jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Nicard, Cyril | Rébillat, Marc | Devos, Olivier | El May, Mohamed | Mechbal, Nazih
Source: SPIRE - Sciences Po Institutional REpository (article, 2025-09-01)
Field: Materials Science — Metals and Alloys

Study of crack initiation and propagation from an electrochemically generated corrosion damage monitored by ultrasonic technique Corrosion remains a critical issue in various industrial sectors, often being underestimated until significant damage occurs and heavy maintenance operation has to be carried out. Early detection and intervention can drastically extend asset life and reduce maintenance costs. Non-destructive testing (NDT) methods, widely used in the aeronautical industry [1], have proven effective in detecting structural damage, though they typically require human involvement. Recent advances in ultrasonic techniques, particularly the use of piezoelectric transducer (PZT) networks, offer promising alternatives to shift towards a condition-based maintenance approach. Building upon earlier work [2] that developed an ultrasonic Lamb wave technique for detecting electrochemically induced localized corrosion pit, this study expands the approach by integrating both electrochemical and mechanical analyses of crack initiation and propagation on Aluminum 2024 alloy. In our work, PZT discs generate and receive ultrasonic signals to monitor the evolution of a single corrosion and mechanical fatigue damage. This talk presents the results of our experiments, demonstrating how the combination of electrochemical control and ultrasonic monitoring provides a reliable method for tracking corrosion and crack growth. Embedding these sensors directly into strategic point of structures for continuous monitoring will reduce human intervention/error while generating data that could train machine learning algorithms for predictive maintenance strategies in the future. [1] S. Benavides, Corrosion in the aerospace industry, 2009. [2] C. Nicard, M. Rébillat, O. Devos, M. El May, F. Letellier, S. Dubent, M. Thomachot, M. Fournier, P. Masse, N. Mechbal, In-situ monitoring of µm-sized electrochemically generated corrosion pits using Lamb waves managed by a sparse array of piezoelectric transducers, Ultrasonics, Volume 147, 2025, 107527, ISSN 0041-624X, https://doi.org/10.1016/j.ultras.2024.107527 Ultrasonic sensor ; Corrosion ; Ultrasonic testing ; Welding ; Fracture mechanics Hydrogen embrittlement and corrosion behaviors in metals ; Erosion and Abrasive Machining ; Surface Treatment and Residual Stress
Authors: Mingfeng Liu | Jian-Tao Wang | Liu, Peitao | Qiang Wang | Zeng Liu | Yan Sun | Xing-Qiu Chen
Source: ArXiv.org (preprint, 2025-06-26)
Field: Materials Science — Materials Chemistry

Diverse polymorphs and phase transitions in van der Waals In$_2$Se$_3$ Van der Waals In$_2$Se$_3$ has garnered significant attention due to its unique properties and wide applications associated with its rich polymorphs and polymorphic phase transitions. Despite extensive studies, the vast complex polymorphic phase space remains largely unexplored, and the underlying microscopic mechanism for their phase transformations remains elusive. Here, we develop a highly accurate, efficient, and reliable machine-learning potential (MLP), which not only facilitates accurate exploration of the intricate potential energy surface (PES), but also enables us to conduct large-scale molecular dynamics (MD) simulations with first-principles accuracy. We identify the accurate structure of the $β''$ polymorph and uncover several previously unreported $β'$ polymorph variants exhibiting dynamic stability and competing energies, which are elucidated by characteristic flat imaginary phonon bands and the distinctive Mexican-hat-like PES in the $β$ polymorph. Through the MLP-accelerated MD simulations, we directly observe the polymorphic phase transformations among the $α$, $β$, $β'$, and $β''$ polymorphs under varying temperature and pressure conditions, and build for the first time an ab initio temperature-pressure phase diagram, showing good agreement with experiments. Furthermore, our MD simulations reveal a novel strain-induced reversible phase transition between the $β'$ and $β''$ polymorphs. This work not only unveils diverse polymorphs in van der Waals In$_2$Se$_3$, but also provides crucial atomic insights into their phase transitions, opening new avenues for the design of novel functional electronic devices. van der Waals force ; Phase transition ; Molecular dynamics ; Phase (matter) ; Ab initio ; Polymorphism (computer science) ; Van der Waals strain ; Van der Waals surface ; Ab initio quantum chemistry methods Solid-state spectroscopy and crystallography ; Chalcogenide Semiconductor Thin Films ; Crystal Structures and Properties
Authors: Wenqian Tu | Run Lv | Ding‐Fu Shao | Yuping Sun | W. J. Lu
Source: Physical review. B./Physical review. B (article, 2025-11-25)
Field: Materials Science — Materials Chemistry

