness in multi-domain setups.This journal is © The Royal Society of Chemistry.), Reference(id=1242114162554372196, tenantId=1146029695717560320, journalId=1146032081894723586, articleId=1148708270006657946, doi=null, pmid=null, pmcid=null, year=2024, volume=null, issue=null, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=[18], rfOrder=17, authorNames=Zhang D, Liu W, Tan Q, journalName=arXiv preprint: 2402.06852, refType=null, unstructuredReference=Zhang D, Liu W, Tan Q, et al. ChemLLM: A chemical large language model[DB/OL]. arXiv preprint: 2402.06852, 2024., articleTitle=ChemLLM: A chemical large language model, refAbstract=null), Reference(id=1242114162608898149, tenantId=1146029695717560320, journalId=1146032081894723586, articleId=1148708270006657946, doi=null, pmid=null, pmcid=null, year=2024, volume=null, issue=null, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=[19], rfOrder=18, authorNames=Li J, Liu W, Ding Z, journalName=arXiv preprint: 2403.04197, refType=null, unstructuredReference=Li J, Liu W, Ding Z, et al. Large language models are in-context molecule learners[DB/OL]. arXiv preprint: 2403.04197, 2024., articleTitle=Large language models are in-context molecule learners, refAbstract=null), Reference(id=1242114162676007014, tenantId=1146029695717560320, journalId=1146032081894723586, articleId=1148708270006657946, doi=null, pmid=null, pmcid=null, year=2024, volume=null, issue=null, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=[20], rfOrder=19, authorNames=Liu Y Y, Ding S R, Zhou S, journalName=arXiv preprint: 2406.12950, refType=null, unstructuredReference=Liu Y Y, Ding S R, Zhou S, et al. MolecularGPT: Open large language model (LLM) for few-shot molecular property prediction[DB/OL]. arXiv preprint: 2406.12950, 2024., articleTitle=MolecularGPT: Open large language model (LLM) for few-shot molecular property prediction, refAbstract=null), Reference(id=1242114162734727271, tenantId=1146029695717560320, journalId=1146032081894723586, articleId=1148708270006657946, doi=null, pmid=null, pmcid=null, year=2024, volume=null, issue=null, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=[21], rfOrder=20, authorNames=Zhao Z H, Ma D, Chen L, journalName=arXiv preprint: 2401.14818, refType=null, unstructuredReference=Zhao Z H, Ma D, Chen L, et al. ChemDFM: A large language foundation model for chemistry[DB/OL]. arXiv preprint: 2401.14818, 2024., articleTitle=ChemDFM: A large language foundation model for chemistry, refAbstract=null), Reference(id=1242114162797641832, tenantId=1146029695717560320, journalId=1146032081894723586, articleId=1148708270006657946, doi=null, pmid=null, pmcid=null, year=2024, volume=null, issue=null, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=[22], rfOrder=21, authorNames=Huang Y Q, Zhang R Y, He X S, journalName=arXiv preprint: 2409.13989, refType=null, unstructuredReference=Huang Y Q, Zhang R Y, He X S, et al. ChemEval: A comprehensive multi-level chemical evaluation for large language models[DB/OL]. arXiv preprint: 2409.13989, 2024., articleTitle=ChemEval: A comprehensive multi-level chemical evaluation for large language models, refAbstract=null), Reference(id=1242114162868945001, tenantId=1146029695717560320, journalId=1146032081894723586, articleId=1148708270006657946, doi=null, pmid=null, pmcid=null, year=2024, volume=67, issue=12, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=[23], rfOrder=22, authorNames=Zhao Z H, Chen B, Li J P, journalName=Science China Information Sciences, refType=null, unstructuredReference=Zhao Z H, Chen B, Li J P, et al. ChemDFM-X: Towards large multimodal model for chemistry[J]. Science China Information Sciences, 2024, 67(12): 220109, doi: 10.1007/s11432-024-4243-0., articleTitle=ChemDFM-X: Towards large multimodal model for chemistry, refAbstract=null), Reference(id=1242114162940248170, tenantId=1146029695717560320, journalId=1146032081894723586, articleId=1148708270006657946, doi=null, pmid=null, pmcid=null, year=2023, volume=32, issue=11, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=[24], rfOrder=23, authorNames=Chen Z X, Xie F K, Wan M, journalName=Chinese Physics B, refType=null, unstructuredReference=Chen Z X, Xie F K, Wan M, et al. MatChat: A large language model and application service platform for materials science[J]. Chinese Physics B, 2023, 32(11): 118104, doi: 10.1088/1674-1056/ad04cb., articleTitle=MatChat: A large language model and application service platform for materials science, refAbstract=null), Reference(id=1242114163003162731, tenantId=1146029695717560320, journalId=1146032081894723586, articleId=1148708270006657946, doi=null, pmid=null, pmcid=null, year=2024, volume=53, issue=7, pageStart=431, pageEnd=441, url=null, language=null, rfNumber=[25], rfOrder=24, authorNames=张林峰, 王涵, journalName=物理, refType=null, unstructuredReference=张林峰, 王涵. 模拟微观世界: 从薛定谔方程到大原子模型[J]. 