⚡ Bolt: [performance improvement] Optimize Pandas DataFrame Iteration

[alinelena]

💡 What: Replaced df.iterrows() with df.to_dict("records") when creating lookup dictionaries (parquet_by_sha and parquet_by_argone_rel) in verify_processed_omol25.py. Removed the redundant .to_dict() call on pq_row later in the script.

🎯 Why: Iterating over a DataFrame using df.iterrows() is notoriously slow in Pandas because it wraps every row into a Pandas Series object. This causes significant overhead, particularly on large DataFrames. Converting the DataFrame to a list of native Python dictionaries first allows us to bypass this abstraction overhead, drastically speeding up the dictionary comprehensions.

📊 Impact: Expected to reduce execution time of the file loading and mapping generation step significantly (often an order of magnitude faster for larger datasets).

🔬 Measurement: Execute python src/lavello_mlips/verify_processed_omol25.py against a large output Parquet file and measure the time it takes to output Loaded {len(df)} records from Parquet. vs the subsequent Loading ExtXYZ file... log.

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PR created automatically by Jules for task 6083572916232079911 started by @alinelena

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