Entalpic
Entalpic provides an AI-driven materials engineering platform that designs molecules and materials for surface-driven industrial processes at the atomic scale. The company combines AI, atomistic simulations, and experimental feedback loops to screen large chemical spaces under real industrial constraints. Its high-throughput discovery engine integrates multimodal datasets from patents, simulations, and partner laboratories.
- New Materials
- Chemical Technology
- Artificial Intelligence
- Software Only
- Clean Technology
Funding
Raised to date
$9.4MRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
across 1 round
€8.5M · Seed
Announced Sep 2024 · from article date
Disclosed cumulative: $9.3M
Founders
Product
Problem
The chemical and materials industry faces significant pressure to develop more sustainable materials and efficient chemical processes. Traditional catalyst and material discovery methods are slow, expensive, and frequently fail to account for real-world manufacturing constraints, hindering the transition to greener chemistry.
Solution
Entalpic offers an AI-driven platform for engineering materials at the atomic scale, specifically designed for surface-driven industrial processes such as catalysis and atomic layer deposition. The platform combines AI and quantum modeling with high-throughput screening to rapidly evaluate large chemical spaces and identify viable material candidates. Entalpic integrates experimental and characterization data, as well as computational fluid dynamics-based process modeling, into its discovery engine to ensure AI-designed materials can be reliably manufactured under real conditions. This feedback loop between simulation and experiment accelerates the development of novel catalysts and materials while reducing the time and cost associated with traditional discovery methods.
Target Audience
Primary customers are chemical companies, materials science researchers, and industrial manufacturers seeking AI-accelerated discovery of catalysts and materials for surface-driven processes, including partners in the semiconductor and sustainable chemistry sectors.
Features
- Generative AI and quantum modeling for de novo material and catalyst design
- High-throughput screening engine that evaluates large chemical spaces under real industrial constraints
- Multimodal datasets combining structured patent analysis, academic literature, atomistic simulation data, and experimental characterization data
- Digital twin process modeling using machine learning and computational fluid dynamics to simulate material behavior in manufacturing conditions
- Experimental integration loop with partner laboratories to validate and refine AI-predicted materials
- Proprietary tools and published frameworks including LeMat-Bulk (cleaned quantum chemistry database), LeMat-Traj (atomic trajectories dataset), and Catalyst GFlowNet for autonomous catalyst discovery