jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Alfonso Ferretti | Giacomo Melani | L. Benedetti | Robert A. Sorodoc | Alessandro Fortunelli | Giuseppe Brancato
Source: Journal of Chemical Information and Modeling (article, 2025-11-12)
Field: Materials Science — Materials Chemistry

Accurate Simulations of Water and Aqueous Solutions through Fine-Tuned Dispersion-Corrected Density Functional Theory and Machine-Learning Interatomic Potentials Dispersion-corrected density functional theory (DFT-D) is widely employed to model large molecular systems at an affordable computational cost and to develop machine-learning interatomic potentials (MLIPs), enabling reliable molecular dynamics (MD) simulations of condensed-phase systems. Yet, given a molecular system, the choice of a specific DFT-D model that can achieve the necessary accuracy over an extended range of physicochemical properties and conditions is generally not trivial. Here, we report an effective computational strategy for enhancing the accuracy of standard DFT-D models toward high-level quantum mechanical data and for developing MLIPs preserving the same high fidelity. Taking water as a paradigmatic example, we derive a novel MLIP and demonstrate that its use allows us to accurately predict a wide range of properties in diverse forms, from small clusters to bulk liquid and ice, such as radial distribution functions, fusion/vaporization enthalpies, diffusion constants, and density isobars, capturing remarkably well its peculiar and anomalous behavior, often elusive even to standard first-principle MD simulations. Furthermore, we show how the same computational strategy can be readily extended to treat aqueous solutions. Considering MgCl<sub>2</sub> in water as a test case, we develop a MLIP and use it to predict the metal ion hydration structure and the water exchange dynamics exhibiting a significantly improved agreement with experiments with respect to both standard DFT-D and classical force fields. Molecular dynamics ; Density functional theory ; Range (aeronautics) ; Aqueous solution ; Diffusion ; Water model ; Liquid water ; Interatomic potential ; Experimental data Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Spectroscopy and Quantum Chemical Studies
Authors: Nicolas Hayer (20484275) | Hans Hasse (1455325) | Fabian Jirasek (5236307)
Source: Figshare (article, 2025-05-09)
Field: Materials Science — Materials Chemistry

Modified UNIFAC2.0-A Group-Contribution Method Completedwith Machine Learning Predicting thermodynamic properties of mixtures is a cornerstone of chemical engineering, yet conventional group-contribution (GC) methods like modified UNIFAC (Dortmund) remain limited by incomplete parameter tables. To address this, we present modified UNIFAC 2.0, a hybrid model that integrates a matrix completion method from machine learning into the GC framework, allowing for the simultaneous training of all pair-interaction parameters, including those that cannot be fitted directly due to missing data. By training on more than 500,000 experimental data points for activity coefficients and excess enthalpies from the Dortmund Data Bank, modified UNIFAC 2.0 achieves improved accuracy, while significantly expanding the predictive scope compared to the latest published modified UNIFAC (Dortmund) version, which covers only 39% of all possible interactions. Its flexible design allows updates with new experimental data or customizations for specific applications. The new model can easily be implemented in established simulation software with complete parameter tables readily available. UNIFAC ; Experimental data ; Missing data ; Training set ; Work (physics) Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; Process Optimization and Integration
Authors: Yang Hu | Giacomo Lorenzin | Jeyun Yeom | Manura Liyanage | W.A. Curtin | Lars P. H. Jeurgens | Jolanta Janczak‐Rusch | Claudia Cancellieri | Vladyslav Turlo
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Ceramics and Composites

Origin of Interface Stress Enhancement and Compressive-to-Tensile Stress Transition in Immiscible Nanomultilayers Materials science ; Stress (linguistics) ; Ultimate tensile strength ; Composite material ; Compressive strength ; Interface (matter) Advanced ceramic materials synthesis ; Advanced materials and composites ; Metal and Thin Film Mechanics
Authors: Victoria Da Poian | Sarah M. Hörst | Eric Lyness | Xiang Li | Ryan M. Danell | W. B. Brinckerhoff | Bethany Theiling
Source: Frontiers in Astronomy and Space Sciences (article, 2025-12-18)
Field: Chemistry — Spectroscopy