Phonon anharmonicity driven charge density wave transition and ultrafast dynamics in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mn>1</mml:mn> <mml:mrow> <mml:mi>T</mml:mi> </mml:mrow> <mml:mo>−</mml:mo> <mml:mrow> <mml:mi>Ta</mml:mi> <mml:msub> <mml:mi mathvariant="normal">S</mml:mi> <mml:mn>2</mml:mn> </mml:msub> </mml:mrow> <mml:mo>/</mml:mo> <mml:mrow> <mml:mi>TaS</mml:mi> <mml:msub> <mml:mi mathvariant="normal">e</mml:mi> <mml:mn>2</mml:mn> </mml:msub> </mml:mrow> </mml:math> Charge density wave (CDW), a symmetry-breaking collective phenomenon in condensed matter systems, exhibits periodic modulations of electron density coupled with lattice distortions, where the lattice plays a critical role via electron-phonon coupling. In transition metal dichalcogenides (TMDs) $1T\text{\ensuremath{-}}\mathrm{Ta}{\mathrm{S}}_{2}/\mathrm{TaS}{\mathrm{e}}_{2}$, experiments reveal rich temperature- and pressure-dependent CDW phase behaviors, along with metastable CDW states induced by ultrafast optical excitation. Nevertheless, the underlying mechanisms governing thermal/pressure-driven transitions and particularly the microscopic evolution of CDW phases remain incompletely understood. Here, we perform first-principles anharmonic phonon calculations and machine-learning force-field molecular dynamics at finite temperatures/pressures to investigate the CDW transitions in $1T\text{\ensuremath{-}}\mathrm{Ta}{\mathrm{S}}_{2}/\mathrm{TaS}{\mathrm{e}}_{2}$. The calculated CDW transition temperature ${T}_{\mathrm{CDW}}$ and critical pressure ${P}_{\mathrm{c}}$ are in quantitative agreement with experimental values. Our results demonstrate that the melting of CDW originates from phonon anharmonicity, with ionic fluctuations dominating the transition dynamics. We observe the microscopic evolution of CDW under varying temperature/pressure, revealing an ultrafast nucleation process of CDW (\ensuremath{\sim}3 ps). Our results emphasize the essential role of phonon anharmonicity in elucidating CDW transition mechanisms underlying, and advance fundamental understanding of CDW-related phenomena in TMDs. Charge density wave ; Anharmonicity ; Metastability ; Phonon ; Nucleation ; Phase transition ; Lattice (music) ; Peierls transition 2D Materials and Applications ; Organic and Molecular Conductors Research ; Inorganic Chemistry and Materials
Authors: S Chen | Yan Chen | Zutao Zhu | Zihao Wang | Xiangping Zhang | Zhongmei Li | Wenli Du | Chenglin Chang | Weifeng Shen
Source: AIChE Journal (article, 2025-12-08)
Field: Chemistry — Inorganic Chemistry

