  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.
