MolSimplify: A toolkit for automating discovery in inorganic chemistry. Issue 22 (1st July 2016)

• Record Type: Journal Article
• Authors: Ioannidis, Efthymios I.; Gani, Terry Z. H.; Kulik, Heather J.
• Is Part Of: Journal of computational chemistry. Volume 37:Issue 22(2016)
• Page Start: 2106
• Page End: 2117
• Publication Date: 2016-07-01
• Subjects: chemical discovery -- structure generation -- first‐principles simulation -- high‐throughput screening -- python; Chemistry -- Data processing -- Periodicals; 542.85
• DOI: 10.1002/jcc.24437
• Languages: English
• ISSNs: 0192-8651

Abstract

Abstract : We present an automated, open source toolkit for the first‐principles screening and discovery of new inorganic molecules and intermolecular complexes. Challenges remain in the automatic generation of candidate inorganic molecule structures due to the high variability in coordination and bonding, which we overcome through a divide‐and‐conquer tactic that flexibly combines force‐field preoptimization of organic fragments with alignment to first‐principles‐trained metal‐ligand distances. Exploration of chemical space is enabled through random generation of ligands and intermolecular complexes from large chemical databases. We validate the generated structures with the root mean squared (RMS) gradients evaluated from density functional theory (DFT), which are around 0.02 Ha/au across a large 150 molecule test set. Comparison of molSimplify results to full optimization with the universal force field reveals that RMS DFT gradients are improved by 40%. Seamless generation of input files, preparation and execution of electronic structure calculations, and post‐processing for each generated structure aids interpretation of underlying chemical and energetic trends. © 2016 Wiley Periodicals, Inc. Abstract : An automated approach for the computational generation, characterization, and discovery of inorganic complexes is introduced. The molSimplify software code provides an integrated approach to structure generation, simulation automation, and data analysis. This toolkit speeds up chemical discovery with first‐principles simulations both by reducing simulation overhead and maximizing exploration of chemical space.