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Accelerated Discovery

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

A faster way to screen supply chains for harmful chemicals
Research
Kim Martineau
13 Jun 2025
How to make AI models more accurate: Embrace failure
Research
Peter Hess
13 Jun 2025
The 2024 IBM Research annual letter
Deep Dive
Sriram Raghavan, Mukesh Khare, and Jay Gambetta
30 Jan 2025
Meet IBM’s new family of AI models for materials discovery
News
Kim Martineau
20 Dec 2024
A new tool for accelerating the discovery of new materials
Research
Peter Hess
20 Nov 2024
How IBM Research built a lab for the future of computing
Deep Dive
Mike Murphy
29 Aug 2024
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
Dynamic Multimodal Fusion for Robust Molecular Representation Learning
- - Indra Priyadarsini S
  - Seiji Takeda
  - et al.
- 2025
- ACS Fall 2025
Designing a Multi-Agent AI Framework to Mine Scientific Literature and Experiment Datasets to Support and Enrich Single-Cell Analyses
- - Joao Bettencourt-Silva
  - Natasha Mulligan
  - et al.
- 2025
- ISMB 2025
LEOPARD: Linking Experimental Observations with Predictive Algorithms to reveal Response Dynamics for the single cell
- - Vibha Anand
  - Akira Koseki
  - et al.
- 2025
- ISMB 2025
Regress, Don't Guess: A Regression-like Loss on Number Tokens for Language Models
- - Jonas Zausinger
  - Lars Pennig
  - et al.
- 2025
- ICML 2025
Predicting Glucose Levels in Diabetic Kidney Transplant Recipients Using Multivariate Temporal Modeling Across Clinically Defined Subcohorts
- - Carla Agurto Rios
  - Eduardo Castro
  - et al.
- 2025
- ICDH 2025

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