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Quick Start by Role

PhD students & Postdocs — Start with Data Types and Use Cases. For HPC simulations, see Infrastructure and Containers.

PIs & Group Leaders — Review Metadata Standards (CIF, NeXus) and Repositories. See also Data Management Plans.

Data Stewards — Key cross-references: File Formats, Repositories, Workflow Managers.

What is Chemistry & Materials Data Science?

Chemistry and materials science generate highly diverse data — from molecular structures and spectroscopic measurements to materials properties and simulation trajectories. Data science in these fields focuses on organizing, sharing, and computationally analyzing this information to accelerate discovery of new molecules, materials, and processes.

The Need for Data Management in Chemistry & Materials Science​

These fields face specific data management challenges:

  • Diverse data types — Spectroscopic data, crystal structures, reaction parameters, thermodynamic properties, and simulation outputs all require different handling
  • Provenance and reproducibility — Experimental conditions, instrument calibrations, and synthesis protocols must be captured alongside raw data
  • Chemical identifiers — Molecules need standardized representations (SMILES, InChI, CAS numbers) to enable cross-referencing
  • Large simulation data — Molecular dynamics trajectories and DFT calculations produce terabytes of data per study
  • Lack of universal standards — Unlike bioinformatics, many subfields still lack widely adopted metadata schemas

Key Application Areas​

  • Computational chemistry — Quantum mechanical calculations, molecular dynamics, reaction pathway prediction
  • Materials discovery — High-throughput screening, machine learning for materials properties
  • Spectroscopy and analytical chemistry — NMR, mass spectrometry, IR/Raman, X-ray diffraction
  • Catalysis — Catalyst design, reaction optimization, kinetic modeling
  • Polymer science — Structure-property relationships, processing parameters
  • Electrochemistry — Battery research, fuel cells, corrosion studies

Common Tools and Software​

Molecular Modeling and Simulation​

  • Gaussian / ORCA — Quantum chemistry calculations (DFT, ab initio)
  • VASP — Plane-wave DFT for solid-state calculations
  • GROMACS / LAMMPS — Molecular dynamics simulations
  • RDKit (Python) — Cheminformatics toolkit for molecular analysis
  • ASE (Python) — Atomic Simulation Environment

Data Analysis and Visualization​

  • Python (NumPy, SciPy, pandas) — General data analysis
  • Matplotlib / Plotly — Visualization
  • Mercury / VESTA — Crystal structure visualization
  • PyMOL / VMD — Molecular visualization

Electronic Lab Notebooks​

  • eLabFTW — Open-source electronic lab notebook, available @TUM
  • Chemotion — ELN specifically designed for chemistry with integrated repository

Infrastructure and Support​

Researchers working with chemistry and materials data at TUM can access:

  • LRZ Linux Cluster — For DFT calculations and molecular dynamics
  • LRZ AI Systems — GPU clusters for machine learning on molecular data
  • Chemotion Repository — Domain-specific repository for chemical research data

Getting Started​

If you're new to chemistry & materials data science:

  1. Explore our resources — Check out the Data Types and Use Cases
  2. Access infrastructure — Apply for computing resources at LRZ
  3. Get support — Contact rdhub@mdsi.tum.de for data management guidance