Tailwater Informatics
Tailwater Informatics provides an AI-driven platform that predicts critical electronic material properties directly from atomic structures, reducing evaluation time and cost by orders of magnitude. The system combines physics-informed machine learning with proprietary, domain-specific data pipelines and cloud-scale compute infrastructure for high-throughput screening of semiconductor, energy, and advanced manufacturing materials.
- New Materials
- Artificial Intelligence
- Semiconductor
- Software Only
Funding
Founders
Founder details are not available yet.
Product
Problem
Modern materials innovation is constrained by legacy computational tools that are slow, expensive, and difficult to integrate into today's AI-driven workflows. Evaluating electronic material properties traditionally requires costly, time-prohibitive calculations that limit how many candidates can be screened and slow down decision-making in enterprise discovery processes.
Solution
Tailwater Informatics provides a platform that uses physics-informed machine learning to predict critical electronic material properties from atomic configurations, helping teams move faster from candidate screening to decision-making. The system combines a proprietary dataset and neural network to perform calculations that historically were cost and time prohibitive. A high-throughput screening engine rapidly evaluates large volumes of material candidates, while proprietary data pipelines supply structured, domain-specific datasets designed for advanced materials workflows. The platform runs on cloud-based infrastructure built to support modern discovery at enterprise scale, enabling faster innovation for semiconductor, energy, and advanced manufacturing businesses.
Target Audience
Primary customers are research and development teams in semiconductor, energy, and advanced manufacturing businesses that need faster evaluation of electronic material properties for discovery workflows.
Features
- High-throughput screening engine that rapidly evaluates large volumes of material candidates with greater efficiency
- Proprietary data pipelines built on structured, domain-specific datasets designed for advanced materials workflows
- Physics-informed neural network training infrastructure that predicts critical material properties at speed
- Scalable cloud-based compute framework designed to support enterprise-scale discovery
- Public documentation and GitHub tutorials demonstrating platform usage and integration