Material Discovery
Generative models for discovering novel materials with desired properties
Overview
Our Material Discovery models utilize advanced generative AI techniques to explore vast chemical and structural spaces, identifying promising new materials with desired properties. Unlike traditional discovery methods that rely on time-consuming trial and error, these models can efficiently navigate complex material landscapes to propose candidate materials with high potential.
By combining machine learning, computational chemistry, and material science principles, these models accelerate the discovery process by orders of magnitude, opening new opportunities for innovation in diverse fields such as energy storage, catalysis, electronics, and sustainable materials.
Available Models
How It Works
Our material discovery models employ several AI approaches to generate and identify novel materials:
Generative Design Process
- Property Specification: Users define desired material properties and constraints.
- Latent Space Exploration: AI models explore a compressed representation of the material space.
- Candidate Generation: Novel material candidates are generated that potentially satisfy the specified criteria.
- Property Validation: Generated candidates undergo computational screening to validate their properties.
- Refinement: Promising candidates are refined through iterative feedback loops.
Forward Design
Starting from known materials or chemical building blocks, the model explores incremental modifications to improve specific properties.
Inverse Design
Beginning with target properties, the model works backward to identify material compositions and structures that would exhibit those properties.
Applications
Energy Storage Materials
Our discovery models have successfully identified novel electrode and electrolyte materials with improved energy density, cycling stability, and safety profiles.
Catalyst Development
Generative models have accelerated the discovery of high-performance catalysts for chemical transformations, fuel cells, and CO₂ reduction.
Advanced Functional Materials
Our models have contributed to the development of novel materials with tailored optical, magnetic, thermal, and electronic properties.
Getting Started
Access to our Material Discovery models is available through our cloud API or on-premise deployment. To start discovering novel materials with AI:
- Sign up for a ThinkMaterial account
- Define your material property targets and constraints
- Select the appropriate discovery model for your application
- Generate and evaluate candidate materials through our intuitive interface
- Export promising candidates for experimental validation