jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Irene Bassi | Cristina Nuzzi | Simone Pasinetti | Sabina Merlo | Valentina Bello
Source: ACTA IMEKO (article, 2026-03-20)
Field: Chemistry — Analytical Chemistry

Authenticity assessment of goat milk: detecting dilution with cow milk by ML-enhanced speckle pattern imaging Dilution of precious goat milk with cheaper cow milk represents one of the most common types of food fraud in the dairy industry. Existing methods for detecting the presence of cow milk mainly rely on chemical tests or spectroscopic analyses, which require dedicated laboratory facilities, expensive instrumentation, complex sample preparation, and long waiting times for results. We propose an innovative method based on the combination of speckle pattern imaging and artificial intelligence models to detect the adulteration of goat milk with cow milk. The instrumental setup we developed is based on a low-cost semiconductor laser and an industrial CMOS camera. We analyzed five milk samples and applied well-established machine learning models, achieving an accuracy of 96.9 % on the test set. These results are very promising and pave the way for the development of new opto-electronic systems to fight milk adulteration. Cow milk ; Speckle pattern ; Sample (material) ; Pattern recognition (psychology) ; Dilution Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Biosensors and Analytical Detection
Authors: Yachan Liu | Ping Yang | Gustavo Pérez | Aaron Sun | Wei Fan | Subhransu Maji | Peng Bai
Source: Research Square (preprint, 2026-01-30)
Field: Materials Science — Materials Chemistry

Representation Learning for Long-Chain Hydrocarbon Adsorption in Zeolites Nanoporous ; Graph ; Adsorption ; Convolutional neural network ; Artificial neural network ; Residual ; Transferability ; Representation (politics) Machine Learning in Materials Science ; Zeolite Catalysis and Synthesis ; Catalysis and Oxidation Reactions
Authors: Martin Hoffmann Petersen | Steen Lysgaard | Arghya Bhowmik | Kedar Hippalgaonkar | Juan Maria Garcia Lastra
Source: ArXiv.org (article, 2026-03-27)
Field: Materials Science — Materials Chemistry

Importance of Electronic Entropy for Machine Learning Interatomic Potentials Machine learning interatomic potentials (MLIPs) enable large-scale atomistic simulations but remain challenged in describing mixed-valence materials where charge ordering strongly influences thermodynamic stability. Here we investigate the role of electronic entropy in MLIP structural optimization of the battery cathode material \ce{NaFePO4}. We show that conventional MLIPs fail to reproduce the correct stability of intermediate \ce{Na} concentrations because structural optimization leads to incorrect \ce{Fe^{2+}}/\ce{Fe^{3+}} charge assignments, resulting in erroneous energy ordering and convex-hull predictions. Analysis of magnetic moments during structural optimization reveals that MLIPs are unable to capture electronic entropy associated with charge ordering. To address this limitation, we introduce an approach that embeds charge-state information directly into the MLIP representation by distinguishing between \ce{Fe^{2+}} and \ce{Fe^{3+}} environments during training. Retraining CHGNet, cPaiNN, and MACE with this representation enables accurate structural optimization, correct identification of charge ordering, and improved agreement with density functional theory convex hulls. Our results demonstrate that incorporating electronic entropy into MLIP representations is essential for modeling charge-disordered materials and provide a practical framework for extending MLIP simulations to mixed-valence transition-metal systems. Entropy (arrow of time) ; Electronic structure ; Representation (politics) ; Charge (physics) ; Density functional theory ; Principle of maximum entropy ; Charge density ; Optimization problem Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Thermal Expansion and Ionic Conductivity
Authors: HongHui Xiao | Hao Hu | Hanyi Mei | Hao Deng | Yongzhi Zhang | Jing Nie | Hao Wu | Karyne M. Rogers | Yuwei Yuan | Chunlin Li
Source: Food Safety and Health (article, 2026-01-01)
Field: Chemistry — Analytical Chemistry

