---
title: Aixelo | AI Enterprise Software, Strategy Consulting, and Professional Services
description: Aixelo helps chemical and materials companies transition to a better R&D process through the adoption of next-generation AI.
---
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# Real projects. Real results.
These are not sandboxes or proof-of-concept demos. These are real R&D projects where structured, AI-driven experimentation made a measurable difference.
## Case Study 1: Optimizing SURMOF Coatings
**THE CHALLENGE**
Creating highly crystalline, oriented metal-organic framework (MOF) coatings requires optimizing multiple interacting synthesis parameters — metal and linker concentrations, modulator amounts, cleaning times. Without systematic guidance, scientists face a combinatorial explosion of conditions to test, with no clear path to optimal SURMOF growth.
**THE APPROACH**
Using Edison 4.0’s adaptive optimization, the team defined multiple synthesis parameters and ran autonomous, multi-objective optimization via genetic algorithms. The system identified the best synthesis conditions while simultaneously analyzing which parameters actually mattered most.
**THE RESULT**
### 95%
crystallinity achieved
### Uniform
orientation across all samples
### < few nm
surface roughness
Beyond the immediate results, the team developed a general methodology applicable to various material synthesis processes — demonstrating that the approach works across different MOF systems and substrates.
**KEY INSIGHT**
The biggest value wasn’t just the optimized coating — it was understanding which parameters actually drive performance. That knowledge transfers to every future project.
**PUBLICATIONS**
Fully Automated Optimization of Robot-Based MOF Thin Film Growth via Machine Learning Approaches. doi.org/10.1002/admi.202201771
Enhancing the Quality of MOF Thin Films for Device Integration Through Machine Learning: A Case Study on HKUST-1 SURMOF Optimization. doi.org/10.1002/adfm.202404631
## Case Study 2: Predicting Synthesis Conditions for Novel MOFs
**THE CHALLENGE**
Before synthesizing a new metal-organic framework, researchers typically spend weeks searching literature for relevant synthesis conditions. This process is tedious, inconsistent, and often overlooks key insights buried across thousands of papers — resulting in redundant lab work and missed opportunities.
**THE APPROACH**
Aixelo used natural language processing (NLP) and machine learning to automatically extract synthesis data from over 10,000 research papers, creating the SynMOF database — a structured knowledge base containing synthesis parameters and structural information for over 900 MOFs. Machine learning models then predicted optimal synthesis conditions for new MOFs based on this data.
**THE RESULT**
### 10,000+
research papers analyzed automatically
### 900+
MOF synthesis records extracted
### Higher
prediction accuracy than human experts
The system accelerated the identification of promising MOF candidates, significantly reduced trial-and-error, and enabled the discovery of MOFs with unique properties — opening doors to breakthroughs in material science that would have taken years of manual literature review.
**KEY INSIGHT**
The bottleneck in materials R&D isn’t always the experiments — sometimes it’s the knowledge that’s already out there but trapped in thousands of unstructured papers. AI can unlock it.
**PUBLICATIONS**
MOF Synthesis Prediction Enabled by Automatic Data Mining and Machine Learning. doi.org/10.1002/anie.202200242
Functional Material Systems Enabled by Automated Data Extraction and Machine Learning. doi.org/10.1002/adfm.202302630
## Research Highlight: AI-Driven Perovskite Solar Cell Discovery
In a study published in Science, Aixelo co-founder Prof. Pascal Friederich and advisory board member Prof. Christoph Brabec demonstrated how advanced machine learning can transform materials discovery.
The research explored a virtual library of one million candidate molecules for perovskite solar cells. Using AI-driven, closed-loop design strategies, the team identified new materials capable of boosting solar cell efficiencies to over 26% — with only around 150 experiments, rather than thousands.
#### 1,000,000
candidate molecules screened
#### ~150
experiments to reach target
#### >26%
solar cell efficiency achieved
**Why this matters for your R&D**
The methodologies behind this breakthrough — adaptive optimization, closed-loop experimentation, virtual screening — are the same principles built into Edison 4.0. Whether you work with solar cells, formulations, polymers, or catalysts, the approach is transferable: let AI navigate the search space so your team focuses on what matters.
Published in Science: DOI 10.1126/science.ads0901
## What These Projects Have in Common
#### Real projects, not demos
Every case started with a real R&D challenge — not a sandbox or proof of concept. The outcome mattered.
#### Fewer experiments, deeper insights
AI-guided experimentation consistently reduced the number of experiments needed while providing understanding of why results happened — not just what happened.
#### Knowledge that transfers
The methodologies developed in each project apply beyond the original problem — to new materials, new systems, new teams.
## Bring your own project.
Tell us about your R&D challenge. We’ll walk you through Edison 4.0 with your use case in mind.
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## Our Team
We are a Karlsruhe Institute of Technology (KIT) spin-out and pioneer closed-loop AI for accelerated innovation.
#### Dr. Christoph Kreisbeck
##### Co-Founder
Chief Executive Officer
#### PD Dr. Manuel Tsotsalas
##### Co-Founder
Chief Technology Officer
#### Prof. Dr. Pascal Friederich
##### Co-Founder
Chief Scientific Officer
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