In recent years, groundbreaking advancements in natural language processing have culminated in the emergence of powerful large language models (LLMs), which have showcased remarkable capabilities across a vast array of domains, including the understanding, generation, and translation of natural language, and even tasks that extend beyond language processing. In this report, we delve into the performance of LLMs within the context of scientific discovery, focusing on GPT-4, the state-of-the-art language model. Our investigation spans a diverse range of scientific areas encompassing drug discovery, biology, computational chemistry (density functional theory (DFT) and molecular dynamics (MD)), materials design, and partial differential equations (PDE). Evaluating GPT-4 on scientific tasks is crucial for uncovering its potential across various research domains, validating its domain-specific expertise, accelerating scientific progress, optimizing resource allocation, guiding future model development, and fostering interdisciplinary research. Our exploration methodology primarily consists of expert-driven case assessments, which offer qualitative insights into the model's comprehension of intricate scientific concepts and relationships, and occasionally benchmark testing, which quantitatively evaluates the model's capacity to solve well-defined domain-specific problems. Our preliminary exploration indicates that GPT-4 exhibits promising potential for a variety of scientific applications, demonstrating its aptitude for handling complex problem-solving and knowledge integration tasks. Broadly speaking, we evaluate GPT-4's knowledge base, scientific understanding, scientific numerical calculation abilities, and various scientific prediction capabilities.

翻译：近年来，自然语言处理领域的突破性进展催生了强大的大型语言模型（LLMs），这些模型在诸多领域展现出卓越能力，包括自然语言的理解、生成与翻译，甚至涉及超越语言处理范畴的任务。本报告深入探讨了LLMs在科学发现背景下的表现，重点聚焦当前最先进的语言模型GPT-4。我们的研究覆盖了药物发现、生物学、计算化学（密度泛函理论（DFT）与分子动力学（MD））、材料设计以及偏微分方程（PDE）等多个科学领域。评估GPT-4在科学任务上的表现，对于揭示其在各研究领域的潜力、验证其领域专业知识、加速科学进展、优化资源配置、指导未来模型开发以及促进跨学科研究具有关键意义。我们的探索方法主要包括专家驱动的案例评估——这一定性分析手段可揭示模型对复杂科学概念及其关系的理解程度，以及偶尔采用的基准测试——用于定量评估模型解决特定领域明确定义问题的能力。初步探索表明，GPT-4在多种科学应用中展现出令人期待的前景，验证了其处理复杂问题求解与知识整合任务的潜力。总体而言，我们评估了GPT-4的知识储备、科学理解力、科学计算能力以及各类科学预测能力。