Hyperspectral Imaging Combined With Image Fusion Features and Machine Learning to Discriminate Different Origins, Grades, and Shelf‐Life of Oranges ABSTRACT This study reports the first application of hyperspectral feature fusion technology combined with machine learning algorithm to qualitatively distinguish oranges from different growing regions, grades, and shelf‐lives. For origin traceability, a partial least squares discriminant analysis (PLS‐DA) model outperformed competing methods, including deep learning models. Based on a hyperspectral dataset of 146 oranges from four different producing regions in China, with the training and prediction accuracy reaching 100% and 97.1%, respectively. Furthermore, 224 oranges were assigned as either high or lower grade based on their chemical quality data (soluble solid content, vitamin C, and sugar–acid ratios). PLS‐DA and random forest (RFT) models, incorporating spectral processing and image fusion, established a grading mechanism with 94.4% prediction accuracy. Finally, for shelf‐life determination (0, 10, and 20 days), a PLS‐DA model utilizing spectral, image, and spectrum—image feature fusion data also achieved 94.4% accuracy. In addition, the fused characteristic bands combined with spectral analysis indicate that chlorophyll, sugar, and moisture content are the key chemical substances that distinguish different types of oranges. The results of this study provide strong technical support and theoretical evidence for applying hyperspectral fusion technology to other similar fruit evaluations. Hyperspectral imaging ; Pattern recognition (psychology) ; Fusion ; Random forest ; Linear discriminant analysis ; Partial least squares regression ; Sensor fusion ; Feature (linguistics) ; Support vector machine Spectroscopy and Chemometric Analyses ; Remote-Sensing Image Classification ; Remote Sensing in Agriculture
Authors: Vikram Chavan | Anantatamukala Amrutha | Debarna Bhattacharjee | K V Mani Krishna | Namit Pai | Samik Nag | Saurabh Kundu | Somnath Basu | Ajay Singh Panwar | David T. Fullwood | I. Samajdar
Source: Scripta Materialia (article, 2026-02-12)
Field: Materials Science — Materials Chemistry

Measurement of mesoscopic hydrostatic residual strain/stress – An emerging possibility Mesoscopic physics ; Hydrostatic equilibrium ; Residual stress ; Hydrostatic pressure ; Residual ; Deformation (meteorology) ; Diffraction Microstructure and mechanical properties ; Metal Forming Simulation Techniques ; High-Velocity Impact and Material Behavior
Authors: Li Ling Zhou | Mei-Fang Wang | Chaokun Huang | Meng Song | Xiujuan Wang
Source: Chinese Journal of Polymer Science (article, 2026-03-25)
Field: Materials Science — Polymers and Plastics

Prediction of Stress-strain Behavior for Polyurethane Elastomers Based on Machine Learning Elastomer ; Nonlinear system ; Polymer ; Intermolecular force ; Molecular descriptor ; Generalization ; Mean squared error ; Polyurethane Polymer composites and self-healing ; Machine Learning in Materials Science ; Elasticity and Material Modeling
Authors: P. Bernát Szabó | Zeno Schätzle | Frank Noé
Source: ArXiv.org (article, 2026-03-26)
Field: Materials Science — Materials Chemistry

Enabling ab initio geometry optimization of strongly correlated systems with transferable deep quantum Monte Carlo A faithful description of chemical processes requires exploring extended regions of the molecular potential energy surface (PES), which remains challenging for strongly correlated systems. Transferable deep-learning variational Monte Carlo (VMC) offers a promising route by efficiently solving the electronic Schrödinger equation jointly across molecular geometries at consistently high accuracy, yet its stochastic nature renders direct exploration of molecular configuration space nontrivial. Here, we present a framework for highly accurate ab initio exploration of PESs that combines transferable deep-learning VMC with a cost-effective estimation of energies, forces, and Hessians. By continuously sampling nuclear configurations during VMC optimization of electronic wave functions, we obtain transferable descriptions that achieve zero-shot chemical accuracy within chemically relevant distributions of molecular geometries. Throughout the subsequent characterization of molecular configuration space, the PES is evaluated only sparsely, with local approximations constructed by estimating VMC energies and forces at sampled geometries and aggregating the resulting noisy data using Gaussian process regression. Our method enables accurate and efficient exploration of complex PES landscapes, including structure relaxation, transition-state searches, and minimum-energy pathways, for both ground and excited states. This opens the door to studying bond breaking, formation, and large structural rearrangements in systems with pronounced multi-reference character. Ab initio ; Monte Carlo method ; Excited state ; Energy minimization ; Potential energy surface ; Gaussian ; Electronic structure ; Configuration space ; Space (punctuation) ; Quantum Monte Carlo Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Quantum many-body systems
Authors: Kasper Tolborg
Source: ChemRxiv (article, 2026-03-11)
Field: Materials Science — Materials Chemistry

