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Spectral fingerprints can differentiate between unique molecular structures with identical 2D connectivity, revolutionizing how we assess chemical similarity.
NNPs can achieve near-chemical accuracy in enzyme catalysis predictions with less than 1,000 system-specific data points, revolutionizing the efficiency of mechanistic studies.
A unified platform for molecular machine learning that supports 100 elements and incorporates uncertainty quantification could democratize access to advanced chemical property predictions.
Finally, a large, diverse, and experimentally-anchored dataset of transition metal complex DFT properties is available to fuel ML model development and DFT benchmark studies.
