Abstract Machine learning is shaping up our lives in many ways. In analytical sciences, machine learning provides an unprecedented opportunity to extract information from complex or big datasets in chromatography, mass spectrometry, NMR, and spectroscopy, among others. This is especially the case in Raman and surface-enhanced Raman scattering (SERS) techniques where vibrational spectra of complex chemical mixtures are acquired as large datasets for the analysis or imaging of chemical systems. The classical linear methods of processing the information no longer suffice and thus machine learning methods for extracting the chemical information from Raman and SERS experiments have been implemented recently. In this review, we will provide a brief overview of the most common machine learning techniques employed in Raman, a guideline for new users to implement machine learning in their data analysis process, and an overview of modern applications of machine learning in Raman and SERS.
Simon Ghyselincks, Valeriia Okhmak, Stefano Zampini
et al.
Abstract Reconstructing the structural geology and mineral composition of the first few kilometers of the Earth's subsurface from sparse or indirect surface observations remains a long‐standing challenge with critical applications in mineral exploration, geohazard assessment, and geotechnical engineering. This inherently ill‐posed problem is often addressed by classical geophysical inversion methods, which typically yield a single maximum‐likelihood model that fails to capture the full range of plausible geology. The adoption of modern deep learning methods has been limited by the lack of large 3D training data sets. We address this gap with StructuralGeo, a geological simulation engine that mimics eons of tectonic, magmatic, and sedimentary processes to generate a virtually limitless supply of realistic synthetic 3D lithological models. Using this data set, we train both unconditional and conditional generative flow‐matching models with a 3D attention U‐Net architecture. The resulting foundation model can reconstruct multiple plausible 3D scenarios from surface topography and sparse borehole data, depicting structures such as layers, faults, folds, and dikes. By sampling many reconstructions from the same observations, we introduce a probabilistic framework for estimating the size and extent of subsurface features. While the realism of the output is bounded by the fidelity of the training data to true geology, this combination of simulation and generative AI functions offers a flexible prior for probabilistic modeling, regional fine‐tuning, and use as an AI‐based regularizer in traditional geophysical inversion workflows.
Geophysics. Cosmic physics, Information technology
Machine learning is used to approximate density functionals. For the model problem of the kinetic energy of noninteracting fermions in 1D, mean absolute errors below 1 kcal/mol on test densities similar to the training set are reached with fewer than 100 training densities. A predictor identifies if a test density is within the interpolation region. Via principal component analysis, a projected functional derivative finds highly accurate self-consistent densities. The challenges for application of our method to real electronic structure problems are discussed.
Adrian P. Wozniak, Mateusz Milczarek, Joanna Wozniak
With the growing popularity of machine learning, implementations of the environment for developing and maintaining these models, called MLOps, are becoming more common. The number of publications in this area is relatively small, although growing rapidly. Our goal was to review the current state of the literature in the MLOps area and answer the following research questions: What classes of tools are used in MLOps environments? Which tool implementations are the most popular? What processes are implemented within MLOps? What metrics are used to measure the effectiveness of MLOps implementation? Based on this review, we identified classes of tools included in the MLOps architecture, along with their most popular implementations. While some tools originate from DevOps practices, others, such as Model Orchestrators, Feature Stores, and Model Repositories, are unique to MLOps. We propose a reference MLOps architecture based on these findings and outline the stages of the model production process. We also sought metrics that would allow us to assess and compare the effectiveness of MLOps practices, but unfortunately, we were unable to find a satisfactory answer in this area.
Gabriele De Carolis, Vincenzo Giannico, Leonardo Costanza
et al.
This study investigated the application of high-resolution satellite imagery from SuperDove satellites combined with machine learning algorithms to estimate the spatiotemporal variability of some winter wheat parameters, including the relative leaf chlorophyll content (RCC), relative water content (RWC), and aboveground dry matter (DM). The research was carried out within an experimental field in Southern Italy during the 2024 growing season. Different machine learning (ML) algorithms were trained and compared using spectral band data and calculated vegetation indices (VIs) as predictors. Model performance was assessed using R<sup>2</sup> and RMSE. The ML models tested were random forest (RF), support vector regressor (SVR), and extreme gradient boosting (XGB). RF outperformed the other ML algorithms in the prediction of RCC when using VIs as predictors (R<sup>2</sup> = 0.81) and in the prediction of the RWC and DM when using spectral bands data as predictors (R<sup>2</sup> = 0.71 and 0.87, respectively). Model explainability was assessed with the SHAP method. A SHAP analysis highlighted that GNDVI, Cl1, and NDRE were the most important VIs for predicting RCC, while yellow and red bands were the most important for DM prediction, and yellow and nir bands for RWC prediction. The best model found for each target was used to model its seasonal trend and produce a variability map. This approach highlights the potential of integrating ML and high-resolution satellite imagery for the remote monitoring of wheat, which can support sustainable farming practices.