Order-disorder phase transition and orientational defect formation in a hybrid organic-inorganic piezoelectric material Hybrid organic-inorganic materials offer an alternative to Pb-based ceramics for ferroelectric and piezoelectric applications. However, these materials suffer from ferroelectric-to-paraelectric phase transitions above which the piezoelectric response disappears. For most hybrid materials, this occurs below room temperature. Here, we investigate a notable exception, TMCMCdCl3 (TMCM = trimethylchloromethyl ammonium), which features an order-disorder phase transition at 400 K. By training a machine learning force field based on the scalable Allegro architecture and performing large scale molecular dynamics simulations, we show that the phase transition temperature can be predicted in close agreement with experiments, provided that dispersion interactions are included, and that the high temperature phase is dominated by dynamical disorder of the molecular cations. We uncover an unexpected molecular orientation in the high temperature phase, not following the expectations from the symmetry relations between ferroelectric and paraelectric phases, which contributes to entropic stabilisation of the high temperature phase. This demonstrates the shortcomings of previous model Hamiltonian based approaches, which did not include the unexpected molecular orientations. Finally, we show that orientational defects of the molecular cations are present in significant concentrations in the low temperature phase, suggesting that these can act as nucleation sites for polarisation switching. This paves the way for accurate modelling of ferroelectric-to-paraelectic phase transitions and design of hybrid organic-inorganic materials with exceptional room temperature piezoelectric response. Piezoelectricity ; Phase transition ; Phase (matter) ; Work (physics) ; Dislocation Ferroelectric and Piezoelectric Materials ; Solid-state spectroscopy and crystallography ; Advanced Sensor and Energy Harvesting Materials
Authors: Chong Zhang | Yuanbo Li | Congcong Cui | Shunai Che | Lu Han | Yuanyuan Cao
Source: Science China Chemistry (article, 2026-01-04)
Field: Chemistry — Physical and Theoretical Chemistry

Engineering of bioinspired hierarchical guanine crystals Nucleation ; Crystal (programming language) ; Crystallization ; Crystal growth ; Guanine ; Crystallite ; Exfoliation joint ; Birefringence Crystallography and molecular interactions ; Calcium Carbonate Crystallization and Inhibition ; Luminescence and Fluorescent Materials
Authors: Seunghyun Yoo | Sanghong Kim | Namkyung Yoon | Hwangnam Kim
Source: Open MIND (preprint, 2026-01-31)
Field: Chemistry — Spectroscopy

Contrastive Domain Generalization for Cross-Instrument Molecular Identification in Mass Spectrometry Identifying molecules from mass spectrometry (MS) data remains a fundamental challenge due to the semantic gap between physical spectral peaks and underlying chemical structures. Existing deep learning approaches often treat spectral matching as a closed-set recognition task, limiting their ability to generalize to unseen molecular scaffolds. To overcome this limitation, we propose a cross-modal alignment framework that directly maps mass spectra into the chemically meaningful molecular structure embedding space of a pretrained chemical language model. On a strict scaffold-disjoint benchmark, our model achieves a Top-1 accuracy of 42.2% in fixed 256-way zero-shot retrieval and demonstrates strong generalization under a global retrieval setting. Moreover, the learned embedding space demonstrates strong chemical coherence, reaching 95.4% accuracy in 5-way 5-shot molecular re-identification. These results suggest that explicitly integrating physical spectral resolution with molecular structure embedding is key to solving the generalization bottleneck in molecular identification from MS data. Generalization ; Embedding ; Chemical space ; Matching (statistics) ; Identification (biology) ; Pattern recognition (psychology) ; Space (punctuation) ; Mass spectrometry Mass Spectrometry Techniques and Applications ; Computational Drug Discovery Methods ; Machine Learning in Materials Science
Authors: Filip Franciszek Powala | Piotr Szustakiewicz | Przemyslaw Pula | Pawel Wawrzyniec Majewski
Source: Macromolecular Rapid Communications (article, 2026-03-20)
Field: Materials Science — Materials Chemistry

