As part of the publication 'Exploring Structure-Property Relationships in Magnesium Dissolution Modulators'1, this repository includes the code of the developed webapp ExChem. A running version can be found at www.exchem.de.
What is ExChem?
Searching for structure-property relationships is an effective approach to predict yet unknown material properties. ExChem allows the exploration of vast areas of chemical space by combining machine learning methods with comprehensive molecular databases.
Small organic molecules that form complexes with corrosive species accelerating the degradation process have shown great potential to control the dissolution properties of pure magnesium (Mg) materials and its alloys.2 However, as the chemical space of small organic molecules is effectively infinite, the most challenging task is to find molecules with beneficial properties for specific applications. Fortunately, recent studies based on a comprehensive database of magnesium dissolution modulators1 revealed that for CPMg220 (commercial purity Mg, containing 220 ppm iron impurities) the molecular structure correlates well with the corrosion inhibition efficiency (IE).3,4
To further explore the structure-property relationships in magnesium dissolution modulators, a structure-property landscape was generated using a SOAP kernel and sketch-map for 152 compounds, of which 78 have already been experimentally tested2. On this basis, a kernel ridge regression (KRR) model was trained and used to predict the IEs for a second database of over 7000 commercially available chemicals. Computation of a second SOAP kernel combining both databases allows to screen large areas of chemical space for similar structures based on a selected compound of interest, thus facilitating the search for new corrosion inhibitors.
Note: For some compounds duplicates exist in the reference database. However, due to the training error of the KRR model it may happen that experimental and predicted IEs do not match, although the structures are identical.
Selection of a point in the sketch-map leads to visualization of the corresponding molecular structure in the dataset.
Additionally, structures similar to the selected are presented in a table
along with their CAS number, similarity value and KRR-predicted IE.
Selecting a table row of interest leads to visualization
of the according molecular structure and its SMILES string.
Atoms are colored according to the CPK coloring scheme.
[1] Exploring Structure-Property Relationships in Magnesium Dissolution Modulators, npj Materials Degradation 5 2 (2020)
[2] Comprehensive screening of Mg corrosion inhibitors, Corrosion Science 128 224–240 (2017)
[3] Data Science Based Mg Corrosion Engineering, Frontiers in Materials 6 53 (2019)
[4] In silico Screening of Modulators of Magnesium Dissolution, Corrosion Science 108245 (2020)