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Technical University of Munich

Accelerated Scientific Discovery

LRZ SuperMUC. Picture: StMWK / Axel König

Exploration, exploitation and understanding of the vast chemical space for functional materials is virtually impossible by conventional manual experimentation and necessitates the integrated deployment of automated experimental and theoretical methods. We therefore employ high-throughput and combinatorial methods in materials design, synthesis, characterization and device building with close integration with corresponding virtual experiments. Recognizing the need to link virtual and physical experiments, we actively develop methods for inter-lab orchestration and ontology-linked data management.

  • Classical & Quantum Computation
  • AI & Machine Learning

  • X-ray Science & Technology
  • In situ Electron Microscopy and Spectroscopy
  • Advanced Chemical Imaging
  • Novel Mechanistic Probes
  • Modulated and defect structure chemistry
  • Non-periodic structures

  • Data-driven drug discovery and material design

  • Crystal Structure Prediction
  • Functional Property Prediction

  • Automation & High-throughput in Catalysis and Battery Research
  • Intention Agnostic Orchestration
  • Lab Automation and Orchestration

News - Accelerated Scientific Discovery

Quantum Science & Technologies, Accelerated Scientific Discovery, Research, Physics |

Quantum computer opens a new path to practical scientific applications

The results mark an important step toward practical applications of quantum computing in physics and materials research.

Fundamental Forces and Cosmic Evolution, Accelerated Scientific Discovery, ORIGINS, Research, Physics |

Bigger machine learning models, better physics

ATLAS demonstrates how scaling up AI unlocks new capabilities in particle identification.

Clean Technology Solutions, Accelerated Scientific Discovery, AMC, e-conversion, Research, Physics |

Teaching a Hamster physics: realistic simulation of next-gen solar materials

By fusing physics with machine learning, researchers model complex energy materials at real-world temperatures and scales.

Clean Technology Solutions, Accelerated Scientific Discovery, e-conversion, Research, Physics |

“With machine learning, we can revolutionize materials research”

What drives our energy pioneers: Prof. Patrick Rinke

Quantum Science & Technologies, Accelerated Scientific Discovery, Research, Physics |

“Invisible” magnets for faster IT

Prof. Johannes Knolle represents the TUM School of Natural Sciences in this Japanese-German consortium

Fundamental Science for Health, AMC, Research, Chemistry |

Scientists explore a smarter drug delivery strategy for cisplatin

A step towards next-generation cancer nanomedicine

FRM II, Accelerated Scientific Discovery, Research, Physics |

Why corals bleach

Neutrons from FRM II help investigate the processes involved in coral death

BioSysteM, Biomolecular Engineering & Design, Accelerated Scientific Discovery, Bioscience |

Designing proteins for targeted cell activation

NewIn: Thomas Schlichthärle

Clean Technology Solutions, Accelerated Scientific Discovery, AMC, e-conversion, Research, Physics |

Machine Learning Sheds New Light on Fast-Moving Ions in Solid Electrolytes

The framework improves accuracy while drastically reducing computational cost.

Fundamental Forces and Cosmic Evolution, Accelerated Scientific Discovery, ORIGINS, Research, Physics |

BMFTR funds “SciFM” to develop foundation models in astro- and particle physics

Profs. Lukas Heinrich (TUM) and Daniel Grün (LMU) lead the effort from Munich Center for Machine Learning

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