jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shih-Lin Lin
Source: IEEE Transactions on Components Packaging and Manufacturing Technology (article, 2026-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Physics-Informed Modeling of Coupled Electro–Thermo–Mechanical Degradation in ADAS Interconnects Under Self-Heating and Bias Temperature Instability Reliability degradation in nanoscale automotive interconnects is increasingly governed by coupled electrical, thermal, and mechanical effects. Here, a physics-informed electro– thermo–mechanical framework is developed to predict the lifetime of 5-nm ADAS interconnects subjected to electromigration (EM), bias temperature instability (BTI), and vibration-induced fatigue. The model extends the classical Black and Basquin relations to capture concurrent electrothermal and mechanical degradation mechanisms under self-heating and bias compensation. Analytical results reveal that thermal–BTI coupling accelerates EM failure by approximately seventeenfold compared with the nominal baseline. A competing-failure formulation is further introduced to quantify the lifetime trade-off between electrical and mechanical degradation, enabling the determination of design envelopes that satisfy 20-year automotive reliability targets. Finally, a physics-informed neural network (PINN) is trained to extrapolate beyond the simulated stress range, embedding the governing EM– vibration equations as soft constraints. This hybrid physics–AI framework offers a transferable approach for predicting reliability in nanoscale automotive and electronic materials. Degradation (telecommunications) ; Instability ; Temperature measurement ; Stability (learning theory) ; Work (physics) ; Transient (computer programming) ; Electronic packaging Copper Interconnects and Reliability ; Electronic Packaging and Soldering Technologies ; Semiconductor materials and devices
Authors: Oga Hayashi | Naoomi Yamada
Source: Langmuir (article, 2026-03-04)
Field: Materials Science — Materials Chemistry

Interpretable Machine Learning Uncovers the Dominance of Oxygen Partial Pressure and Crystallinity in Mo-Doped In <sub>2</sub> O <sub>3</sub> Thin Film Growth Data-driven approaches have accelerated materials discovery, yet they often remain "black boxes" that prioritize performance over physical understanding. To bridge the gap between statistical correlation and physical causality, this study establishes an interpretable machine learning (IML) framework applied to the sputter deposition of Mo-doped In<sub>2</sub>O<sub>3</sub> thin films. Unlike conventional predictive models, our approach uses XGBoost regression combined with feature-importance analysis to quantitatively decouple the entangled effects of deposition parameters. Crucially, the model autonomously discovered─without explicit prior knowledge─that the carrier density is governed by oxygen partial pressure (<i>P</i><sub>O<sub>2</sub></sub>), while electron mobility is driven by crystallinity depending on the trade-off effects between adatom diffusion and high-energy particle bombardment. We experimentally validated these ML-derived hypotheses, identifying that the <i>P</i><sub>O<sub>2</sub></sub> dependence stems from defect compensation by interstitial oxygen rather than simple oxygen vacancies and that the mobility peak corresponds to optimal crystallinity. This work demonstrates that IML can effectively "rediscover" governing physical laws from small experimental data sets, offering a scalable strategy to elucidate growth mechanisms in functional materials. Crystallinity ; Thin film ; Partial pressure ; Oxygen ; Diffusion ; Sputtering ; Work (physics) ; Deposition (geology) Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Ferroelectric and Negative Capacitance Devices
Authors: Oga Hayashi | Naoomi Yamada
Source: Figshare (article, 2026-03-04)
Field: Materials Science — Materials Chemistry

