Sriram Raghavan

Aashka Trivedi

Masayasu Muraoka

Bharath Dandala

Yousef El-Kurdi

Peter Hess

Mukesh Khare

Mike Murphy

Bishwaranjan Bhattacharjee

Darío Gil

Jay Gambetta

Kim Martineau

Rong Zhang

News

Research

Technical note

Deep Dive

The 2023 IBM Research annual letter

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

Mitigating the environmental harm of PFAS ‘forever chemicals’ 

How IBM Research built a lab for the future of computing

A new tool for accelerating the discovery of new materials

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

The 2024 IBM Research annual letter

Enhance  scientific discovery with multimmodal knowledge

Knowledge Enhanced Accelerated Discovery 

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

Accelerated Discovery of Battery Materials

Deep Search

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

AI for Scientific Discovery

ICLR 2025

NAACL 2025

Canadian Separations Symposium 2025

Hybrid Cloud

Foundation Models

Natural Language Processing

Generative AI

Security

Exploratory Science

Granite

Semiconductors

Science

Quantum

Materials Discovery

Accelerated Discovery

Few fields will get value from quantum computing as quickly as chemistry. Even today’s supercomputers struggle to model a single molecule in its full complexity. We study algorithms designed to do what those machines can’t, and power a new era of discovery in chemistry, materials, and medicine.

Quantum Chemistry

Causal modeling is crucial to the effectiveness and trust of AI by ensuring that actions lead to intended outcomes. We study the inference of causal effects and relationships, as well as the application of causal thinking to out-of-distribution generalization, fairness, robustness, and explainability.

Causality

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.

The combination of increasingly powerful computers and AI offers the possibility to be able to detect, diagnose, and cure diseases like never before. At IBM Research, we’re working on creating software and AI systems that can convert reams of health data into useable information for clinicians the world over.

Healthcare and Life Sciences

We're building systems that can convert reams of health data into useable information for clinicians the world over.

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.

On the left is an unlabeled molecular structure diagram. On the right is a stack of three patent documents, and the top one has several molecular structures, including the one shown on the left. That one is highlighted in green.

Accelerated Discovery

Our work

The 2024 IBM Research annual letter

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

A new tool for accelerating the discovery of new materials

How IBM Research built a lab for the future of computing

Mitigating the environmental harm of PFAS ‘forever chemicals’

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

Projects

AI for Scientific Discovery

Deep Search

Knowledge Enhanced Accelerated Discovery

Accelerated Discovery of Battery Materials

Publications

MDLab: AI frameworks for Carbon Capture and Battery Materials

How AI can accelerate Materials Discovery for CO2 Capture

Opportunities of Scalable and Electrostatically Optimized Electrodes for Electric Field- and Current-Driven Microfluidic Applications

Are Large Language Models Effective in Clinical Trial Design? A Study on Baseline Feature Generation

A new framework for evaluating model out-of-distribution generalisation for the biochemical domain

SMI-TED: A large-scale foundation model for materials and chemistry

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