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The world is changing rapidly every day, and the way we used to solve problems won’t cut it anymore. At IBM Research, we’re combining our expertise in quantum computing, AI, and hybrid cloud to drastically increase how quickly we can discover solutions to tackle today’s most urgent problems.

Our work

• The 2024 IBM Research annual letter

Sriram Raghavan, Mukesh Khare, and Jay Gambetta
30 Jan 2025

• Accelerated Discovery
• AI
• Quantum
• Semiconductors

• Meet IBM’s new family of AI models for materials discovery

• Accelerated Discovery
• AI
• Foundation Models
• Generative AI
• Materials Discovery

• A new tool for accelerating the discovery of new materials

• Accelerated Discovery
• AI
• Materials Discovery
• Science

• How IBM Research built a lab for the future of computing

• Accelerated Discovery
• AI
• Hybrid Cloud
• Quantum
• Semiconductors

• Mitigating the environmental harm of PFAS ‘forever chemicals’

• Accelerated Discovery
• AI
• Exploratory Science
• Foundation Models
• Generative AI
• Natural Language Processing

• IBM and NASA build language models to make scientific knowledge more accessible

Bishwaranjan Bhattacharjee, Aashka Trivedi, Masayasu Muraoka, Bharath Dandala, Rong Zhang, and Yousef El-Kurdi
12 Mar 2024

• Accelerated Discovery
• AI
• Generative AI
• Granite
• Science
• See more of our work on Accelerated Discovery

Projects

AI for Scientific Discovery

Creating the AI-enabled lab for a new era of reproducible and collaborative experimentation

Deep Search

Knowledge Enhanced Accelerated Discovery

Enhance scientific discovery with multimmodal knowledge

Accelerated Discovery of Battery Materials

Leveraging our expertise in materials science, AI, quantum and high performance computing, we're developing a more powerful, sustainable, and energy-efficient battery.

View more projects

Publications

• MDLab: AI frameworks for Carbon Capture and Battery Materials

  • • Bruce Elmegreen
      • Hendrik Hamann
      • et al.
  • 2025
  • Frontiers in Environmental Science

• TDiMS : A Topological Distance based Intra-Molecular Substructure Descriptor for Improved Machine Learning Predictions

  • • Lisa Hamada
      • Indra Priyadarsini S
      • et al.
  • 2025
  • AAAI 2025

• Uncertainty Analysis in Predicting Molecular Properties Using Chemical Foundation Models

  • • Siya Kunde
      • Emilio Ashton Vital Brazil
      • et al.
  • 2025
  • AAAI 2025

• Enhancing foundation models for scientific discovery via multimodal knowledge graph representations

  • • Vanessa Lopez
      • Lam Thanh Hoang
      • et al.
  • 2025
  • Journal of Web Semantics

• Foundation models for the electric power grid

  • • Hendrik F. Hamann
      • Blazhe Gjorgiev
      • et al.
  • 2024
  • Joule

• Improving electrolyte performance for target cathode loading using an interpretable data-driven approach

  • • Vidushi Sharma
      • Andy Tek
      • et al.
  • 2024
  • Cell Reports Physical Science

View all publications

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