Reference number : 172-2023-2024-Q4-1015961, amendment number : , agreement type : G, recipient legal name : University of Waterloo, recipient country : CA, recipient province : ON, recipient city : Waterloo|Waterloo, recipient postal code : N2L 3G1, program name english : Collaborative Science, Technology and Innovation Program - Collaborative R&D Initiatives, program name french : Programme de collaboration en science, en technologie et en innovation - Initiatives de collaboration en R-D, program purpose english : Collaborate on multiparty research and development programs to catalyze transformative, high-risk, high-reward research with the potential for game-changing scientific discoveries and technological breakthroughs in priority areas., program purpose french : Collaborer à des programmes multipartites de recherche-développement dans le but de profiter de la synergie entre la recherche transformatrice à haut risque et à haut rendement et le potentiel de découvertes scientifiques et de percées technologiques révolutionnaires dans des secteurs d’intervention prioritaires., agreement title english : Accelerating scientific discovery with large language models, agreement title french : Accélérer les découvertes scientifiques grâce à de grands modèles de langage, agreement number : 1015961, agreement value : 130000.0, agreement start date : 2024-03-22, agreement end date : 2026-03-31, description english : Scientific discovery, by nature, is a lengthy and arduous journey largely due to the vastness of its exploratory scope. To illustrate, the design of a 100 amino-acid protein sequence entails the exploration of mind-boggling 20^100 possibilities. Historical approaches to scientific discovery in realms such as protein, molecular, and material design have relied heavily on the artful blend of intuition, experience, and a dash of serendipity. These empirical-driven methods, while valuable, are markedly time-intensive and laden with numerous unsuccessful attempts. The advent of artificial intelligence (AI) promised a new velocity in the discovery process. Yet, prior efforts have generally centered on small-scale models with constrained generalizability, resulting in suboptimal outcomes. The emergence of large language models (LLMs), with their remarkable reasoning and generalization prowess, holds the potential to catalyze a seismic shift in this field, thereby dramatically streamlining the scientific discovery journey. The project aims to accelerate scientific discovery with big data. By utilizing the currently popular large foundation models, it will be possible to unleash their generalization capabilities to help researchers significantly shrink the search space. By leveraging AI techniques in the process, it can make the search process more explainable and systematic., description french : Accélérer les découvertes scientifiques grâce à de grands modèles de langage, expected results english : In the short term, anticipated outcomes will be strengthened collaborations across industry, academia, and government to support research excellence. In the medium term, anticipated outcomes will be the development of new and potentially disruptive technologies with collaborators. In the long term, find collaborative solutions to public policy challenges and create stronger innovation systems., expected results french : À court terme, les résultats prévus seront des collaborations renforcées entre l'industrie, le milieu universitaire et le gouvernement pour soutenir l'excellence en recherche. À moyen terme, les résultats attendus seront le développement de nouvelles technologies potentiellement perturbatrices avec des collaborateurs. À long terme, trouver des solutions collaboratives aux défis des politiques publiques et créer des systèmes d'innovation plus solides., additional information english : nan, additional information french : nan, owner organization : nrc-cnrc, owner organization title : National Research Council Canada | Conseil national de recherches Canada