Augmenting sparse spaceflight mass spectra datasets for machine learning applications Mass spectrometers are powerful instruments that aim to identify unknown compounds via their mass-to-charge ratio and perform quantitative and semi-quantitative analysis. These instruments have been essential to space missions over the past several decades ( e.g. , Pioneer Venus, Viking, Galileo, Cassini, Mars Science Laboratory) with several more en route ( e.g. , JUpiter ICy moons Explorer (JUICE), Europa Clipper) or under development ( e.g. , Rosalind Franklin, Dragonfly). However, future missions targeting remote planetary bodies increasingly face limited data transmission rates and volumes, which limit the amount of information that can be sent back to Earth. These challenges highlight the need for onboard science autonomy to optimize science return. Machine learning (ML) and data science tools can significantly contribute to the development of science autonomy by enabling rapid interpretation and prioritization of science data. Yet, these efforts for planetary science applications are hindered by the scarcity of representative datasets for training models, especially for complex flight instruments. In this work, we build on our earlier science autonomy work using the Mars Organic Molecule Analyzer (MOMA) instrument for the Rosalind Franklin (ExoMars) mission as a proof-of-concept. We investigate the generation of artificial mass spectra through “manual” augmentation techniques and evaluate their performance on mass spectrometer (MS) data using the laser desorption/ionization mass spectrometry (LDMS) mode of the flight-like MOMA engineering test unit (ETU). We implement basic transformation-based augmentation methods such as peak intensity randomization, peak shifting (by limited and realistic m/z values), etc. We assess their scientific integrity in collaboration with instrument experts and investigate how the inclusion of generated data affects the performance of ML algorithms for mass spectral analysis. We compare the performance of supervised learning models on predicting the chemical categories of new input mass spectra, both with and without augmented data, to evaluate the impact of these techniques. Our work provides guidelines for developing realistic augmented mass spectra without compromising scientific validity, while also contributing to the development of a mature framework for ML tools in MS data analysis, advancing science autonomy for existing and future planetary missions. Exploration of Mars ; Mars Exploration Program ; Spacecraft ; Space exploration ; NASA Deep Space Network ; Robotic spacecraft ; Scientific instrument ; Space Science Mass Spectrometry Techniques and Applications ; Planetary Science and Exploration ; Machine Learning in Materials Science
Authors: Songxue Chi | Feng Ye | Stephan Rosenkranz | Xing He | Jinsong Huang | Tyson Lanigan-Atkins | Haotong Wei | Chengjie Mao | Mayanak K. Gupta | Tao Hong | R. Osborn | Matthew Krogstad | Olivier Delaire | Yaohua Liu
Source: UNC Libraries (article, 2025-10-04)
Field: Chemistry — Inorganic Chemistry

Multiple Lattice Instabilities and Complex Ground State in Cs2AgBiBr6 Metal-halide perovskites (MHPs) are attracting considerable interest for optoelectronic applications, with Cs2AgBiBr6 one of the main contenders among lead-free systems. Cs2AgBiBr6 crystallizes in a nominally double-perovskite structure, but exhibits a soft lattice with large atomic fluctuations characteristic of MHPs. While crucial to understand electron-phonon and phonon-phonon couplings, the spatiotemporal correlations of these fluctuations remain largely unknown. Here, we reveal these correlations using comprehensive neutron and x-ray scattering measurements on Cs2AgBiBr6 single crystals, complemented with first-principles simulations augmented with machine-learned neural-network potentials. We report the discovery of an unexpected complex modulated ground-state structure containing several hundred atoms, arising from a soft-phonon instability of the low-temperature tetragonal phase. Further, our experiments and simulations both reveal extensive correlated two-dimensional fluctuations of Br octahedra at finite temperature, arising from soft optic phonons that are strongly broadened by anhamonicity, reflecting very shallow potential wells. These results provide new insights into the atomic structure and fluctuations in MHPs, critical to understand and control their thermal and optoelectronic properties. Tetragonal crystal system ; Phonon ; Lattice (music) ; Thermal fluctuations ; Instability ; Ground state ; Scattering Inorganic Chemistry and Materials ; Optical properties and cooling technologies in crystalline materials ; Rare-earth and actinide compounds
Authors: Yanbang Tang
Source: Annals of Nuclear Energy (article, 2025-12-01)
Field: Materials Science — Materials Chemistry

