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.
Our work
Resolving the first anti-aromatic carbon allotrope
Technical noteLeo Gross- Materials Discovery
- Physical Sciences
An AI foundation model that learns the grammar of molecules
NewsPayel Das, Youssef Mroueh, Inkit Padhi, Vijil Chenthamarakshan, Jerret Ross, and Brian Belgodere- Accelerated Discovery
- AI
- Foundation Models
- Life Sciences
- Materials Discovery
Accelerating discovery for societal and economic impact
ExplainerJed Pitera, Mathias Steiner, Daniel P. Sanders, Young-Hye Na, Maxwell Giammona, Kristin Schmidt, and Tim Erdmann- Accelerated Discovery
- Climate
- Generative AI
- Materials Discovery
- Responsible Technology
Computer simulations identify new ways to boost the skin’s natural protectors
ResearchJason Crain5 minute read- Accelerated Discovery
- Healthcare
- Materials Discovery
- Physical Sciences
How to use AI to discover new drugs and materials with limited data
News2 minute read- AI
- Generative AI
- Materials Discovery
How generative AI models can fuel scientific discovery
ExplainerJohn R Smith and Matteo Manica6 minute read- Accelerated Discovery
- AI
- Generative AI
- Materials Discovery
- See more of our work on Materials Discovery
Projects
Accelerator Technologies
Publications
- 2023
- NeurIPS 2023
- 2023
- NeurIPS 2023
- Yuankai Luo
- Lei Shi
- et al.
- 2023
- NeurIPS 2023
- 2023
- MRS Fall Meeting 2023
- Yves Gaetan Nana Teukam
- Federico Zipoli
- et al.
- 2023
- MRS Fall Meeting 2023
- Seiji Takeda
- Akihiro Kishimoto
- et al.
- 2023
- MRS Fall Meeting 2023
Project Photoresist
We used our accelerated discovery process to identify and synthesize a novel photoacid generator in less than a year — far quicker than it usually takes.
Tools + code
RoboRXN for Chemistry
A unique tool for digital chemistry. Language models based on transformers can predict the most likely outcome of a chemical reaction and perform retrosynthetic analysis. RoboRXN can also program hardware to produce a molecule in a remotely accessible, autonomous chemical laboratory.
View project →IBM Molecule Generation Experience
An AI-driven molecular inverse-design platform, which automatically designs brand new molecular structures rapidly and diversely.
View project →RXNmapper
A chemically agnostic attention-guided reaction mapper.
View project →Deep Search Knowledge Graph of COVID-19 Literature
IBM is providing free access to its COVID-19 Knowledge Graph, which is part of its Corpus Processing Service. This knowledge graph integrates COVID-19 data from various sources.
View project →CIRCA
CIRCA enables easy search, visualization, and flexible export options of chemically-annotated data from publicly available patents and other data sources.
View project →
