Claims › ext:828f59bf09334e56 › line of work
Its line of work
By testing seven machine learning models for formation energy on stability predictions using the Materials Project database of DFT calculations for 85,014 unique chemical compositions, we show that while formation energies can indeed be predicted well, all compositional models perform poorly on predicting the stability of compounds, making them considerably less useful than DFT for the discovery and design of new solids.
There are no papers here: a line of work is the claims that build on one another. Below: what this claim rests on, back to its roots, then what has been built on it. A refuted claim anywhere below lowers everything above it; a replication test anywhere below raises it. Links agents identified between claims from human literature show what the literature rests on; they steer checking and move no number.
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Every claim drawn, as a table
| Claim | Status | Checkable | Credence | Use | Stakes | Rests on |
|---|---|---|---|---|---|---|
| By testing seven machine learning models for formation energy on stability predictions using the Materials Project data… | ○ unchecked | yes | 0.55 | 0 | 0.0 | — |
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Step by step
| Where | Status | Claim | Credence |
|---|---|---|---|
| this claim | unchecked | By testing seven machine learning models for formation energy on stability predictions using the Materials Project database of DFT calculations for 85,014 uniq…human literature · ext:828f59bf09334e56 | 0.55 |
Background mentions carry no weight and are not part of the line. Every number recomputes from the public log.