An AutoML-driven surrogate model for high-fidelity prediction of photon shielding spectra Electromagnetic shielding ; Monte Carlo method ; Surrogate model ; Photon ; Energy (signal processing) ; Attenuation ; Detector ; Spectral line Radiation Shielding Materials Analysis ; Nuclear reactor physics and engineering ; Graphite, nuclear technology, radiation studies
Authors: Baoming Shi | Lei Zhang | Qiang Du
Source: ArXiv.org (preprint, 2025-10-15)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A Stochastic Algorithm for Searching Saddle Points with Convergence Guarantee Saddle points provide a hierarchical view of the energy landscape, revealing transition pathways and interconnected basins of attraction, and offering insight into the global structure, metastability, and possible collective mechanisms of the underlying system. In this work, we propose a stochastic saddle-search algorithm to circumvent exact derivative and Hessian evaluations that have been used in implementing traditional and deterministic saddle dynamics. At each iteration, the algorithm uses a stochastic eigenvector-search method, based on a stochastic Hessian, to approximate the unstable directions, followed by a stochastic gradient update with reflections in the approximate unstable direction to advance toward the saddle point. We carry out rigorous numerical analysis to establish the almost sure convergence for the stochastic eigenvector search and local almost sure convergence with an $O(1/n)$ rate for the saddle search, and present a theoretical guarantee to ensure the high-probability identification of the saddle point when the initial point is sufficiently close. Numerical experiments, including the application to a neural network loss landscape and a Landau-de Gennes type model for nematic liquid crystal, demonstrate the practical applicability and the ability for escaping from "bad" areas of the algorithm. Hessian matrix ; Saddle point ; Convergence (economics) ; Saddle ; Rate of convergence ; Stability (learning theory) ; Eigenvalues and eigenvectors ; Stochastic approximation Liquid Crystal Research Advancements ; Nonlinear Dynamics and Pattern Formation ; Advanced Memory and Neural Computing
Authors: Alfonso Ferretti (22606762) | Giacomo Melani (13169979) | Luca Benedetti (22233660) | Robert A. Sorodoc (22606765) | Alessando Fortunelli (22606768) | Giuseppe Brancato (715249)
Source: Figshare (article, 2025-11-12)
Field: Materials Science — Materials Chemistry

Accurate Simulationsof Water and Aqueous Solutionsthrough Fine-Tuned Dispersion-Corrected Density Functional Theoryand Machine-Learning Interatomic Potentials Dispersion-corrected density functional theory (DFT-D) is widely employed to model large molecular systems at an affordable computational cost and to develop machine-learning interatomic potentials (MLIPs), enabling reliable molecular dynamics (MD) simulations of condensed-phase systems. Yet, given a molecular system, the choice of a specific DFT-D model that can achieve the necessary accuracy over an extended range of physicochemical properties and conditions is generally not trivial. Here, we report an effective computational strategy for enhancing the accuracy of standard DFT-D models toward high-level quantum mechanical data and for developing MLIPs preserving the same high fidelity. Taking water as a paradigmatic example, we derive a novel MLIP and demonstrate that its use allows us to accurately predict a wide range of properties in diverse forms, from small clusters to bulk liquid and ice, such as radial distribution functions, fusion/vaporization enthalpies, diffusion constants, and density isobars, capturing remarkably well its peculiar and anomalous behavior, often elusive even to standard first-principle MD simulations. Furthermore, we show how the same computational strategy can be readily extended to treat aqueous solutions. Considering MgCl<sub>2</sub> in water as a test case, we develop a MLIP and use it to predict the metal ion hydration structure and the water exchange dynamics exhibiting a significantly improved agreement with experiments with respect to both standard DFT-D and classical force fields. Molecular dynamics ; Density functional theory ; Range (aeronautics) ; Aqueous solution ; Liquid water ; Water model ; Diffusion ; Interatomic potential ; Experimental data Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Spectroscopy and Quantum Chemical Studies
Authors: M. Uchida | T. Yokoi | Y. Ogura | K. Matsunaga
Source: Physical Review Materials (article, 2025-11-25)
Field: Materials Science — Materials Chemistry

