人工智能论文素材

Intro

The world is currently undergoing an AI revolution that is having profound effects on science and society, brought about by the integration of predictive models.

当前，一场由预测模型的融合所推动的人工智能革命正在席卷全球，深刻影响着科学与社会的方方面面。

The foundational AI models driving this revolution consist of billions of model parameters trained on immense datasets.

驱动这场革命的基础人工智能模型，是基于海量数据集训练而成，包含数十亿个模型参数。

Despite its transformative potential in areas such as energy transmission, magnetic levitation for transportation, and powerful superconducting magnets for medical imaging [2], the development and understanding of new superconducting materials are often constrained by the paucity of large, comprehensive datasets.

尽管超导技术在能源传输、交通磁悬浮以及用于医学成像的高功率超导磁体等领域拥有变革性的应用潜力 [2]，但新型超导材料的研发与机理认知，长期受限于大规模、高质量数据集的缺失。

ML can expedite the discovery of new potential electron-phonon superconductors by accelerating a strategy that has emerged since the theoretical predictions [14–17] and subsequent discovery of high-temperature hydride superconductors [18–20].

机器学习可加速新型潜在电子 - 声子超导体的发现，这一研究策略自高温氢化物超导体的理论预测 [14–17] 及后续实验发现 [18–20] 以来逐步兴起。

The effectiveness of AI models for superconductors hinges on two key factors: the training dataset and the choice of machine learning technique.

超导领域人工智能模型的有效性取决于两大核心因素：训练数据集的质量，以及机器学习技术的选择。