About this Abstract |
Meeting |
1st World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
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Symposium
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First World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
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Presentation Title |
Combining Organic and Inorganic Descriptors for Predictions of Solubility and Volatility Across Vast Chemical Space |
Author(s) |
Anand Chandrasekaran, Simon D. Elliot, Asela Chandrasinghe, Yuling An, H. Shaun Kwak, Mathew D. Halls |
On-Site Speaker (Planned) |
Anand Chandrasekaran |
Abstract Scope |
Traditional QSPR approaches use machine learning to map the 2D structures of molecules to a particular label/property. These 2D structures are usually numerically encoded as bit-based fingerprints that capture the substructures around atoms in a molecule. However, such approaches do not perform well for inorganic or metal-containing compounds. More recently, materials informatics descriptors have been developed that span the entire periodic table, capturing both chemical and stoichiometric information. In this work we utilize a combination of traditional QSPR fingerprints and inorganic descriptors to train ML models for solubility and volatility across a huge chemical space of organic, organometallic, and inorganic materials. For solubility, we use the AqSolDB dataset and for volatility we have carefully curated an organometallic dataset with more than 3000 pressure vs temperature measurements. We benchmark a number of different machine learning approaches and show that stacking estimators perform significantly better in comparison to using a single algorithm/model. |
Proceedings Inclusion? |
Undecided |