<scp>CIF2MOFNet</scp> : A deep learning model with multi‐dimensional coordinate features to accelerate <scp>MOF</scp> screening for <scp> CO <sub>2</sub> </scp> capture Abstract Metal–organic frameworks (MOFs) are promising adsorbents for carbon capture, while their structural complexity poses challenges for rapid screening. This study develops a novel deep learning model, CIF2MOFNet, which predicts CO 2 working capacity and CO 2 /N 2 selectivity of MOFs directly from their crystallographic information files (CIFs). In addition to the 2D structural projections used in previous methods, CIF2MOFNet incorporates an innovative 1D representation derived from atomic coordinates. This hybrid strategy effectively captures crucial spatial distributions and elemental compositions, which have often been overlooked in 2D simplifications. Thus, CIF2MOFNet achieves a significantly higher predictive accuracy, while circumventing the computational complexity associated with full 3D structural representations. Trained on 342,489 MOFs, CIF2MOFNet efficiently screens 7426 experimentally synthesized MOFs and identifies 321 high‐performance candidates, reducing computation time per MOF from 7393 s to just 0.021 s while maintaining strong predictive performance. Structural analysis of top candidates highlights that key adsorption‐related characteristics, including optimal pore size and functional group types, are linked to superior CO 2 adsorption performance, demonstrating strong potential for accelerating MOF discovery and guiding rational MOF design for efficient CO 2 capture. As an end‐to‐end model using CIF directly, CIF2MOFNet offers a universal strategy for rapid, high‐throughput screening across any potential nanoporous material database with CIF data, providing valuable insights for the design of next‐generation adsorbents. Nanoporous ; Deep learning ; Key (lock) ; Computation ; Representation (politics) ; Rational design ; Computational complexity theory Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies ; Covalent Organic Framework Applications
Authors: Ewan R. S. Wallace (21602610) | Nathan C. Frey (1316181) | Joshua A. Rackers (1587952)
Source: Figshare (article, 2025-06-25)
Field: Materials Science — Materials Chemistry

Strain ProblemsGot You in a Twist? Try StrainRelief:A Quantum-Accurate Tool for Ligand Strain Calculations Ligand strain energy, the energy difference between the bound and unbound conformations of a ligand, is an important component of structure-based small molecule drug design. A large majority of observed ligands in protein-small molecule cocrystal structures bind in low-strain conformations, making strain energy a useful filter for structure-based drug design. In this work we present a tool for calculating ligand strain with a high accuracy. StrainRelief uses a MACE neural network potential (NNP), trained on a large database of density functional theory (DFT) calculations to estimate ligand strain of neutral molecules with quantum accuracy. We show that this tool estimates strain energy differences relative to DFT to within 1.4 kcal/mol, more accurately than alternative NNPs. These results highlight the utility of NNPs in drug discovery, and provide a useful tool for drug discovery teams. Strain (injury) ; Density functional theory ; Ligand (biochemistry) ; Molecule ; Cocrystal ; Strain energy ; Strain energy density function ; Work (physics) Machine Learning in Materials Science ; Crystallography and molecular interactions ; Protein Structure and Dynamics
Authors: Caio Lima Firme | Elvis S. Boes
Source: Canadian Journal of Chemistry (article, 2025-05-16)
Field: Materials Science — Materials Chemistry

(Coulomb) Local potential energy density–supramolecular energy (LPED–SME) machine learning prediction—a web application to obtain the local SME from simple inputs We developed a freely accessible web application ( https://clfirme.pythonanywhere.com/ ) that uses supervised machine learning (ML) to predict (Coulomb) local potential energy density (LPED) for intermolecular and intramolecular interactions. The user-friendly interface accepts simple inputs: atomic charges of interacting atoms and interatomic distances, avoiding the complex calculations typically required by Quantum Theory of Atoms in Molecules topology. Our learning curves demonstrate model stability relative to dataset size, leveraging uncoded, smooth features with strong physical relationships to the target LPED. After testing six different ML models, we found that Regularized Gradient Boosting performed best, achieving excellent predictive capacity in both primary and secondary testing. This model has an average uncertainty estimate of 40.9%, with larger errors observed for highly negative LPED values. Chemistry ; Simple (philosophy) ; Energy (signal processing) ; Supramolecular chemistry ; Coulomb ; Electric potential energy ; Molecule ; Organic chemistry ; Quantum mechanics ; Physics Machine Learning in Materials Science
Authors: Tian Xia (139140) | Rui-Tian Ma (22338874) | Jia-Ying Li (316807) | Hui Liu (33624) | Hai-Bo Yi (570022)
Source: Figshare (article, 2025-09-30)
Field: Materials Science — Biomaterials

