Timeline for Molecular orbital energies prediction with ML algorithms
Current License: CC BY-SA 4.0
9 events
| when toggle format | what | by | license | comment | |
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| Sep 23, 2019 at 16:06 | vote | accept | Blade | ||
| Sep 21, 2019 at 20:14 | history | edited | Geoff Hutchison |
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| Sep 21, 2019 at 19:53 | answer | added | Geoff Hutchison | timeline score: 3 | |
| Sep 19, 2019 at 17:58 | comment | added | Blade | Thanks @Feodoran. By nature, I agree that the question is a broad one, but, I hope that I made it clear that I'm looking for one liner answers such as yours. (Not one-line, but one paragraph! :D) | |
| Sep 19, 2019 at 17:43 | comment | added | Feodoran | One a side note: orbital energies are not solutions to the electronic Schrödinger equation! They are more like intermediate results. | |
| Sep 19, 2019 at 17:42 | comment | added | Feodoran | Fundamentally, molecular properties are determined by the atoms, their type, their positions (which includes the chemical environment of each atom) and the number of electrons (total charge). Most of your mentioned features actually try to describe the chemical environment by classifying different structural patterns (e.g. closest neighbors, bond type). Change any of that and you will get different orbital energies. Of course some changes have a larger influence than others. Answering this question is essentially what chemistry is all about. Which makes your question a very broad one ... | |
| Sep 18, 2019 at 16:30 | comment | added | Blade | Thanks for the comment, @Tyberius. Well said. The "hand-wavy arguments about why certain features might influence orbital energies" is what I'm looking for. | |
| Sep 18, 2019 at 16:20 | comment | added | Tyberius♦ | The interpretation of the how the features lead to a given output is a problem with any machine learning prediction. We can make some sort of hand-wavy arguments about why certain features might influence orbital energies, how much a given feature influences an output is entirely dependent on the structure of the model. Particularly for neural networks, where there are a number of layers between features and output, it is difficult to tease out what features are most important. | |
| Sep 18, 2019 at 15:05 | history | asked | Blade | CC BY-SA 4.0 |