Dr. Mohammad Soleymanibrojeni
AI Research Engineer
Intelligent solutions for digital transformation
About Me
Multidisciplinary materials engineer with 8+ years of research and industry experience, specializing in AI/ML solutions. An innovative thinker who integrates emerging technologies to drive continuous improvement and accelerate process timelines. Results-driven professional with expertise in materials properties, big data analytics, forecasting, visualization, technical reporting, agentic workflows, and collaboration. Exceptionally dedicated, with strong interpersonal, communication, and organizational skills.
Areas of Expertise
AI/ML Research
- •Active Learning Models
- •Battery Interface Prediction
- •Generative AI Pipelines
- •Semantic Interfacing
- •Knowledge Graphs
- •Adaptive Data Structures
Materials Engineering
- •Battery Materials
- •Materials Selection
- •Failure Analysis
- •Technical Standards
- •Quality Assurance
- •Materials Characterization
Technical Development
- •Research Toolbox Creation
- •Automated Debugging Systems
- •Novel Retrieval Systems
- •Publication Assistance Tools
- •Agentic Workflows
- •Data Modeling
Tech Stack
- •Python
- •PyTorch
- •FastAPI
- •Docker/Containerization
- •CI/CD
- •React/Next.js
Data & Analytics
- •Big Data Analytics
- •Forecasting
- •Visualization
- •Technical Reporting
- •Systemic Analysis
- •Cloud Deployment
Collaboration
- •International Partnerships
- •Multi-million Euro Initiatives
- •Technical Advisory
- •Documentation
- •Workflow Coordination
- •Cross-functional Leadership
Featured Projects
Active Learning for Battery Interface Prediction
Developed an innovative active learning approach to model solid-electrolyte interphase formation in Li-ion batteries, achieving 90%+ prediction accuracy while minimizing computational costs.
Technologies Used
📄 Related Publication
An active learning approach to model solid-electrolyte interphase formation in Li-ion batteries (2024) - 5 citations
Impact
- Reduced experimental iterations by 70%
- Achieved 90%+ prediction accuracy
- Published in Journal of Materials Chemistry A
ML-Powered Research Toolbox
Collaborated on the creation of a comprehensive machine learning toolbox for materials research that dramatically accelerated analytical processes from hours to minutes.
Technologies Used
Impact
- Analysis time reduced by 85%
- Adopted by multiple research groups
- Integrated into BIG-MAP project workflow
Generative AI Pipeline for Materials Discovery
An Agentic AI approach that synergizes large language models (LLMs) with a domain-specific knowledge graph to enhance reasoning and autonomy in generating, submitting, and monitoring simulation protocols for materials discovery.
Technologies Used
Impact
- Contributed to BIG-MAP €20M research initiative
- Automated DFT protocol generation and validation
- Reduced discovery timeline by 60%
tex-Rex: AI-Powered Publication Assistant
Tex-Rex is an AI-powered academic paper analysis tool that helps researchers improve their scientific manuscripts through automated feedback and suggestions.
Technologies Used
Impact
- Reduced publication preparation time by 40%
- Supported multiple languages
- Improved consistency across research publications
Elastic Dictionary for Materials Data
An adaptive, hierarchical data structure that dynamically organizes string data and text into a semantic tree structure.
Technologies Used
Impact
- Improved data retrieval efficiency by 65%
- Enhanced cross-referencing of research data
- Integrated with multiple research databases
Atomistic Simulation of Material Interfaces
Conducted comprehensive atomistic simulations of epoxy/water/aluminum systems using the ReaxFF method to understand interface behavior at the molecular level.
Technologies Used
📄 Related Publication
Atomistic simulations of Epoxy/Water/Aluminum systems using the ReaxFF method (2020) - 14 citations
Impact
- Published in Computational Materials Science
- Cited by 14+ research papers
- Provided insights for corrosion prevention
Publications
Timeline
An active learning approach to model solid-electrolyte interphase formation in Li-ion batteries
M Soleymanibrojeni, CRC Rego, M Esmaeilpour, W Wenzel
Journal of Materials Chemistry A 12 (4), 2249-2266
This paper presents an innovative active learning approach for modeling the formation of solid-electrolyte interfaces in lithium-ion batteries, significantly improving prediction accuracy and computational efficiency.
Surface energies control the anisotropic growth of β-Ni (OH) 2 nanoparticles in stirred reactors
N Streichhan, D Goonetilleke, H Li, M Soleymanibrojeni, PW Hoffrogge, et al.
Surfaces and Interfaces 51, 104736
This study investigates how surface energies influence the anisotropic growth of β-Ni(OH)2 nanoparticles in stirred reactor environments, providing insights for controlled nanoparticle synthesis.
Professional Experience
AI Research Engineer/Post-Doc
Karlsruher Institut für Technologie
- •Developed active learning models for battery interface prediction with 90%+ accuracy
- •Created ML-powered research toolbox that accelerated analytical processes
- •Implemented semantic interfacing and knowledge graphs for materials discovery
- •Collaborated with 30+ international partners on BIG-MAP (€20M research initiative)
- •Contributed to Plattform MaterialDigital (PMD) project development
Key Projects
- →Generative AI Pipelines for autonomous battery material discovery
- →Battery Interface Prediction with 90% accuracy
- →tex-Rex: AI-powered publication assistant
- →Elastic Dictionary: Adaptive data structure
Materials Engineer
National South Fields Oil Company
- •Collaborated on materials selection for critical oil production equipment worth €10B+
- •Provided technical advisory across five departments to enhance operational efficiency
- •Partnered with teams to implement environmental/technical standards and compliance protocols
- •Worked cross-functionally to reduce material failure rates through systematic analyses
Education
Ph.D. in Materials Science and Engineering
Chinese Academy of Sciences
Dissertation: Developed a novel method for characterizing aerospace materials
M.Sc. in Materials Science and Engineering
University of Tehran
Thesis: Studied multi-scale finite element methods and atomistic models
B.Sc. in Materials Science and Engineering
Isfahan University of Technology
Project: Investigated thermodynamic properties of materials
Get in Touch
Contact Information
hi@moslmn.link
Location
Karlsruhe, Germany