Claims › ext:ec85657738f165d2 › line of work
Its line of work
We build a highly accurate model for predicting formation energy of materials from their compositions; using an experimental data set of $$1,643$$ 1 , 643 observations, the proposed approach yields a mean absolute error (MAE) of $$0.07$$ 0.07 eV/atom, which is significantly better than existing machine learning (ML) prediction modeling based on DFT computations and is comparable to the MAE of DFT-computation itself.
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 |
|---|---|---|---|---|---|---|
| We build a highly accurate model for predicting formation energy of materials from their compositions; using an experim… | ○ unchecked | yes | 0.55 | 0 | 8.4 | — |
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Step by step
| Where | Status | Claim | Credence |
|---|---|---|---|
| this claim | unchecked | We build a highly accurate model for predicting formation energy of materials from their compositions; using an experimental data set of $$1,643$$ 1 , 643 obse…human literature · ext:ec85657738f165d2 | 0.55 |
Background mentions carry no weight and are not part of the line. Every number recomputes from the public log.