InterpretableMachine Learning Uncovers the Dominanceof Oxygen Partial Pressure and Crystallinity in Mo-Doped In<sub>2</sub>O<sub>3</sub> Thin Film Growth Data-driven approaches have accelerated materials discovery, yet they often remain “black boxes” that prioritize performance over physical understanding. To bridge the gap between statistical correlation and physical causality, this study establishes an interpretable machine learning (IML) framework applied to the sputter deposition of Mo-doped In<sub>2</sub>O<sub>3</sub> thin films. Unlike conventional predictive models, our approach uses XGBoost regression combined with feature-importance analysis to quantitatively decouple the entangled effects of deposition parameters. Crucially, the model autonomously discoveredwithout explicit prior knowledgethat the carrier density is governed by oxygen partial pressure (<i>P</i><sub>O<sub>2</sub></sub>), while electron mobility is driven by crystallinity depending on the trade-off effects between adatom diffusion and high-energy particle bombardment. We experimentally validated these ML-derived hypotheses, identifying that the <i>P</i><sub>O<sub>2</sub></sub> dependence stems from defect compensation by interstitial oxygen rather than simple oxygen vacancies and that the mobility peak corresponds to optimal crystallinity. This work demonstrates that IML can effectively “rediscover” governing physical laws from small experimental data sets, offering a scalable strategy to elucidate growth mechanisms in functional materials. Crystallinity ; Partial pressure ; Thin film ; Oxygen ; Diffusion ; Sputtering ; Support vector machine ; Deposition (geology) Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Thin-Film Transistor Technologies
Authors: Hongguang Guo | Ping Chen | Feiyun Huang | Li Luo | Jingquan Wang | Zheng Min
Source: Research Square (preprint, 2026-02-16)
Field: Materials Science — Materials Chemistry

Autonomous nanocomposite electrochemical sensing of antibiotics across aquatic ecosystems Electrochemistry ; Nanocomposite ; Electrochemical gas sensor ; Aquatic ecosystem ; Graphene ; Electrode ; Oxide MXene and MAX Phase Materials ; Pharmaceutical and Antibiotic Environmental Impacts ; Advanced biosensing and bioanalysis techniques
Authors: Kousuke Nakano | Stefano Battaglia | Jürg Hutter
Source: The Journal of Chemical Physics (article, 2026-03-27)
Field: Materials Science — Materials Chemistry

Fast evaluation of unbiased atomic forces in <i>ab initio</i> variational Monte Carlo via the Lagrangian technique Ab initio quantum Monte Carlo (QMC) methods are state-of-the-art electronic structure calculations based on highly parallelizable stochastic frameworks for accurate solutions of the many-body Schrödinger equation, suitable for modern many-core supercomputer architectures. Despite its potential, one of the major drawbacks that still hinders QMC applications, especially when targeting dynamical properties of large systems or extensive datasets, is the lack of an affordable method to compute atomic forces that are consistent with the corresponding potential energy surfaces (PESs), also known as unbiased atomic forces. Recently, one of the authors in the present paper proposed a way to obtain unbiased forces with the Jastrow-correlated Slater determinant Ansatz, where the determinant part is frozen to the values obtained by a mean-field method, such as density functional theory [K. Nakano, M. Casula, and G. Tenti, Phys. Rev. B 109, 205151 (2024)]. However, the proposed method has a significant drawback for its applications: for a system with N nuclei, one requires 6N additional density functional theory (DFT) calculations to get unbiased forces, which is not negligible as the system size increases. This paper presents a way to replace the 6N DFT calculations with a single coupled-perturbed Kohn-Sham calculation, following the so-called Lagrangian technique established in quantum chemistry. This improves the computational cost and scalability of the method. We also demonstrate that the developed unbiased variational Monte Carlo (VMC) force calculation improves not only the consistency with PESs but also its accuracy, by investigating three molecules from the rMD17 benchmark set, and comparing the unbiased VMC forces with those obtained by the coupled-cluster singles and doubles with perturbative triples [CCSD(T)] calculations. We found that the bare VMC forces are biased from the CCSD(T) ones, while the unbiased ones give values closer to those of the CCSD(T) ones. Our benchmark test also reveals that the unbiased VMC forces yield very consistent values with hybrid and meta generalized gradient approximations (e.g., ωB97X-D3BJ and ωB97M-D3BJ), but do not necessarily yield values that are very close to those of CCSD(T). Our finding paves the way to generate machine learning interatomic potentials based on VMC forces more efficiently and accurately. Quantum Monte Carlo ; Monte Carlo method ; Scalability ; Density functional theory ; Benchmark (surveying) ; Consistency (knowledge bases) ; Ab initio Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Advanced Physical and Chemical Molecular Interactions
Authors: Huizhen Wang | Jia zhang | Yueyun Feng | Benchao Zhu | Lijun Diao
Source: Engineering Applications of Artificial Intelligence (article, 2026-03-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Physics-informed neural networks for safe operating boundary prediction of permanent magnet synchronous motors under thermal demagnetization risk Probabilistic logic ; Artificial neural network ; Demagnetizing field ; Nonlinear system ; Magnet ; Coupling (piping) ; Feed forward Magnetic Properties and Applications ; Electric Motor Design and Analysis ; Machine Fault Diagnosis Techniques
Authors: Arthur Souesme | R Guinebretiere | O. Castelnau | Alexandre Boulle
Source: Machine Learning Science and Technology (article, 2026-03-23)
Field: Materials Science — Materials Chemistry