Application of neural-network potential based on trainable descriptor to crystallographically complex lattice defects in silicon A trainable descriptor based on artificial neural networks (ANN), developed in our recent study [M. Uchida et al., Phys. Rev. Mater. 8, 103805 (2024)], is integrated into a neural-network potential (NNP) and applied to various lattice defects in molecular simulations of silicon. The ANN descriptor achieves higher predictive accuracy for point defects, surfaces, and symmetric tilt grain boundaries (STGBs) than analytic-function-based descriptors. As a case study of crystallographically complex defects, the ANN descriptor is applied to asymmetric tilt grain boundaries (ATGBs), where favorable atomic structures are governed by the competition between multiple structural units. For the \ensuremath{\Sigma}9(111)||(115) ATGB, the predicted atomic structure shows a grain boundary energy close to the DFT value and agrees with previous electron microscopy observations. Similarly, \ensuremath{\Sigma}3, \ensuremath{\Sigma}5, and \ensuremath{\Sigma}9 ATGBs are also systematically examined with the combination of the ANN descriptor and DFT calculations. ATGBs in the \ensuremath{\Sigma}5 system can be described by structural units of two STGBs with the same \ensuremath{\Sigma} value, indicating that \ensuremath{\Sigma}5 ATGBs ideally facet into \ensuremath{\Sigma}5 STGB structures. In contrast, this trend does not hold for the \ensuremath{\Sigma}3 and \ensuremath{\Sigma}9 ATGBs due to the crystallographic incompatibility of the constituent STGBs. As a result, \ensuremath{\Sigma}3 and \ensuremath{\Sigma}9 ATGBs involve structural units that cannot be predicted by the crystallographic misorientation of the two gains. Electronic-structure analysis reveals that specific atoms at ATGBs create defect levels within the band gap, depending on the inclination angle of ATGBs. These results highlight that the high-accuracy ANN descriptor provides a deeper understanding of atomic and electronic structures and energetics of complex lattice defects. Misorientation ; Grain boundary ; Silicon ; Lattice (music) ; Crystallographic defect ; Tilt (camera) Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Surface and Thin Film Phenomena
Authors: Eaindra Lwin | Mathis Julian Gölz | Nils O. B. Lüttschwager | Martin A. Suhm | Silvan Käser | Valerii Andreichev | Magalie Brandes | Markus Meuwly
Source: ChemRxiv (preprint, 2025-06-01)
Field: Chemistry — Spectroscopy

Rotational, vibrational, conformational and diastereomeric dimer cooling of aminoalcohols in soft supersonic expansions and the monohydrate of dimethylaminoethanol Supersonic jet expansions allow to cool molecules and to form molecular complexes over a wide range of expansion conditions, ranging from nearly effusive expansions of the pure vapour to colder expansions in carrier gases. The resulting molecular species can be probed by infrared absorption and Raman scattering. They are not in thermal equilibrium, but one can assign effective average Boltzmann temperatures for rotational, selected vibrational and in low-barrier cases even conformational degrees of freedom. If the conformational energy difference is not known, one can at least follow the evolution of competing structures with expansion conditions and from this derive relative energy sequences. For aminoethanol and its N-methylated variants, we explore rotational band contour analysis in OH stretching fundamentals, intensity analysis of sum and difference transitions with scaffold modes, relative intensities of isomers and the evolution of transient relative chirality to estimate the associated Boltzmann temperatures or energy sequences. The focus is on trends rather than on highly accurate numbers, which anyway depend on details like nozzle geometry or precise nozzle distance. These trends can be used for a better understanding of the vibrational spectra of other hydrogen-bonded systems. We show that the B3LYP functional is not able to describe the diastereomeric energy sequence for the dimethylaminoethanol dimer and that thermal shifts of infrared bands due to the weakening of hydrogen bonding depend strongly on the hydrogen bond strain. We also discuss high-barrier cases of conformational isomerism, which resist supersonic cooling and allow for low-temperature spectroscopy of metastable isomers. We assign the OH stretching spectra of the monohydrate of dimethylaminoethanol with an unusually strong water downshift. Finally, one of the successful machine learning-based models of the first HyDRA blind challenge is applied and improved for predicting the position of its water OH stretch wavenumber. The original model, based on computed harmonic wavenumbers for moderately strong H-bonds leads to a difference of 461 cm-1 whereas improvements based on VPT2 calculations for the base model reduce this to 49 cm-1. Diastereomer ; Dimer ; Supersonic speed ; Chemistry ; Computational chemistry ; Stereochemistry ; Organic chemistry ; Physics ; Thermodynamics Molecular Spectroscopy and Structure ; Spectroscopy and Laser Applications ; Advanced Chemical Physics Studies
Authors: Min-Yu Li | Wenhua Rao | Shang Gao | Chao Shen | Lin Tao | Xiaohong Pan | Binghai Lou | Guocheng Fan | Jinfeng Hu
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Rapid Detection Method for Citrus Huanglongbing Based on an Improved Convolutional Neural Network with Fourier Transform Infrared Spectroscopy Fourier transform infrared spectroscopy ; Fourier transform ; Infrared ; Convolutional neural network ; Fourier transform spectroscopy ; Spectroscopy ; Biological system ; Computer science ; Artificial intelligence ; Pattern recognition (psychology) ; Mathematics ; Physics ; Optics ; Biology ; Astronomy ; Mathematical analysis Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis
Authors: Nan wang | Zhiyi Xia | Yiming Li | Shi‐Yang Tang | Zhuorong Fan | Xi Fang | Haoyi Tao | Xiaochen Cai | Guolin Ke | Linfeng Zhang | Yanhui Hong
Source: ArXiv.org (preprint, 2025-08-22)
Field: Materials Science — Materials Chemistry

