MatPilot: an LLM-enabled AI Materials Scientist under the Framework of Human-Machine Collaboration

• URL: http://arxiv.org/abs/2411.08063v1
• Date: Sun, 10 Nov 2024 12:23:44 GMT
• Title: MatPilot: an LLM-enabled AI Materials Scientist under the Framework of Human-Machine Collaboration
• Authors: Ziqi Ni, Yahao Li, Kaijia Hu, Kunyuan Han, Ming Xu, Xingyu Chen, Fengqi Liu, Yicong Ye, Shuxin Bai,
• Abstract summary: We developed an AI materials scientist named MatPilot, which has shown encouraging abilities in the discovery of new materials. The core strength of MatPilot is its natural language interactive human-machine collaboration. MatPilot integrates unique cognitive abilities, extensive accumulated experience, and ongoing curiosity of human-beings.
• Score: 13.689620109856783
• License:
• Abstract: The rapid evolution of artificial intelligence, particularly large language models, presents unprecedented opportunities for materials science research. We proposed and developed an AI materials scientist named MatPilot, which has shown encouraging abilities in the discovery of new materials. The core strength of MatPilot is its natural language interactive human-machine collaboration, which augments the research capabilities of human scientist teams through a multi-agent system. MatPilot integrates unique cognitive abilities, extensive accumulated experience, and ongoing curiosity of human-beings with the AI agents' capabilities of advanced abstraction, complex knowledge storage and high-dimensional information processing. It could generate scientific hypotheses and experimental schemes, and employ predictive models and optimization algorithms to drive an automated experimental platform for experiments. It turns out that our system demonstrates capabilities for efficient validation, continuous learning, and iterative optimization.

Related papers

• Many Heads Are Better Than One: Improved Scientific Idea Generation by A LLM-Based Multi-Agent System [62.832818186789545]
  Virtual Scientists (VirSci) is a multi-agent system designed to mimic the teamwork inherent in scientific research. VirSci organizes a team of agents to collaboratively generate, evaluate, and refine research ideas. We show that this multi-agent approach outperforms the state-of-the-art method in producing novel scientific ideas.
  arXiv  Detail & Related papers  (2024-10-12T07:16:22Z)
• A Perspective on AI-Guided Molecular Simulations in VR: Exploring Strategies for Imitation Learning in Hyperdimensional Molecular Systems [0.7853804618032806]
  Interactive molecular dynamics in virtual reality (iMD-VR) has recently been developed as a 'human-in-the-loop' strategy. This paper explores the possibility of employing user-generated iMD-VR datasets to train AI agents via imitation learning (IL)
  arXiv  Detail & Related papers  (2024-09-11T11:21:02Z)
• SciAgents: Automating scientific discovery through multi-agent intelligent graph reasoning [0.0]
  A key challenge in artificial intelligence is the creation of systems capable of autonomously advancing scientific understanding. We present SciAgents, an approach that leverages three core concepts. The framework autonomously generates and refines research hypotheses, elucidating underlying mechanisms, design principles, and unexpected material properties. Our case studies demonstrate scalable capabilities to combine generative AI, ontological representations, and multi-agent modeling, harnessing a swarm of intelligence' similar to biological systems.
  arXiv  Detail & Related papers  (2024-09-09T12:25:10Z)
• "Turing Tests" For An AI Scientist [0.0]
  This paper proposes a "Turing test for an AI scientist" to assess whether an AI agent can conduct scientific research independently. We propose seven benchmark tests that evaluate an AI agent's ability to make groundbreaking discoveries in various scientific domains.
  arXiv  Detail & Related papers  (2024-05-22T05:14:27Z)
• Empowering Biomedical Discovery with AI Agents [15.125735219811268]
  We envision "AI scientists" as systems capable of skeptical learning and reasoning. Biomedical AI agents combine human creativity and expertise with AI's ability to analyze large datasets. AI agents can impact areas ranging from virtual cell simulation, programmable control of phenotypes, and the design of cellular circuits to developing new therapies.
  arXiv  Detail & Related papers  (2024-04-03T16:08:01Z)
• Agent-based Learning of Materials Datasets from Scientific Literature [0.0]
  We develop a chemist AI agent, powered by large language models (LLMs), to create structured datasets from natural language text. Our chemist AI agent, Eunomia, can plan and execute actions by leveraging the existing knowledge from decades of scientific research articles.
  arXiv  Detail & Related papers  (2023-12-18T20:29:58Z)
• AI for Mathematics: A Cognitive Science Perspective [86.02346372284292]
  Mathematics is one of the most powerful conceptual systems developed and used by the human species. Rapid progress in AI, particularly propelled by advances in large language models (LLMs), has sparked renewed, widespread interest in building such systems.
  arXiv  Detail & Related papers  (2023-10-19T02:00:31Z)
• A Neuro-mimetic Realization of the Common Model of Cognition via Hebbian Learning and Free Energy Minimization [55.11642177631929]
  Large neural generative models are capable of synthesizing semantically rich passages of text or producing complex images. We discuss the COGnitive Neural GENerative system, such an architecture that casts the Common Model of Cognition.
  arXiv  Detail & Related papers  (2023-10-14T23:28:48Z)
• Large Language Models for Scientific Synthesis, Inference and Explanation [56.41963802804953]
  We show how large language models can perform scientific synthesis, inference, and explanation. We show that the large language model can augment this "knowledge" by synthesizing from the scientific literature. This approach has the further advantage that the large language model can explain the machine learning system's predictions.
  arXiv  Detail & Related papers  (2023-10-12T02:17:59Z)
• The Future of Fundamental Science Led by Generative Closed-Loop Artificial Intelligence [67.70415658080121]
  Recent advances in machine learning and AI are disrupting technological innovation, product development, and society as a whole. AI has contributed less to fundamental science in part because large data sets of high-quality data for scientific practice and model discovery are more difficult to access. Here we explore and investigate aspects of an AI-driven, automated, closed-loop approach to scientific discovery.
  arXiv  Detail & Related papers  (2023-07-09T21:16:56Z)
• Simulation Intelligence: Towards a New Generation of Scientific Methods [81.75565391122751]
  "Nine Motifs of Simulation Intelligence" is a roadmap for the development and integration of the essential algorithms necessary for a merger of scientific computing, scientific simulation, and artificial intelligence. We argue the motifs of simulation intelligence are interconnected and interdependent, much like the components within the layers of an operating system. We believe coordinated efforts between motifs offers immense opportunity to accelerate scientific discovery.
  arXiv  Detail & Related papers  (2021-12-06T18:45:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.