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Data & Analytics · By learningmatter-mit
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| # | Skill | Repository | Stars | Used in | Tokens | Auto-check | Licence | Updated |
|---|---|---|---|---|---|---|---|---|
| 1 | Scripts for Wasserstein deconvolution of 1H NMR mixture spectra against reference spectra, reaction product prediction, time-series kinetics, and spectral plotting. | learningmatter-mit/ | 176 | — | ~2.3k | Automated safety check: Pass | MIT | today |
| 2 | Calculate the spontaneous ferroelectric polarization across a non-polar to polar structure transition using the Berry Phase method. | learningmatter-mit/ | 176 | — | ~738 | Automated safety check: Pass | MIT | today |
| 3 | Fine-tune Fairchem machine learning interatomic potentials (UMA, ESEN) on custom datasets. | learningmatter-mit/ | 176 | — | ~1.7k | Automated safety check: Pass | MIT | today |
| 4 | Fine-tune MACE machine learning interatomic potentials on custom datasets. | learningmatter-mit/ | 176 | — | ~2.6k | Automated safety check: Pass | MIT | today |
| 5 | Fine-tune MatGL machine learning interatomic potentials on custom datasets. | learningmatter-mit/ | 176 | — | ~1.4k | Automated safety check: Pass | MIT | today |
| 6 | Benchmark of inference speed of Machine Learning Interatomic Potentials (MLIPs). | learningmatter-mit/ | 176 | — | ~1k | Automated safety check: Pass | MIT | today |
| 7 | Train a property predictor head on top of a Machine Learning Interatomic Potential (MLIP) backbone (MACE or MatGL) to predict custom intensive or extensive properties from crystal or molecular… | learningmatter-mit/ | 176 | — | ~1.3k | Automated safety check: Pass | MIT | today |