High-throughput determination of crystallite size and microstrain from x-ray diffraction data with deep neural networks Abstract The development of high-throughput x-ray diffraction (XRD) techniques presents significant challenges to traditional data analysis frameworks. The present work demonstrates that deep convolutional neural networks (CNNs) can reliably quantify key microstructural parameters of polycrystalline materials, specifically crystallite size and heterogeneous strain (microstrain) extracted from powder XRD patterns, within milliseconds. A CNN was trained using synthetic data generated with a dedicated code based on the GSAS-II software. We employ the example of monoclinic ZrO 2 to critically examine the theoretical performances of the CNN. The accuracy of the predictions was systematically investigated within a crystallite size range of 5–1000 nm and a microstrain range of 0.05%–2%. In this context, the role of the resolution of the diffractometer and the XRD peak profile shape is discussed in details. For instance, on a low-resolution laboratory diffractometer it is possible to determine the crystallite size within a 5–100 nm range with 99.7% accuracy in the absence of microstrain, and 97.3% accuracy when microstrains are present. Higher accuracies are obtained with a high-resolution diffractometer and over an extended range of size and microstrain. The potential of the CNN is demonstrated by the analysis of the crystallite size and microstrains in MgAl 2 O 4 formed by a solid-state reaction between MgO and Al 2 O 3 using in situ XRD at 1200 °C performed at the ESRF. The CNN can determine the crystallite size and microstrain in 0.004 s per diagram with values that deviate by ∼7% and ∼8% from a conventional Rietveld refinement, respectively, which is contained within the statistical uncertainties of these parameters. We further demonstrate that the same training dataset can be used for other regression problems, for instance phase fraction quantification, with no additional computational cost and only minor modifications to the network architecture. This work paves the way for real-time data analysis at synchrotron facilities. Crystallite ; Diffractometer ; Diffraction ; Monoclinic crystal system ; Rietveld refinement ; Range (aeronautics) ; Powder Diffractometer ; X-ray crystallography Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; High-pressure geophysics and materials
Authors: Rafael Baságlia Rosante | Ricardo F. Lancelotti | Edgar Dutra Zanotto
Source: Scripta Materialia (article, 2026-01-16)
Field: Materials Science — Ceramics and Composites

JeZCA and JeZiCA – Powerful predictors of glass-forming ability Liquidus ; Viscosity ; Stoichiometry ; Crystallization ; Jump ; Simple (philosophy) ; Energy (signal processing) Glass properties and applications ; Catalysis and Oxidation Reactions ; Thermal and Kinetic Analysis
Authors: Fabrizio Camerin | Susana Marín-Aguilar | Anna Stradner | Peter Schurtenberger | Emanuela Zaccarelli
Source: arXiv (Cornell University) (preprint, 2026-03-19)
Field: Chemistry — Physical and Theoretical Chemistry

