Shedding new light on topological lattices
Technical note
Mariana Rajado Silva, Thilo Stöferle, and Ioannis Georgakilas
Science
Our focus extends beyond just increasing the rate of scientific discovery; it's about laying new technological foundations that can be applied across multiple fields, driving significant advancements in climate science, materials discovery, healthcare, and more.
Our work
Remembering Dick Garwin, renowned engineer and advisor
NewsMike MurphyMiklós Ajtai and Regina Barzilay received IEEE medals for applying unexpected methods to computer science
NewsPeter HessIBM and ESA open-source TerraMind, the best performing generative AI model for Earth observation
ReleaseAshley PetersonSimplifying geospatial AI with TerraTorch 1.0
Technical noteRomeo Kienzler, Juan Bernabé-Moreno, Paolo Fraccaro, Bianca Zadrozny, Campbell Watson, Benedikt Blumenstiel, Joao Lucas de Sousa Almeida, Michael Johnston, Christian Pinto, and Michal MuszynskiIBM and Université de Sherbrooke announce two quantum research chairs
Q & AAlexandre Choquette- See more of our work on Science
Topics
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.Materials Discovery
It can take over 10 years to come up with new materials. At IBM Research, we’re looking to accelerate the discovery process using new AI methods, robotics, the hybrid cloud, and quantum computers. Our goal is to unlock new properties and materials to address global challenges in years not decades.
Publications
Stable iterative refinement for solving linear systems with inaccurate computation
- 2026
- J. Comput. Appl. Math.
MDLab: AI frameworks for Carbon Capture and Battery Materials
- Bruce Elmegreen
- Hendrik Hamann
- et al.
- 2025
- Frontiers in Environmental Science
Avoiding Leakage Poisoning: Concept Interventions Under Distribution Shifts
- Mateo Espinosa Zarlenga
- Gabriele Dominici
- et al.
- 2025
- ICML 2025
AUTOCIRCUIT-RL: Reinforcement Learning-Driven LLM for Automated Circuit Topology Generation
- Prashanth Vijayaraghavan
- Luyao Shi
- et al.
- 2025
- ICML 2025
Modern Methods in Associative Memory
- 2025
- ICML 2025
CIRCUITSYNTH-RL: LLM-Based Circuit Topology Synthesis with RL Refinement
- Prashanth Vijayaraghavan
- Luyao Shi
- et al.
- 2025
- DAC 2025
The fastest path to progress
Watch a new short film on the computing revolutions that are accelerating the rate of scientific discovery like nothing before.
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