Source data for “Learning when to use physical relationships in descriptor-based tabular machine learning”
Abstract
This dataset contains the source data of the article “Learning when to use physical relationships in descriptor-based tabular machine learning” (AI for Science). The study compares Direct, Physics-only, Residual-only, Ordinary stacking, PhysResStack and PhysicsGate models on 16 target–route endpoints from four published benchmarks of solid-state ionic conductors, thermoelectric materials, liquid metal batteries and photovoltaic devices. The deposit provides the seed-level evaluation metrics (Data S2), split identifiers (Data S1) and the values plotted in Figures 2–5 and S1–S3. Raw third-party datasets are not redistributed; their sources are listed in Table S1 of the Supplementary Information.
Authors 10
-
Han Zhou Aachen
Forschungszentrum Jülich · RWTH Aachen University
Affiliation as printed
Forschungszentrum Jülich
RWTH Aachen University
-
Shiwen Wang Aachen
Forschungszentrum Jülich · RWTH Aachen University
Affiliation as printed
Forschungszentrum Jülich
RWTH Aachen University
-
Mengni Huo Aachen
Forschungszentrum Jülich · RWTH Aachen University
Affiliation as printed
Forschungszentrum Jülich
RWTH Aachen University
-
Affiliation as printed
Nanchang University
-
Affiliation as printed
Nanchang University
-
Affiliation as printed
Forschungszentrum Jülich
High Power Research Laboratory
-
Affiliation as printed
Forschungszentrum Jülich
-
Affiliation as printed
Forschungszentrum Jülich
-
Nanchang University · Gannan Normal University
Affiliation as printed
Nanchang University
Gannan Normal University
-
Rüdiger-A. Eichel Aachen
Forschungszentrum Jülich · RWTH Aachen University
Affiliation as printed
Forschungszentrum Jülich
RWTH Aachen University