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  • amdlab. nl - Autonomous Energy Materials Discovery [AMD]
    [AMD] is a computational research group led by Dr Süleyman Er of DIFFER At [AMD], we develop and use automated virtual materials discovery frameworks – powered by high-throughput physics-based classical and quantum calculations, artificial intelligence methods, and advanced data-infrastructures – to accelerate the discovery of molecules materials for energy applications
  • Süleyman Er – [AMD] - amdlab. nl
    Email: S Er@differ nl Tel: +31 40 3334936 Address: De Zaale 20, 5612 AJ Eindhoven, The Netherlands
  • News – [AMD] - amdlab. nl
    Presentation at the Forum on Artificial Intelligence in Research Süleyman Er will present a talk titled The Use of AI for Autonomous Materials Discovery at the FAIR – Forum on Artificial Intelligence in Research on Wednesday, April 30, 2025 The Department of Materials Physics, Faculty of Sciences, at the Autonomous University of Madrid, Spain, is hosting the event, which will be held in
  • Publications – [AMD] - amdlab. nl
    Advanced Energy Materials, 2401658 (2024) Computational and machine learning-assisted discovery and experimental validation of conjugated sulfonamide cathodes for lithium-ion batteries
  • Team – [AMD] - amdlab. nl
    Alumni Our past members are currently working as (chemistry physics) researchers and (data software) engineers in research (e g Chinese Academy of Sciences, French Aerospace Lab, TNO), academic (e g Delft University of Technology, Eindhoven University of Technology, RWTH Aachen, University of Strasbourg, University of Twente, Utrecht University), and industrial (e g ASML, DAF, Nielsen
  • RedDB – [AMD] - amdlab. nl
    How to cite? If you use RedDB in your study, please cite the below publication as well as the data and code repositories Publication: Scientific Data 9, 718 (2022) Data: Harvard Dataverse Code: GitHub
  • Research – [AMD] - amdlab. nl
    At the Autonomous Energy Materials Discovery [AMD] research group our focus is on developing and using predictive methods for the discovery of molecules and materials for energy applications, which is fully in line with DIFFER’s mission Our research endeavors can be categorized into the following five groups:
  • Software-Data – [AMD] - amdlab. nl
    AqSolPred: aqueous solubility prediction tool ChemPlot: python library for chemical space visualization ChemPrice, a Python package for automated chemical price search RedCat, an Automated Discovery Workflow for Aqueous Organic Electrolytes RedPred: redox reaction energy prediction tool
  • Open Positions – [AMD] - amdlab. nl
    We occasionally offer internships and junior senior researcher roles in digital (AI4Materials, software, data) and experimental (lab automation) aspects of Self-Driving Labs for Chemical Energy ; Postdoc, PhD, and engineering positions are listed on the DIFFER vacancies page Please apply through the DIFFER portal ; For Bachelor Master internships, thesis projects, or research fellowship
  • AqSolDB: A curated reference set of aqueous solubility - amdlab. nl
    AqSolDB: A curated reference set of aqueous solubility (version 1 0) Search from the right panel





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