Automated Photothermal Control of Block Copolymer Self‑Assembly and Metal Oxide Nanostructures Sintering We report a microscope-based photothermal platform that combines structured laser light projection with machine-learning-optimized illumination to enable rapid, spatially selective thermal processing of block copolymer (BCP) films and their inorganic replicas. Two neural-network models were developed: optical-to-thermal (O2T), which predicts radial thermal profiles from optical patterns, and thermal-to-optical (T2O), which generates the optical pattern required to produce a target thermal field. Using these tools, we achieved near-top-hat temperature uniformity over a 1 mm diameter area and accessed temperatures from ∼100°C-350°C for BCP ordering and in excess of 500°C for local sintering/ashing of metal-oxide nanostructures. The lab-on-chip workflow permitted screening of more than 80 process conditions on only a handful of samples and in hours, reducing material and time needs by roughly a factor of 20 vs. conventional thermal annealing. We demonstrate controlled photothermal driving of self-assembly, rapid local removal of polymer templates, and post-processing sintering of In<sub>2</sub>O<sub>3</sub> and Fe<sub>2</sub>O<sub>3</sub> BCP-templated nanowires, with correlated changes in carbon residue and nanowire morphology as a function of temperature. The approach enables automated, high-throughput exploration of optical-thermal interactions and ordering kinetics, offering a scalable route to accelerate optimization of nanoscale materials and device-relevant processing. Photothermal therapy ; Copolymer ; Nanostructure ; Thermal ; Sintering ; Nanoscopic scale ; Polymer ; Nanowire ; Photothermal effect Block Copolymer Self-Assembly ; Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization
Authors: Seunghyun Yoo | Sanghong Kim | Namkyung Yoon | Hwangnam Kim
Source: ArXiv.org (article, 2026-01-31)
Field: Chemistry — Spectroscopy

Contrastive Domain Generalization for Cross-Instrument Molecular Identification in Mass Spectrometry Identifying molecules from mass spectrometry (MS) data remains a fundamental challenge due to the semantic gap between physical spectral peaks and underlying chemical structures. Existing deep learning approaches often treat spectral matching as a closed-set recognition task, limiting their ability to generalize to unseen molecular scaffolds. To overcome this limitation, we propose a cross-modal alignment framework that directly maps mass spectra into the chemically meaningful molecular structure embedding space of a pretrained chemical language model. On a strict scaffold-disjoint benchmark, our model achieves a Top-1 accuracy of 42.2% in fixed 256-way zero-shot retrieval and demonstrates strong generalization under a global retrieval setting. Moreover, the learned embedding space demonstrates strong chemical coherence, reaching 95.4% accuracy in 5-way 5-shot molecular re-identification. These results suggest that explicitly integrating physical spectral resolution with molecular structure embedding is key to solving the generalization bottleneck in molecular identification from MS data. Generalization ; Embedding ; Chemical space ; Matching (statistics) ; Identification (biology) ; Pattern recognition (psychology) ; Space (punctuation) ; Mass spectrometry Mass Spectrometry Techniques and Applications ; Computational Drug Discovery Methods ; Machine Learning in Materials Science
Authors: N Serir | M Khenata | Rabah Khenata | H R Jappor | D. Singh | Sh Rab | H. Meradji | S. Bin-Omran | Souraya Goumri-Said
Source: Physical Chemistry Chemical Physics (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

A combined first-principles and machine learning study of phase stability and thermoelectric properties in ZrSiPt ZrSiPt is a versatile ternary compound that stabilizes in two distinct crystal structures: a cubic half-Heusler phase and an orthorhombic TiNiSi-type phase. This study presents an integrated first-principles and machine learning (ML) investigation of its phase-dependent structural, electronic, mechanical, thermodynamic, and thermoelectric properties. Density functional theory (DFT) calculations with the GGA-PBE functional and TB-mBJ potential reveal that the cubic phase is an indirect-gap semiconductor (∼1.40 eV), while the orthorhombic phase is a semimetallic, mechanically stiff and anisotropic system. Spin-orbit coupling is shown to have a negligible effect on the electronic structure. Thermodynamic analysis within the quasi-harmonic approximation suggests a possible phase-stability crossover near 300 K, driven primarily by vibrational entropy. Refined transport calculations, incorporating deformation-potential-derived relaxation times and lattice thermal conductivity from the Debye-Callaway model, drastically revise the thermoelectric assessment: the cubic phase exhibits a high lattice thermal conductivity that suppresses its figure of merit (<i>ZT</i>), whereas the orthorhombic phase, with intrinsically low thermal conductivity, emerges as the more promising candidate for further investigation. A proof-of-concept machine-learning framework; trained on a synthetic dataset derived from DFT results and validated <i>via</i> stratified cross-validation; successfully classifies the crystal phase (∼90% accuracy) and predicts key thermoelectric properties. Feature-importance analysis identifies lattice constant and band gap as the dominant descriptors, directly linking geometric packing and electronic stability to phase selection. This combined DFT+ML approach not only elucidates the dual-phase behavior of ZrSiPt but also demonstrates a synergistic workflow for accelerating the design of phase-specific materials for thermoelectric, optoelectronic, and mechanically robust applications. Ternary operation ; Stability (learning theory) ; Orthorhombic crystal system ; Thermoelectric effect ; Phase (matter) ; Crystal structure prediction ; Structural stability Advanced Thermoelectric Materials and Devices ; Heusler alloys: electronic and magnetic properties ; Machine Learning in Materials Science
Authors: Simon Gramatte | X. Wang | Michael Alejandro Hernández Bertrán | C. Cancellieri | Giovanni Pizzi | Deborah Prezzi | Iurii Timrov | O. Politano | I Utke | Lars P. H. Jeurgens | Vladyslav Turlo
Source: Journal of Materials Chemistry A (article, 2026-01-01)
Field: Materials Science — Ceramics and Composites