UniEM-3M: A Universal Electron Micrograph Dataset for Microstructural Segmentation and Generation Quantitative microstructural characterization is fundamental to materials science, where electron micrograph (EM) provides indispensable high-resolution insights. However, progress in deep learning-based EM characterization has been hampered by the scarcity of large-scale, diverse, and expert-annotated datasets, due to acquisition costs, privacy concerns, and annotation complexity. To address this issue, we introduce UniEM-3M, the first large-scale and multimodal EM dataset for instance-level understanding. It comprises 5,091 high-resolution EMs, about 3 million instance segmentation labels, and image-level attribute-disentangled textual descriptions, a subset of which will be made publicly available. Furthermore, we are also releasing a text-to-image diffusion model trained on the entire collection to serve as both a powerful data augmentation tool and a proxy for the complete data distribution. To establish a rigorous benchmark, we evaluate various representative instance segmentation methods on the complete UniEM-3M and present UniEM-Net as a strong baseline model. Quantitative experiments demonstrate that this flow-based model outperforms other advanced methods on this challenging benchmark. Our multifaceted release of a partial dataset, a generative model, and a comprehensive benchmark -- available at huggingface -- will significantly accelerate progress in automated materials analysis. Segmentation ; Benchmark (surveying) ; Pattern recognition (psychology) ; Baseline (sea) ; Image segmentation ; Generative grammar ; Generative model Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Electron and X-Ray Spectroscopy Techniques
Authors: Dongxiao Chen | Philippe Sautet
Source: Angewandte Chemie (article, 2025-11-04)
Field: Materials Science — Materials Chemistry

Atomistic Landscape of Pt Nanoparticles via Machine Learning: How Size Effect and Hydrogen Adsorption Govern Structural Ensembles and Catalytic Activity Abstract Understanding the atomistic structure and fluxionality of Pt nanoparticles under reactive conditions is essential for rational design of effective catalysts, yet their structural complexity presents a great challenge. In this work, we combine grand canonical global optimization methods and machine learning potentials to explore the atomistic landscape of nanometer‐sized (1∼2 nm) Pt nanoparticles under a pressure of hydrogen, resulting in a comprehensive library of Pt x H y nanoparticles with over one million low‐energy metastable structures. We found that hydrogen adsorption drives a size‐dependent transformation from an amorphous to a crystalline structure, leading to sharp phase transitions for smaller nanoparticles and smooth transformations for larger ones. This behavior is governed by a competition between distinct core configurations, as well as the formation of rigid and fluxional local domains, where stability is dictated by specific surface motifs at low H coverage and by the crystalline core at high H coverage. By applying this structural library for reactivity modeling of methane dehydrogenation and ethylene hydrogenation, we show a marked discrepancy between the abundance of a surface site and its catalytic contribution, indicating that the active sites are rare, structurally distinct motifs, not the most common sites. Dehydrogenation ; Nanoparticle ; Catalysis ; Metastability ; Amorphous solid ; Molecular dynamics ; Adsorption ; Rational design Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalysis and Oxidation Reactions