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David Martin-Calle

Publications and source records attributed to David Martin-Calle.

3 recordsLinked to original sources

A Practical Noise2Noise Denoising Pipeline for High-Throughput Raman Spectroscopy

A lightweight and reproducible denoising pipeline for high-throughput Raman spectroscopy is presented. The approach relies on a one-dimensional convolutional autoencoder trained using a Noise2Noise strategy, requiring neither external spectral libraries nor high signal-to-noise reference spectra for training. From a reduced training subset composed of repeated short-exposure acquisitions, the model learns to reconstruct Raman spectra while efficiently suppressing stochastic noise. The method is evaluated on a heterogeneous mineral sample, using both quantitative spectral fidelity metrics (RMSE, SNR, SSIM) and task-oriented criteria based on unsupervised K-means classification. Results demonstrate that integration times as short as 5 ms per spectrum, which are typically insufficient for reliable interpretation, yield denoised spectra with high fidelity to the reference data while preserving chemically coherent maps. This work provides a practical trade-off between spectral quality and acquisition speed, enabling fast, adaptable Raman workflows compatible with routine laboratory use. It also offers a transferable framework for other one-dimensional spectroscopic modalities.

cs.AI↗

Reproducible container solutions for codes and workflows in materials science

A computing solution combining the GNU Guix functional package manager with the Apptainer container system is presented. This approach provides fully declarative and reproducible software environments suitable for computational materials science. Its versatility and performance enable the construction of complete frameworks integrating workflow managers such as AiiDA, and Ewoks that can be deployed on HPC infrastructures. The efficiency of the solution is illustrated through several examples: (i) AiiDA workflows for automated dataset construction and analysis as well as path-integral molecular dynamics based on ab initio calculations; (ii) workflows for the training of machine-learning interatomic potentials; and (iii) an Ewoks workflow for the automated analysis of coherent X-ray diffraction data in large-scale synchrotron facilities. These examples demonstrate that the proposed environment provides a reliable and reproducible basis for computational and data-driven research in materials science.

cond-mat.mtrl-sci↗

Domain convexification: a simple model for invasion processes

We propose an invasion model where domains grow up to their convex hulls and merge when they overlap. This model can be seen as a continuum and isotropic counterpart of bootstrap percolation models. From numerical investigations of the model starting with randomly scattered discs in two dimensions, we find an invasion transition that occurs via macroscopic avalanches. The disc concentration threshold and the sharpness of the transition are found to decrease as the system size is increased. Our results are consistent with a vanishing threshold in the limit of infinitely large system sizes. However this limit could not be investigated by simulations. For finite initial concentrations of discs, the cluster size distribution presents a power-law tail characterized by an exponent that varies approximately linearly with the initial concentration of discs. These results at finite initial concentration open novel directions for the understanding of the transition in systems of finite size. Furthermore, we find that the domain area distribution has oscillations with discontinuities. In addition, the deviation from circularity of large domains is constant. Finally, we compare our results to experimental observations on de-adhesion of graphene induced by the intercalation of nanoparticles.

cond-mat.stat-mech↗