Deciphering Molecular Charge Anisotropy: the Case of Antibody Solutions Electrostatic interactions fundamentally govern the structure, stability, and dynamics of charged (bio)matter, yet the impact of heterogeneous and anisotropic charge distributions on the behavior of protein solutions remains elusive. Here, we introduce a versatile multiscale framework that directly connects molecular-level electrostatics to collective properties via a colloid-inspired coarse-grained modeling combined with neural network-assisted optimization. Using monoclonal antibodies as model system, our inverse design approach identifies charge patterns capable of reliably reproducing experimental structure factors, osmotic compressibility and collective diffusion coefficients in a wide region of protein concentrations. Close inspection of our data further uncovers how specific physical features and spatial arrangements of localized charge patches significantly influence the solution structure. This transferable strategy provides a predictive pathway to decode and control charge-driven interactions in complex biomolecules and, more generally, in heterogeneously-charged soft matter systems, with immediate relevance to protein formulation and biomaterials engineering. Electrostatics ; Charge (physics) ; Static electricity ; Biomolecule ; Molecular biophysics ; Inverse ; Compressibility ; Diffusion ; Monoclonal antibody Electrostatics and Colloid Interactions ; Pickering emulsions and particle stabilization ; Nanopore and Nanochannel Transport Studies
Authors: Amrendra Kumar | Muthu Vallinayagam | G. Tejaswini | Anjana E Sudheer | Rajeev Dutt | Matthias Zschornak | D. Murali | Chinnathambi Kamal
Source: Physical Chemistry Chemical Physics (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Energetic, geometric, electronic and magnetic properties of transition-metal-substituted binary CAs <sub>3</sub> Dirac monolayers Two-dimensional magnetic materials have recently emerged as promising candidates for spintronics, optoelectronics, and next-generation sensing technologies. Magnetic doping of Dirac semimetals breaks time-reversal symmetry, thereby inducing anomalous transport phenomena. Using density functional theory (DFT) calculations, we systematically investigate the stability, structural, electronic, and magnetic properties of 3d transition-metal (TM)-substituted CAs<sub>3</sub> Dirac monolayers, considering both single (1.4%) and double (2.8%) substitutional doping at C- and As-sites. The resulting magnetic properties strongly depend on the substitutional site and the filling of the d-orbitals of TM atoms. Mn substitution yields the maximum magnetic moment of 4<i>µ</i><sub>B</sub> per cell. The results reveal that the substitution of V, Cr, Mn, and Fe atoms induces a finite magnetic moment in the pristine CAs<sub>3</sub>. The substitution of TM atoms is found to break the inversion symmetry and reduce the lattice symmetry, resulting in band splitting and the opening of a finite gap at the Dirac point. The calculated formation energies under different chemical potential limits (C- and As-rich or -poor) strongly influence the feasibility of TM doping in CAs<sub>3</sub>. Under C-poor conditions, doping at the As-site is energetically preferred, whereas C-site substitution becomes favorable under C-rich and As-poor conditions for single TM substitution. The magnetic behavior of double TM (Cr, Co) substitution exhibits either magnetic or nonmagnetic behavior depending on whether the dopants occupy near or far sites. To predict formation energies, we develop a tree-based machine learning model, random forest, for formation energy prediction using atomic descriptors. The coefficient of determination <i>R</i><sup>2</sup> value is found to be 0.98 for the testing data. The electrical conductivity of the doped systems is calculated using Boltzmann transport theory within the constant relaxation time approximation. Monolayer ; Dirac (video compression format) ; Transition metal ; Semimetal ; Binary number ; Doping ; Metal 2D Materials and Applications ; Topological Materials and Phenomena ; Graphene research and applications
Authors: Xiaoxuan Ma | Xiaolan Duan | Ying Qu | Ying Gao | Xiaopeng Wang | Kaixing Ru | Mengyuan Zhang | Qiang Niu | Pengfei Zhang
Source: Chinese Chemical Letters (article, 2026-04-01)
Field: Materials Science — Materials Chemistry

Accelerating the discovery of high-entropy perovskite oxides by machine learning Perovskite (structure) ; Deep learning Machine Learning in Materials Science ; High Entropy Alloys Studies ; Inorganic Chemistry and Materials
Authors: Yuqin Xiao | Yuxin Yan | M. Sivakumar | Haitao Zhao | Tao Wu | Cheng Heng Pang
Source: International Journal of Mechanical Sciences (article, 2026-02-04)
Field: Materials Science — Materials Chemistry

Deep-learning-driven modeling of oxygen influence on graphene oxide elastic modulus Elastic modulus ; Graphene ; Oxide ; Bulk modulus ; Modulus ; Molecular dynamics ; Stiffness ; Dynamic mechanical analysis ; Work (physics) Graphene research and applications ; Graphene and Nanomaterials Applications ; Boron and Carbon Nanomaterials Research
Authors: Weibin Liang | Sisi Zheng | R. Andrew Shalliker | Christian J. Doonan | Feng Li
Source: Small (article, 2026-02-17)
Field: Materials Science — Materials Chemistry

