# Transfer Learning

Screening superconductors has long rested on a single experimental parameter, the critical temperature, while quantities that decide usefulness, such as the upper critical field and critical current density, sit unstructured in the literature. AutoSuperCon uses a large language model to extract eight experimental superconducting parameters from more than 47,000 open arXiv preprints in the superconductivity category, adds digitized entries from the Roberts 1976 survey, and yields 20,354 entries for 6,793 superconductors. On the 2,439 materials shared with NIMS SuperCon, critical temperatures show a median deviation of 0.25 K, or 3.5% relative; the database supports large-scale tests of empirical relations and composition-based prediction of critical temperature and upper critical field.

Screening superconductors has long rested on a single experimental parameter, the critical temperature, while quantities that decide usefulness, such as the upper critical field and critical current density, sit unstructured in the literature. AutoSuperCon uses a large language model to extract eight experimental superconducting parameters from more than 47,000 open arXiv preprints in the superconductivity category, adds digitized entries from the Roberts 1976 survey, and yields 20,354 entries for 6,793 superconductors. On the 2,439 materials shared with NIMS SuperCon, critical temperatures show a median deviation of 0.25 K, or 3.5% relative; the database supports large-scale tests of empirical relations and composition-based prediction of critical temperature and upper critical field.

Formation energies from different exchange-correlation functionals have not been directly mergeable: in MatPES the same structures differ by 107 meV/atom on average between PBE and r2SCAN. Using more than 380,000 structurally paired PBE–r2SCAN entries, researchers trained a graph neural network to predict the energy residual between the two functionals and correct PBE formation energies onto the r2SCAN scale. With GPTFF as the backbone, the model reaches a mean absolute error of 14.3 meV/atom on the held-out test set, below the 18.2 meV/atom of CHGNet.

Formation energies from different exchange-correlation functionals have not been directly mergeable: in MatPES the same structures differ by 107 meV/atom on average between PBE and r2SCAN. Using more than 380,000 structurally paired PBE–r2SCAN entries, researchers trained a graph neural network to predict the energy residual between the two functionals and correct PBE formation energies onto the r2SCAN scale. With GPTFF as the backbone, the model reaches a mean absolute error of 14.3 meV/atom on the held-out test set, below the 18.2 meV/atom of CHGNet.

Synchrotron X-ray diffraction produces a 2D diffractogram per second, and image models seem able to read α and β phase fractions of Ti–6Al–4V from them. Two models reach similar accuracy after fine-tuning on 250 experimental diffractograms, yet robustness tests and attention maps show the ImageNet-only model infers fractions from background and pixel noise, while the model pre-trained on simulated diffractograms relies on Bragg reflections. Accuracy alone does not validate a model.

Synchrotron X-ray diffraction produces a 2D diffractogram per second, and image models seem able to read α and β phase fractions of Ti–6Al–4V from them. Two models reach similar accuracy after fine-tuning on 250 experimental diffractograms, yet robustness tests and attention maps show the ImageNet-only model infers fractions from background and pixel noise, while the model pre-trained on simulated diffractograms relies on Bragg reflections. Accuracy alone does not validate a model.

A CRISPRi guide-efficacy model trained in K562 cells barely transferred to the independent Dolcetto library from Sanson et al. 2018 in HT29 and A375 cells, reaching Spearman correlations of only 0.008 and 0.012. Keeping the same 101 features, architecture and hyperparameters but replacing the within-gene percentile label with a continuous cross-study z-score raised transfer to 0.136 in HT29 and 0.158 in A375, and leave-one-chromosome-out performance in K562 from 0.375 to 0.429. The transfer failure therefore traces mainly to label design rather than missing chromatin features.

A CRISPRi guide-efficacy model trained in K562 cells barely transferred to the independent Dolcetto library from Sanson et al. 2018 in HT29 and A375 cells, reaching Spearman correlations of only 0.008 and 0.012. Keeping the same 101 features, architecture and hyperparameters but replacing the within-gene percentile label with a continuous cross-study z-score raised transfer to 0.136 in HT29 and 0.158 in A375, and leave-one-chromosome-out performance in K562 from 0.375 to 0.429. The transfer failure therefore traces mainly to label design rather than missing chromatin features.

When a thin-walled grid conical shell is milled, the rib layout makes local stiffness jump from tool position to tool position, and the cutting force pushes the workpiece into an elastic retreat along the shell normal, so the actual wall thickness falls short of the theoretical depth of cut. The new route treats finite-element simulation as the source domain and real milling experiments as the target domain, uses transfer learning to predict the normal cutting force first, then feeds that load into an equivalent-stiffness analytical model to compute deflection. Across four test sets the method reached 93.05% to 95.27% prediction accuracy, above the CNN, PINN and TL comparison models.

When a thin-walled grid conical shell is milled, the rib layout makes local stiffness jump from tool position to tool position, and the cutting force pushes the workpiece into an elastic retreat along the shell normal, so the actual wall thickness falls short of the theoretical depth of cut. The new route treats finite-element simulation as the source domain and real milling experiments as the target domain, uses transfer learning to predict the normal cutting force first, then feeds that load into an equivalent-stiffness analytical model to compute deflection. Across four test sets the method reached 93.05% to 95.27% prediction accuracy, above the CNN, PINN and TL comparison models.

Each signaling pathway leaves a consistent fingerprint of gene activity across cell types, so signaling history no longer has to be reconstructed separately for every cell type. IRIS is a neural-network model that reads a cell's overall gene activity and estimates which pathways were switched on at each developmental stage. Tested on single cells from mouse embryos during gastrulation, it predicted when and where pathways activate in heart, gut, muscle and spinal cord lineages, and its prediction for a lung-development cell type was confirmed in mouse embryo experiments.

Each signaling pathway leaves a consistent fingerprint of gene activity across cell types, so signaling history no longer has to be reconstructed separately for every cell type. IRIS is a neural-network model that reads a cell's overall gene activity and estimates which pathways were switched on at each developmental stage. Tested on single cells from mouse embryos during gastrulation, it predicted when and where pathways activate in heart, gut, muscle and spinal cord lineages, and its prediction for a lung-development cell type was confirmed in mouse embryo experiments.