Accelerating computational materials discovery with artificial intelligence and cloud high-performance computing: from large-scale screening to experimental validation
Chi Chen, Dan Thien Nguyen, Shannon J. Lee, Nathan A. Baker, Ajay S. Karakoti, Linda Lauw, Craig Owen, Karl T. Mueller, Brian A. Bilodeau, Vijayakumar Murugesan, Matthias Troyer
高通量计算材料发现多年来一直承诺能够显著加速新材料的设计与发现。尽管相关兴趣和活动激增，但大规模计算资源所带来的限制构成了一个重大瓶颈。此外，能够通过实验验证完成的大规模计算发现实例仍然稀缺，尤其是对于具有产品应用价值的材料而言。在此，我们展示了这一愿景如何成为现实：首先在云高性能计算（HPC）资源上结合最先进的人工智能（AI）模型和传统基于物理的模型，快速遍历超过3200万个候选材料，并预测出约50万个潜在稳定的材料。通过聚焦于电池应用中的固态电解质，我们的发现流程进一步识别出18个具有新组成的候选材料，并作为副产品重新发现了该领域十年来的集体知识。通过在云中部署约一千台虚拟机（VM），这一过程耗时不到80小时。随后，我们合成并实验表征了最优候选材料Na$_x$Li$_{3-x}$YCl$_6$（$0 < x < 3$）系列的结构和电导率，证明了这些化合物作为固态电解质的潜力。目前正在实验研究中的其他候选材料可能为Li和Na导电固态电解质新相的计算发现提供更多实例。我们相信，这种协同整合AI模型和云HPC的前所未有的方法不仅加速了材料发现，也展示了AI引导实验在解锁具有现实应用价值的变革性科学突破方面的强大能力。
英文摘要：
High-throughput computational materials discovery has promised significant acceleration of the design and discovery of new materials for many years. Despite a surge in interest and activity, the constraints imposed by large-scale computational resources present a significant bottleneck. Furthermore, examples of large-scale computational discovery carried through experimental validation remain scarce, especially for materials with product applicability. Here we demonstrate how this vision became reality by first combining state-of-the-art artificial intelligence (AI) models and traditional physics-based models on cloud high-performance computing (HPC) resources to quickly navigate through more than 32 million candidates and predict around half a million potentially stable materials. By focusing on solid-state electrolytes for battery applications, our discovery pipeline further identified 18 promising candidates with new compositions and rediscovered a decade's worth of collective knowledge in the field as a byproduct. By employing around one thousand virtual machines (VMs) in the cloud, this process took less than 80 hours. We then synthesized and experimentally characterized the structures and conductivities of our top candidates, the Na$_x$Li$_{3-x}$YCl$_6$ ($0 < x < 3$) series, demonstrating the potential of these compounds to serve as solid electrolytes. Additional candidate materials that are currently under experimental investigation could offer more examples of the computational discovery of new phases of Li- and Na-conducting solid electrolytes. We believe that this unprecedented approach of synergistically integrating AI models and cloud HPC not only accelerates materials discovery but also showcases the potency of AI-guided experimentation in unlocking transformative scientific breakthroughs with real-world applications.
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Accelerating discovery of infrared nonlinear optical materials with large shift current via high-throughput screening
Soochow University（苏州大学）；AI for Science Institute（人工智能科学研究院）；Research Institute for Intelligent Wearable Systems, The Hong Kong Polytechnic University（香港理工大学智能穿戴系统研究所）；Department of Mechanical Engineering, National University of Singapore（新加坡国立大学机械工程系）；Suzhou Institute for Advanced Research, University of Science and Technology of China（中国科学技术大学苏州高等研究院）；School of Biomedical Engineering, Division of Life Sciences and Medicine, University of Science and Technology of China（中国科学技术大学生命科学与医学学院生物医学工程系）
Accelerated Dopant Screening in Oxide Semiconductors via Multi-Fidelity Contextual Bandits and a Three-Tier DFT Validation Funnel
National Institute of Electronics and Information Technology (NIELIT), India（印度国家电子与信息技术研究院（NIELIT））；Indian Institute of Information Technology, Allahabad (IIITA), Prayagraj, India（印度阿拉哈巴德信息技术学院（IIITA），普拉亚格拉杰，印度）
Accelerated Inorganic Electrides Discovery by Generative Models and Hierarchical Screening
University of North Carolina at Charlotte（北卡罗来纳大学夏洛特分校）；North Carolina Battery Complexity, Autonomous Vehicle and Electrification (BATT CAVE) Research Center（北卡罗来纳电池复杂性、自动驾驶与电气化（BATT CAVE）研究中心）
KAN-Enhanced Contrastive Learning Accelerating Crystal Structure Identification from XRD Patterns
Materials Genome Institute, Shanghai University（上海大学材料基因组研究所）；The University of Sydney（悉尼大学）；Shanghai University（上海大学）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