Sequential Optimization of Multivariate Metal–Organic Framework Based Biocatalysis Efficient biocascades require integrated optimization of both biocatalysts and reaction conditions. In the present study, we employ a Latin hypercube sampling-coupled Bayesian optimization (LHS-BO) workflow to sequentially optimizes multivariate Zr-based E-MOF (E-MOF = enzyme@metal-organic framework biocomposite) design and the downstream glucose oxidase-horseradish peroxidase (GOx-HRP) biocascade. Enzymatic assays, attenuated total reflectance Fourier transform infrared spectroscopic (ATR-FTIR) and ultraviolet-visible (UV-Vis) spectroscopic analyses confirm that the optimized multivariate E-MOFs, ZG67, and ZH16, stabilize GOx and HRP in bioactive conformations. ZG67 and ZH16 achieve high encapsulation efficiency (92.2% ± 0.7% and 90.2% ± 1.3%), retained activity (87.1% ± 2.2% and 102.9% ± 14.9%), and enhanced stability under thermal (51.3% ± 1.4% and 45.9% ± 8.7%) and solvent treatments (>80% and ≈60% residual activity, respectively, in ethanol or acetonitrile). The optimal GOx-HRP cascade condition identified by LHS-BO (R49) achieves over 95% of the theoretical maximum 2,3-diaminophenazine (DAP) production rate predicted by the microkinetic model. The strong agreement amongst the experimental data, machine learning modeling, and kinetic modeling validates the robustness of this sequential E-MOF optimization framework. This generalized LHS-BO strategy provides a powerful tool for rational E-MOF and multi-enzyme cascade design, advancing biocatalysis and reaction engineering. Robustness (evolution) ; Biocatalysis ; Cascade ; Multivariate statistics ; Chemometrics ; Fourier transform ; Workflow ; Multi-objective optimization Advanced Nanomaterials in Catalysis ; Metal-Organic Frameworks: Synthesis and Applications ; Enzyme Catalysis and Immobilization
Authors: Sicui Wang | Zhenyu Li | Wenlan Li
Source: Journal of Spectroscopy (article, 2026-01-01)
Field: Chemistry — Analytical Chemistry

A Quality Identification Method for Chinese Herbal Slices Integrating Fuzzy Reasoning–Based Open‐Set Recognition and Support Vector Machine In recent years, quality evaluation of Chinese Herbal Slices (CHS) has primarily relied on morphological traits, chemical composition analysis, and bioactivity assessments, with nondestructive techniques, such as near‐infrared (NIR) spectroscopy and X‐ray imaging, remaining underutilized. Meanwhile, open‐set recognition (OSR), a research hotspot in pattern recognition over the past decade, enables models to not only classify test samples from known classes in the training set but also effectively reject unknown classes. Here, collected CHS classes are defined as known classes, while uncollected classes are treated as unknown classes excluded from the training set. Simultaneously, quality assessment of known CHS classes is required. Existing OSR algorithms, predominantly based on support vector machine (SVM) and deep learning frameworks, are mainly applied to image recognition, with limited exploration in spectral analysis. This study proposes a novel method integrating a fuzzy inference–based OSR classifier with SVM for CHS quality evaluation. First, NIR spectra of CHS were acquired using a FLAMENIR spectrometer. Spectral noise was reduced via Gaussian smoothing, followed by dimensionality reduction to 5 features using principal component analysis (PCA). Subsequently, a hierarchical classifier was constructed: The OSR classifier distinguishes known and unknown classes, while SVM evaluates the quality of known‐class CHS. Across four scenarios on the CHS dataset containing 1700 samples, the proposed method outperforms OSR algorithms and state‐of‐the‐art 1D deep learning algorithms in terms of overall recognition accuracy (ORA), F1, and kappa. These algorithms include weighted‐SVDD, open‐set version of nearest neighbor classifier (OSNN), metric learning, fuzzy reasoning (FR), 1D‐CNN, and 1D‐ResNet integrated with the fusion of collective and extreme information (FCEI). Support vector machine ; Pattern recognition (psychology) ; Classifier (UML) ; Fuzzy logic ; Principal component analysis ; Margin classifier ; Dimensionality reduction ; Curse of dimensionality Spectroscopy and Chemometric Analyses ; Traditional Chinese Medicine Analysis ; Metabolomics and Mass Spectrometry Studies