斯坦福大学与美国能源部SLAC国家加速器实验室的科学家们携手合作，研发了一种人工智能（AI）方法，旨在加速目标材料的发现过程，这一创新为“自动驾驶实验”奠定了基础。传统的材料发现工作既费时又成本高昂，面临材料空间庞大、可能组合繁多的挑战。此次研发的AI方法通过有效收集数据，提高了材料设计的精度与速度，显著减少了实验步骤，极大地提升了研究效率。
这项AI方法的核心是一个框架，通过简单的用户定义过滤算法来捕获实验目标。这些算法能自动转换为三种智能、无参数、顺序数据采集策略，包括SwitchBAX、InfoBAX和MeanBAX，这些策略有效地绕过了设计特定采集函数的繁琐过程。研究团队在TiO2纳米粒子合成和磁性材料表征的数据集上验证了这一方法，结果显示其效率远超当前最先进的技术。
该方法不仅加速了新材料的发现，还为应对气候变化、量子计算和药物设计等领域提供了潜在的解决方案。研究成果以“Targeted materials discovery using Bayesian algorithm execution”为题，于7月18日发表在《npj Computational Materials》杂志上。这一突破性进展将有助于加速科学探索，推动人工智能在材料科学领域的应用，为未来的自动驾驶实验提供有力支持。
研究人员提出的方法不仅提高了实验效率，还为科学家提供了一个直观的界面，使得复杂的实验目标得以清晰、简单地表达。通过该方法，科学家能够定制各种自定义用户定义算法，评估BAX框架是否适合指导实际材料实验。这一创新不仅促进了材料科学领域的研究，也为自动驾驶技术的发展开辟了新的路径。
英语如下：
News Title: “AI Boosts Material Discovery, Laying Foundation for Autonomous Driving”
Keywords: AI acceleration, material discovery, autonomous driving experiments
News Content: Scientists from Stanford University and the SLAC National Accelerator Laboratory, a part of the U.S. Department of Energy, have collaborated to develop an artificial intelligence (AI) method aimed at accelerating the process of identifying target materials. This innovative approach lays the groundwork for “autonomous driving experiments.” Traditional material discovery efforts are time-consuming and expensive, often grappling with the vast space of possible materials and combinations. The AI method, through efficient data collection, has significantly improved the precision and speed of material design, drastically reducing the number of experimental steps and enhancing research efficiency.
The core of this AI method is a framework that captures experimental objectives through simple user-defined filtering algorithms. These algorithms automatically translate into three intelligent, parameter-free sequential data acquisition strategies: SwitchBAX, InfoBAX, and MeanBAX. These strategies effectively sidestep the complex process of designing specific data collection functions. The research team validated this method on datasets related to the synthesis of TiO2 nanoparticles and the characterization of magnetic materials, demonstrating its efficiency surpassing the most advanced technologies currently available.
This AI method not only accelerates the discovery of new materials but also offers potential solutions to challenges in addressing climate change, quantum computing, and drug design. The findings, titled “Targeted materials discovery using Bayesian algorithm execution,” were published on July 18 in the journal npj Computational Materials. This breakthrough will facilitate scientific exploration, promote the application of AI in materials science, and provide strong support for future autonomous driving experiments.
The proposed method not only boosts experimental efficiency but also provides scientists with an intuitive interface to clearly and simply express complex experimental objectives. With this method, scientists can customize various user-defined algorithms and evaluate the suitability of the BAX framework for guiding actual material experiments. This innovation not only advances research in the field of materials science but also opens new paths for the development of autonomous driving technology.
【来源】https://www.jiqizhixin.com/articles/2024-07-29-8
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