title: The Impact of Large Language Models on Scientific Discovery: a
    Preliminary Study using GPT-4
summary: 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.
paper_authors._id: 6552e8ea85642b0948c0e9e4
paper_authors.name: Microsoft Research AI4Science
paper_authors._id: 6552e8ea85642b0948c0e9e5
paper_authors.name: Microsoft Azure Quantum
paper_title: The Impact of Large Language Models on Scientific Discovery: a
    Preliminary Study using GPT-4
paper_summary: 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.
paper_ai_keywords: large language models (LLMs) natural language processing GPT-4 drug discovery biology computational chemistry density functional theory (DFT) molecular dynamics (MD) materials design partial differential equations (PDE) domain-specific expertise expert-driven case assessments benchmark testing scientific concepts scientific relationships domain-specific problems scientific applications knowledge integration tasks scientific numerical calculation abilities scientific prediction capabilities