Precision, intelligence, and a new paradigm for chemical research.

IF 3.1 2区 化学 Q3 CHEMISTRY, PHYSICAL
Shuo Feng, Jun Jiang, Zhenyu Li
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引用次数: 0

Abstract

Chemists have long struggled to precisely regulate and create substances, often relying on trial-and-error methods that are inefficient for complex, high-dimensional research challenges. However, recent advancements in computational and experimental techniques, particularly those with artificial intelligence (AI), are providing new avenues for precision and intelligent chemistry. This perspective highlights the synergistic integration of accurate theoretical simulations, advanced experimental characterization, and AI-driven models, creating a closed-loop system to accelerate chemical discovery and material design. At the core of this framework is an iterative process: precise computational and experimental data lead to advanced intelligent models, which guide the design of optimized reaction parameters or chemical components, and direct robotic platforms that perform reproducible, high-throughput experiments. These experimental data, in turn, provide continuous feedback to refine intelligent models, ultimately enabling precise control of reaction conditions and material properties. To fully realize this vision, we advocate the development of key infrastructures: a multidisciplinary, multimodal, and standardized AI-ready chemical database as a data foundation; a knowledge and logic-enhanced large chemical model for intelligent prediction and design; distributed, full-process robotic laboratories for automated experimentation; and a cloud platform for resource sharing and collaboration. Together, these components constitute a vision for robotic chemist cloud facilities, which will empower researchers with unparalleled capabilities to seamlessly integrate precision and intelligence. This integrated approach promises to accelerate discovery and represents a paradigm shift in chemical research.

精确,智能,化学研究的新范式。
长期以来,化学家们一直在努力精确地调节和创造物质,通常依靠反复试验的方法,这种方法对于复杂、高维的研究挑战来说效率低下。然而,最近计算和实验技术的进步,特别是人工智能(AI)技术的进步,为精确和智能化学提供了新的途径。这一观点强调了精确的理论模拟、先进的实验表征和人工智能驱动模型的协同集成,创造了一个闭环系统,以加速化学发现和材料设计。该框架的核心是一个迭代过程:精确的计算和实验数据导致先进的智能模型,指导优化反应参数或化学成分的设计,并指导机器人平台执行可重复的高通量实验。这些实验数据反过来提供持续的反馈,以完善智能模型,最终实现对反应条件和材料特性的精确控制。为了充分实现这一愿景,我们主张发展关键基础设施:一个多学科、多模式、标准化的人工智能化学数据库作为数据基础;基于知识和逻辑的大型化工模型的智能预测和设计用于自动化实验的分布式、全过程机器人实验室;以及资源共享和协作的云平台。这些组件共同构成了机器人化学家云设施的愿景,这将赋予研究人员无与伦比的能力,以无缝集成精度和智能。这种综合方法有望加速发现,并代表了化学研究的范式转变。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Chemical Physics
Journal of Chemical Physics 物理-物理:原子、分子和化学物理
CiteScore
7.40
自引率
15.90%
发文量
1615
审稿时长
2 months
期刊介绍: The Journal of Chemical Physics publishes quantitative and rigorous science of long-lasting value in methods and applications of chemical physics. The Journal also publishes brief Communications of significant new findings, Perspectives on the latest advances in the field, and Special Topic issues. The Journal focuses on innovative research in experimental and theoretical areas of chemical physics, including spectroscopy, dynamics, kinetics, statistical mechanics, and quantum mechanics. In addition, topical areas such as polymers, soft matter, materials, surfaces/interfaces, and systems of biological relevance are of increasing importance. Topical coverage includes: Theoretical Methods and Algorithms Advanced Experimental Techniques Atoms, Molecules, and Clusters Liquids, Glasses, and Crystals Surfaces, Interfaces, and Materials Polymers and Soft Matter Biological Molecules and Networks.
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