The University of California has awarded $18 million in multicampus research grants, in partnership with UC-managed national laboratories, to advance the frontiers of artificial intelligence in areas of strategic importance and technological innovation.

加州大学（University of California）与加州大学管理的国家实验室合作，提供了1800万美元的多校区研究经费，以推进人工智能在战略重要性和技术创新领域的前沿。

The Artificial Intelligence (AI) Science at Scale initiative provides three grants of $6 million over three years to develop new AI approaches in genomics, quantum materials discovery and geothermal energy. The winning teams are composed of UC faculty across a wide range of disciplines, representing nearly every UC campus. This research is funded by fee income the university receives for managing the Los Alamos and Lawrence Livermore National Laboratories.

人工智能（AI）大规模科学计划在三年内提供三笔600万美元的赠款，用于开发基因组学、量子材料发现和地热能方面的新人工智能方法。获奖团队由加州大学各个学科的教师组成，几乎代表了加州大学的每个校区。这项研究的资金来自大学管理洛斯阿拉莫斯和劳伦斯利弗莫尔国家实验室的费用收入。

“The rapid growth of artificial intelligence is accelerating both the opportunities for, and threats to, the United States’ longstanding economic leadership. To help keep America in the lead, the University of California is scaling up its commitments in critical emerging areas of scientific research, such as AI, and moving forward with unprecedented speed to fund targeted research that fosters innovation,” said Theresa Maldonado, UC Vice President of Research & Innovation.

“人工智能的快速发展正在加速美国长期以来的经济领导地位所面临的机遇和威胁。为了帮助美国保持领先地位，加州大学正在加大对人工智能等关键新兴科学研究领域的投入，并以前所未有的速度向前推进，为促进创新的有针对性的研究提供资金，”加州大学研究与创新副校长特雷莎·马尔多纳多（Theresa Maldonado）说。

“This special initiative brings together the unmatched academic expertise of UC faculty, world-class scientific talent and capabilities at our national labs, and the strategic leadership within our system to accelerate the scale, reach and impact of AI-powered scientific discovery research that benefits the nation,” said June Yu, Vice President of UC National Laboratories.

加州大学国家实验室副院长June Yu表示：“这一特殊计划汇集了加州大学教师无与伦比的学术专长、我们国家实验室的世界级科学人才和能力，以及我们系统内的战略领导力，以加速人工智能科学发现研究的规模、范围和影响，从而造福国家。”

Through the Collaborative Research and Training Awards funded by the UC National Laboratories Fees Research Program, UC has directed lab management fee income towards strategic areas of scientific and national security importance since 2017. More than $92 million has funded the pursuit of breakthroughs in fields including cybersecurity, quantum computing, and wildfire prevention and preparedness.

通过加州大学国家实验室费用研究计划资助的合作研究和培训奖，加州大学自2017年以来一直将实验室管理费收入用于科学和国家安全重要性的战略领域。超过9200万美元的资金用于追求网络安全、量子计算和野火预防和准备等领域的突破。

The AI Science at Scale initiative represents an evolution of this model for creating strategic, impactful and timely research and educational partnerships at scale to further the missions of the Department of Energy and the university, leveraging the domain experts and scientific leadership at the campuses and the national labs. Winning projects were selected from a competitive pool of 27 applications, representing nearly 250 UC faculty.

大规模人工智能科学计划代表了这一模式的演变，旨在创建大规模的战略，有影响力和及时的研究和教育合作伙伴关系，以进一步推进能源部和大学的使命，利用校园和国家实验室的领域专家和科学领导。获奖项目是从27份申请中选出的，代表了加州大学近250名教员。

These are the three winning projects:

以下是三个获奖项目：

AI-Driven Genomics at Scale: from Sequence to Function to Therapeutics

人工智能驱动的大规模基因组学：从序列到功能再到治疗

Principal investigator: Jimmie Ye, Associate Professor of Medicine at UC San Francisco

首席研究员：Jimmie Ye，加州大学旧金山分校医学副教授

Scientists from four UC campuses will use Livermore Lab’s supercomputer to gain new understandings of how certain proteins on the surface of cells may contribute to or protect against disease, starting with blood cancers and autoimmune disorders. Ultimately, this work aims to use artificial intelligence at scale to accelerate personalized medicine and rapidly create treatments for complex diseases.

来自加州大学四个校区的科学家们将利用利弗莫尔实验室的超级计算机，从血癌和自身免疫性疾病开始，对细胞表面的某些蛋白质如何促进或预防疾病有新的认识。最终，这项工作的目标是大规模使用人工智能来加速个性化医疗，并快速创建复杂疾病的治疗方法。

Low-Energy, AI-Informed Phase Transitions (LEAP)

低能量、人工智能支持的相变（LEAP）

Principal investigator: Ram Seshadri, Associate Dean for Research and Director of the Materials Research Laboratory at UC Santa Barbara

首席研究员：Ram Seshadri，研究副院长兼加州大学圣巴巴拉分校材料研究实验室主任

UC faculty will accelerate research into more efficient AI — with help from AI. Using a mix of theory, real-world data and simulations, they’ll train a large language model AI designed to simulate a material’s effectiveness for topological quantum computing. By narrowing the field of materials that will be tested in the lab, this AI could speed up the discovery of new chip materials that switch faster and use less energy.

加州大学教员将在人工智能的帮助下，加速对更高效人工智能的研究。他们将使用理论、现实世界数据和模拟相结合的方法，训练一个大型语言模型人工智能，旨在模拟材料在拓扑量子计算中的有效性。通过缩小将在实验室测试的材料范围，这种人工智能可以加速发现新的芯片材料，这些材料可以更快地转换，使用更少的能源。

Geophysicist.AI: A Foundation Model to Address Multiphysics Challenges in Coupled Subsurface Processes

地球物理学人工智能：解决耦合地下过程中多物理场挑战的基础模型

Principal investigator: Mohammad Qomi, Associate Professor of Civil and Environmental Engineering, Materials Science and Engineering at UC Irvine

首席研究员：Mohammad Qomi，加州大学欧文分校土木与环境工程、材料科学与工程副教授

The Earth’s crust is complex and hard to model accurately with existing computational methods. That makes it hard to determine how to tap into underground thermal energy. A team from five UC campuses will blend large language models, physics-based models, and real-world data from geothermal drilling sites across the Western U.S. into an AI model that could lead to safer, more efficient geothermal energy.

地壳是复杂的，很难用现有的计算方法精确地建模。这使得确定如何利用地下热能变得困难。来自加州大学五个校区的一个团队将把大型语言模型、基于物理的模型和来自美国西部地热钻探点的真实数据融合到一个人工智能模型中，该模型可能会带来更安全、更高效的地热能源。