Nanoconfinementand Interface Effects on Calcium PhosphateAggregation within a 2D Nanochannel: Insights from Deep-Learning MolecularDynamics In this study, we investigated the hydration and aggregation dynamics of Ca<sup>2+</sup> and phosphate species, as well as the structural characteristics of calcium phosphate clusters, within a two-dimensional (2D) nanochannel using molecular dynamics simulations with a deep learning potential. Our findings show that ion dynamics are markedly enhanced under confinement, primarily due to accelerated water dynamics. Ion hydration within the 2D nanochannel is reduced as a result of layered water distribution and frequent water exchange around ions compared to the bulk phase solution, thereby facilitating coordination between Ca<sup>2+</sup> and phosphate species despite observed polarization effects. However, an increased energy barrier for association between Ca<sup>2+</sup> and phosphate species can slow their aggregation within the 2D nanochannel. Since protonated phosphate species exhibit a stronger preference for interfacial water layers than PO<sub>4</sub><sup>3–</sup>, fewer protons are present in cluster of Ca<sup>2+</sup> and phosphate species in the bulk-like region, which facilitates the association of Ca<sup>2+</sup> and phosphate species. The interfacial enrichment of protonated species can also promote the transformation of amorphous calcium phosphate (ACP) to hydroxyapatite. Our results presented here elucidate the influence of nanoconfinement and interfacial interactions on calcium phosphate aggregation within 2D nanochannels, offering valuable insights into biological and biomimetic mineralization processes. Phosphate ; Molecular dynamics ; Protonation ; Calcium ; Amorphous calcium phosphate ; Ion ; Biomineralization Calcium Carbonate Crystallization and Inhibition ; Nanopore and Nanochannel Transport Studies ; Ion-surface interactions and analysis
Authors: Shaohua He | Shaobing Yang | Yuanzhong Wang
Source: LWT (article, 2025-12-27)
Field: Chemistry — Analytical Chemistry

Rapid and non-destructive identification of Gastrodia elata Blume geographical origin by infrared spectroscopy combined with chemometrics Gastrodia elata Blume has high medicinal value and is also used as a cooking ingredient for daily health care. It is widely distributed, and identifying its geographical origin is important for guiding market supervision and meeting consumer needs. This study used near-infrared (NIR), attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy and a data fusion strategy to identify the origins of Gastrodia elata Blume. Initially, two unsupervised learning methods, PCA and t-SNE, were utilised to visualise and analyse Gastrodia elata Blume from seven origins. The findings showed that it was not possible to distinguish between samples from different origins using the visualisation of analytical outcomes. Afterwards, through the comparison of multiple preprocessing methods, the PLS-DA model established by NIR data using FD+SNV preprocessing, and the SVM model that was established by NIR+ATR-FTIR data using SD preprocessing, the accuracy reached 100% (training sets) and 98.41% (test sets), respectively. Furthermore, a residual convolutional neural network (ResNet) model was built using two-dimensional correlation spectroscopy (2DCOS) images, which obtained 100% accuracy using NIR and NIR+ATR-FTIR data, respectively. In contrast, the established ResNet model obtains a robust model without preprocessing steps, providing a rapid, nondestructive and accurate method for origins identification of Gastrodia elata Blume. • IR spectroscopy captured region-specific chemical signatures in G. elata Bl. • Compared the effects of different spectral preprocessing methods on the modeling results. • The ResNet model demonstrates outstanding performance in geographical origin identification. • High robustness and performance even when the sample is unbalanced. Gastrodia elata ; Chemometrics ; Pattern recognition (psychology) ; Preprocessor ; Hyperspectral imaging ; Spectroscopy ; Identification (biology) ; Near-infrared spectroscopy Spectroscopy and Chemometric Analyses ; Biological and pharmacological studies of plants ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Fernando Fajardo-Rojas (9080868) | Ryther Anderson (4725618) | Mingwei Li (1914862) | Remco Chang (11668965) | Diego A. Gómez-Gualdrón (1635055)
Source: Figshare (article, 2025-07-17)
Field: Chemistry — Inorganic Chemistry

