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SLUSCHI Job Status

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Submission Policy

To keep this public service available, each email address may submit only one SLUSCHI job every 24 hours. Additional submissions within 24 hours will not be queued.

If you would like to run more calculations or discuss additional configurations, please email qhong@alumni.caltech.edu to discuss a plan or collaboration.

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MeltBench

SLUSCHI-UP predictions are continuously validated against experimentally measured melting temperatures through MeltBench, our open benchmark platform for inorganic materials.

View MeltBench benchmarks →

Definitions

SLUSCHI-UP MT: SLUSCHI-UP/uMLIP prediction from the simulation workflow.
PBE Correction: a density-functional-theory (DFT) correction to the raw SLUSCHI-UP/uMLIP melting-temperature prediction. The correction is derived from PBE calculations and is intended to reduce systematic errors inherited from the underlying machine-learning potential. Additional theoretical details are available in Hong et al. (2026) .

EXPT Correction: MeltBench reveals that universal machine-learning interatomic potentials (uMLIPs) can systematically overestimate or underestimate melting temperatures for certain classes of materials. To account for these residual biases, we train a surrogate correction model on the difference between uMLIP/PBE predictions and experimentally measured melting temperatures. The reported value applies this learned correction to the baseline prediction (using the PBE-corrected value when available, otherwise the raw uMLIP melting temperature). GNN MT: melting temperature predicted by the GNN model.
Expt MT: experimental melting temperature from MeltBench.
Melting temperature here is T0 in the phase diagram community. In other words, SLUSCHI gives the temperature at which the solid and liquid phases of the same composition have equal free energy.

Showing jobs with status: running

Click a Job ID (for example, ab123) to view detailed results, simulation logs, and output files.