The 2024 IBM Research annual letter
Deep Dive
Sriram Raghavan, Mukesh Khare, and Jay Gambetta
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
Meet IBM’s new family of AI models for materials discovery
NewsKim MartineauA new tool for accelerating the discovery of new materials
ResearchPeter HessHow IBM Research built a lab for the future of computing
Deep DiveMike MurphyMitigating the environmental harm of PFAS ‘forever chemicals’
NewsKim MartineauIBM and NASA build language models to make scientific knowledge more accessible
Technical noteBishwaranjan Bhattacharjee, Aashka Trivedi, Masayasu Muraoka, Bharath Dandala, Rong Zhang, and Yousef El-Kurdi- See more of our work on Accelerated Discovery
Projects
Creating the AI-enabled lab for a new era of reproducible and collaborative experimentation
Enhance scientific discovery with multimmodal knowledge
Leveraging our expertise in materials science, AI, quantum and high performance computing, we're developing a more powerful, sustainable, and energy-efficient battery.
Publications
MDLab: AI frameworks for Carbon Capture and Battery Materials
- Bruce Elmegreen
- Hendrik Hamann
- et al.
- 2025
- Frontiers in Environmental Science
Fast And Accurate Machine Learning Prediction of Back-End-Of-Line Thermal Resistances in Backside Power Delivery and Chiplet Architectures
- Prabudhya Roy Chowdhury
- Aakrati Jain
- et al.
- 2025
- ECTC 2025
How AI can accelerate Materials Discovery for CO2 Capture
- 2025
- Canadian Separations Symposium 2025
Opportunities of Scalable and Electrostatically Optimized Electrodes for Electric Field- and Current-Driven Microfluidic Applications
- 2025
- Biomicrofluidics
Are Large Language Models Effective in Clinical Trial Design? A Study on Baseline Feature Generation
- Nafis Neehal
- Bowen Wang
- et al.
- 2025
- NAACL 2025
A new framework for evaluating model out-of-distribution generalisation for the biochemical domain
- 2025
- ICLR 2025