Data-DrivenInsights on the Impact of Functionalizationon Metal–Organic Framework Free Energies Functionalization is poised to play a prominent role in MOF development as it could become the to-go strategy to bestow extant MOF with new properties and to control the MOF pore shape and size by modulating polymorph selection. Thus, to speed up MOF development through computational work, a better (predictive) understanding of how functionalization impacts MOF synthesizability is needed. Here, we use a data-driven approach where molecular dynamics simulations on 5000+ MOFs are used to shed light on how functionalization affects MOF free energy, as the latter has been largely tied to MOF synthesizability and polymorph selection. More consistently, in MOFs with higher void fractions, we find that functionalization generally reduces free energy, with entropy contributing significantly to this thermodynamic stabilization. Although with some functionalizations (–CF<sub>3</sub>, –F, –Br, –SH, and–OH), the role of entropy is more apparent than with others (–CN, –CH<sub>3</sub>, –NO<sub>2</sub>, and –NH<sub>3</sub>). Through uneven stabilization of polymorphs, we also find functionalization (more often with –Br, –CN, and –CF<sub>3</sub>) as capable of altering polymorph (topology) selection relative to original nonfunctionalized polymorphic families. However, no switch in polymorph stability occurred when the original (unfunctionalized) polymorphs were separated by more than 1.42 kJ/mol per MOF atom. We show that machine learning can predict the functionalization-induced free energy change of a parent MOF with a mean absolute error of 0.17 kJ/mol per atom, using only the physical properties of the parent MOF and the functional group as inputs. The ML-based SHAP analysis agrees with the human analysis on the functionalization of molecular mass and the hydrogen fraction of the parent MOF being among the factors that influence change in free energy the most. Finally, we present a publicly accessible dynamic interface to visualize and navigate the free-energy data, thereby encouraging the research community to engage with and utilize the data to help uncover new insights. Surface modification ; Extant taxon ; Entropy (arrow of time) ; Molecular dynamics ; Effect modification ; Conformational entropy Metal-Organic Frameworks: Synthesis and Applications ; Zeolite Catalysis and Synthesis ; Machine Learning in Materials Science
Authors: Furat, Orkun | Gogineni, Vinay Chakravarthi | Bindslev, Henrik | Nadimi, Esmaeil S.
Source: arXiv (Cornell University) (preprint, 2025-12-17)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Physics-informed Neural Operators for Predicting 3D Electromagnetic Fields Transformed by Metasurfaces Metasurfaces, typically realized as arrays of nanopillars, transform electromagnetic (EM) fields depending on their geometry and spatial arrangement. For solving the inverse problem of designing new metasurfaces that transform EM fields in a desirable manner, it is often necessary to explore large design spaces through full-wave simulations that can be computationally demanding. In this work, we demonstrate that neural operators, which are artificial neural network architectures designed to learn operators between function spaces, can effectively approximate the differential operators underlying Maxwell's equations, enabling their use as fast and accurate 3D surrogate models that can predict 3D EM fields transformed by metasurfaces. To calibrate neural operators, we generate synthetic training data consisting of 3D metasurface geometries together with their associated 3D EM fields obtained by numerically solving Maxwell's equations. Using the generated synthetic data, we train physics-informed neural operators to minimize physical inconsistencies of predicted EM fields by incorporating residuals that capture deviations from Maxwell's equations. We observe that a training dataset consisting of fewer than 5000 examples already suffices to achieve reasonable results. In particular, our experiments show that the resulting 3D surrogate model achieves high predictive performance across a wide range of metasurface geometries, including types of structures not encountered during training. Notably, it predicts diffraction efficiencies with relative errors of 3.9 % and provides a 67-fold speedup compared to conventional 3D simulations. Overall, once trained, our 3D surrogate model can rapidly predict EM fields for previously unseen metasurface geometries, which can facilitate efficient gradient-based design of nanostructured materials for EM wave control. Artificial neural network ; Range (aeronautics) ; Field (mathematics) ; Inverse problem ; Surrogate model ; Speedup ; Inverse ; Electromagnetic field Metamaterials and Metasurfaces Applications ; Acoustic Wave Phenomena Research ; Advanced Antenna and Metasurface Technologies