Computational-chemistry projects still rely on raw log files that are huge, messy, and hard to reuse. Researchers often copy-paste energies into spreadsheets, pass logs around by e-mail, and rebuild fragile parsing scripts for each new study. One typo or missing line can derail days of work, and searching for “all jobs that failed to converge last month” is practically impossible.

QuantumParse, running inside ElizaOS, automates the dull parts and removes the risky parts.

• It reads Gaussian 16 log files automatically, extracts key data (energies, basis sets, warnings, convergence steps), and converts everything into a clean knowledge graph.

• It drops a readable summary straight into the ElizaOS chat window, so you can see the result seconds after a job finishes—no more hand-transcribing numbers.

• It lets you ask follow-up questions in plain English: “Show molecules with imaginary frequencies” or “List number of molecules processed”. The answers come back instantly from the knowledge graph.

• Graph and plot the most useful molecular properties, and analyse the iterations of each gaussian job within elizaos

Who benefits

• Academic and industrial chemists who want to mine years of legacy logs without reinventing parsers.
• HPC admins who need early warnings when jobs keep crashing.
• Data-science teams building machine-learning models on quantum-chemistry data.
• Instructors who want students to interact with real QC calculations instead of static screenshots.

In short, QuantumParse turns raw quantum-chemistry calculations into clear, searchable knowledge—quicker, safer, and with zero manual copy-paste.