物理, 2024, 53(7): 431-441., articleTitle=模拟微观世界: 从薛定谔方程到大原子模型, refAbstract=null), Reference(id=1242114163078660204, tenantId=1146029695717560320, journalId=1146032081894723586, articleId=1148708270006657946, doi=null, pmid=null, pmcid=null, year=2024, volume=53, issue=7, pageStart=431, pageEnd=441, url=null, language=null, rfNumber=[25], rfOrder=25, authorNames=Zhang L F, Wang H, journalName=Physics, refType=null, unstructuredReference=Zhang L F, Wang H. Simulating the microscopic world: From the Schrödinger equation to the large atomic model[J]. Physics, 2024, 53(7): 431-441. (in Chinese), articleTitle=Simulating the microscopic world: From the Schrödinger equation to the large atomic model, refAbstract=null), Reference(id=1242114163149963373, tenantId=1146029695717560320, journalId=1146032081894723586, articleId=1148708270006657946, doi=10.1038/s43588-022-00349-3, pmid=38177366, pmcid=null, year=2022, volume=2, issue=11, pageStart=718, pageEnd=728, url=null, language=null, rfNumber=[26], rfOrder=26, authorNames=Chen C, Ong S P, journalName=Nature Computational Science, refType=null, unstructuredReference=Chen C, Ong S P. A universal graph deep learning interatomic potential for the periodic table[J]. Nature Computational Science, 2022, 2(11): 718-728., articleTitle=A universal graph deep learning interatomic potential for the periodic table, refAbstract=Interatomic potentials (IAPs), which describe the potential energy surface of atoms, are a fundamental input for atomistic simulations. However, existing IAPs are either fitted to narrow chemistries or too inaccurate for general applications. Here we report a universal IAP for materials based on graph neural networks with three-body interactions (M3GNet). The M3GNet IAP was trained on the massive database of structural relaxations performed by the Materials Project over the past ten years and has broad applications in structural relaxation, dynamic simulations and property prediction of materials across diverse chemical spaces. About 1.8 million materials from a screening of 31 million hypothetical crystal structures were identified to be potentially stable against existing Materials Project crystals based on M3GNet energies. Of the top 2,000 materials with the lowest energies above the convex hull, 1,578 were verified to be stable using density functional theory calculations. These results demonstrate a machine learning-accelerated pathway to the discovery of synthesizable materials with exceptional properties.© 2022. The Author(s), under exclusive licence to Springer Nature America, Inc.), Reference(id=1242114163208683630, tenantId=1146029695717560320, journalId=1146032081894723586, articleId=1148708270006657946, doi=null, pmid=null, pmcid=null, year=2023, volume=5, issue=null, pageStart=1031, pageEnd=1041, url=null, language=null, rfNumber=[27], rfOrder=27, authorNames=Deng B W, Zhong P C, Jun K, journalName=Nature Machine Intelligence, refType=null, unstructuredReference=Deng B W, Zhong P C, Jun K, et al. CHGNet as a pretrained universal neural network potential for charge-informed atomistic modelling[J]. Nature Machine Intelligence, 2023, 5: 1031-1041., articleTitle=CHGNet as a pretrained universal neural network potential for charge-informed atomistic modelling, refAbstract=null), Reference(id=1242114163300958319, tenantId=1146029695717560320, journalId=1146032081894723586, articleId=1148708270006657946, doi=null, pmid=null, pmcid=null, year=2023, volume=624, issue=7990, pageStart=80, pageEnd=85, url=null, language=null, rfNumber=[28], rfOrder=28, authorNames=Merchant A, Batzner S, Schoenholz S S, journalName=Nature, refType=null, unstructuredReference=Merchant A, Batzner S, Schoenholz S S, et al. Scaling deep learning for materials discovery[J]. Nature, 2023, 624(7990): 80-85., articleTitle=Scaling deep learning for materials discovery, refAbstract=null), Reference(id=1242114163380650096, tenantId=1146029695717560320, journalId=1146032081894723586, articleId=1148708270006657946, doi=null, pmid=null, pmcid=null, year=2024, volume=10, issue=null, pageStart=293, pageEnd=null, url=null, language=null, rfNumber=[29], rfOrder=29, authorNames=Zhang D, Liu X, Zhang X Y, journalName=NPJ Computational Materials, refType=null, unstructuredReference=Zhang D, Liu X, Zhang X Y, et al. DPA-2: A large atomic model as a multi-task learner[J]. NPJ Computational Materials, 2024, 10: 293, doi: 10.1038/s41524-024-01493-2., articleTitle=DPA-2: A large atomic model as a multi-task learner, refAbstract=null)], funds=[Fund(id=1242114159760965712, tenantId=1146029695717560320, journalId=1146032081894723586, articleId=1148708270006657946, awardId=2023ZD0120700, language=CN, fundingSource=新一代人工智能国家科技重大专项(2023ZD0120700), fundOrder=null, country=null), Fund(id=1242114159823880273, tenantId=1146029695717560320, journalId=1146032081894723586, articleId=1148708270006657946, awardId=QN202420, language=CN, fundingSource=苏州实验室青年人才战略研究课题(QN202420), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1242114158301347892, tenantId=1146029695717560320, journalId=1146032081894723586, articleId=1148708270006657946, xref=null, ext=[AuthorCompanyExt(id=1242114158309736501, tenantId=1146029695717560320, journalId=1146032081894723586, articleId=1148708270006657946, companyId=1242114158301347892, language=EN, country=null, province=null, 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