Bridging classical and quantum interpretation of chemical state analysis by XPS/HAXPES to resolve short-range order in amorphous alumina films Probing the local structure and chemistry of wide-bandgap amorphous oxide thin films remains challenging due to the limitations of lab-based spectroscopy. This work integrates X-ray photoelectron spectroscopy (XPS), hard X-ray photoemission spectroscopy (HAXPES), molecular dynamics simulations using machine-learning interatomic potentials, density-functional theory (DFT) calculations, and classical electrostatic modeling of final-state core-ionization effects in Al atoms to uncover the structure and chemistry of amorphous alumina polymorphs made with atomic layer deposition (ALD). DFT calculations using the ΔKohn-Sham method supported the interpretation of final-state effects and validated electrostatic model assumptions. Shifts in the measured Auger parameters were interpreted as extra-atomic relaxation energies, revealing sensitivity to the local coordination environment. Structural disorder and thermal fluctuations were found to govern the distribution of extra-atomic relaxation energies, suggesting that cryo-XPS can isolate and reveal intrinsic structural building blocks of amorphous oxides. Simulated heating and annealing demonstrated that Auger parameter shifts can serve as indicators of phase decomposition in H-supersaturated ALD amorphous alumina. These findings provide a pathway for comprehensive interpretation and predictive modeling of XPS spectra in amorphous wide-bandgap oxides. Bridging (networking) ; Amorphous solid ; Quantum chemical ; Nanoscopic scale ; Interpretation (philosophy) ; Solid-state ; Chemical bond Advanced ceramic materials synthesis ; Thermal Expansion and Ionic Conductivity ; X-ray Diffraction in Crystallography
Authors: Nikhil Rampal | Stephen E. Weitzner | Fredrick Omenya | Marissa Wood | David Reed | Xiaolin Li | Jonathan R. I. Lee | Liwen F. Wan
Source: Energy storage materials (article, 2026-02-11)
Field: Materials Science — Materials Chemistry

Physics-informed machine learning exploration of Na storage mechanisms in disordered carbon Carbon fibers ; Ion ; Diffusion ; Amorphous solid ; Amorphous carbon ; Function (biology) ; Molecular dynamics ; Sodium Diamond and Carbon-based Materials Research ; Machine Learning in Materials Science ; Fuel Cells and Related Materials
Authors: Anton Leonov | Eugeny Yu. Epifanov | Igor S. Gerasimov | Timofey V. Losev | Michael G. Medvedev
Source: Journal of Chemical Theory and Computation (article, 2026-02-20)
Field: Materials Science — Materials Chemistry

The Potential Blind Spot of the τ-Dependent Exchange Density Functional Approximations Density functional theory (DFT) predicts the existence of an exact functional that allows one to obtain the energy and electron density of the ground state of any molecular system. Although the exact functional is not known, its approximations have become the workhorse of quantum chemistry. In this work, we demonstrate that exchange parts of all τ-dependent meta-GGAs (e.g., TPSS, r<sup>2</sup>SCAN, M06-L, and so on), which are often the methods of choice in current materials modeling, have the same blind spot as much simpler GGAs (e.g., PBE) on two-electron densities, highlighting the scarcity of data about electron density available to a functional. We demonstrate that to overcome this blind spot, a meta-GGA functional should incorporate nonclassic ingredients such as the Laplacian of electron density, which finally becomes possible with the rise of machine learning techniques for the construction of density functional approximations. Density functional theory ; Orbital-free density functional theory ; Blind spot ; Hybrid functional ; Energy functional ; Quantum ; Ground state ; Thomas–Fermi model ; Electron ; Laplace operator Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Advanced Physical and Chemical Molecular Interactions