jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Murilo Neco Saraiva | Orlando Marbello Ospina | Lucas Konaka Nolasco | Renan Souza Cunha | Lucas Nunes Sales de Andrade | Sergio Ricardo Muniz | Cléber Renato Mendonça
Source: Optical Materials (article, 2026-01-23)
Field: Materials Science — Materials Chemistry

Predicting fs-laser-induced NV centers with PCA and neural networks Diamond hosts a variety of lattice defects, among which nitrogen-vacancy (NV) centers stand out due to their relevance in quantum photonics with optically addressable qubits. Yet, the complex laser–material interactions governing its formation are not fully understood, and the influence of laser parameters on NV generation still raises open questions. Here, we investigate the generation of NV centers using principal component analysis (PCA) and artificial neural networks (ANNs) as predictive tools based on femtosecond laser parameters. Experimental results from femtosecond laser micromachining of diamond provided the dataset for our analysis. We employed PCA to reduce data dimensionality and uncover dominant experimental trends, while a multilayer perceptron model was trained to predict NV center generation under simulated conditions. GridSearch optimization and Leave-One-Out cross-validation (LOOCV) ensured the best performance and robustness of the ANN. Our results reveal that NV center generation is directly proportional to laser peak fluence and inversely proportional to pulse duration and excitation wavelength. Notably, PCA and ANN modeling independently converged on consistent trends, reinforcing the reliability of the observed parameter–defect relationships. This convergence supports the development of predictive frameworks for controlled color center generation in diamond with greater precision. • This study applied PCA and artificial neural networks to predict NV center generation in diamond. • NV center formation increases with laser fluence and decreases with pulse duration and wavelength. • PCA and neural networks reached the same conclusions observed experimentally, confirming the reliability of the results. Artificial neural network ; Laser ; Principal component analysis ; Femtosecond ; Multilayer perceptron ; Fluence ; Photonics ; Perceptron Diamond and Carbon-based Materials Research ; Laser Material Processing Techniques ; Nonlinear Optical Materials Studies
Authors: Jiayun Wu | Yang Zha | Fanxiang Bu | Linlin Liu | Yirou Liang | Bangchao Xi | Fei He | Yi Wang | Pengxin Huang | Yunbo Li | Xiaotong Jiang | Lin Zhang | Guangyu Qiu
Source: Sensors and Actuators B Chemical (article, 2026-03-15)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Spectral multiplexing with optical-path-tailored interferometric sensing system for real-time differential phase detection Multiplexing ; Interferometry ; Sensitivity (control systems) ; Analyte ; Robustness (evolution) ; Signal processing ; Biosensor ; SIGNAL (programming language) Gold and Silver Nanoparticles Synthesis and Applications ; Photonic and Optical Devices ; Optical Polarization and Ellipsometry
Authors: Junjie Wu | Timothy J. Rupert
Source: Open MIND (preprint, 2026-01-28)
Field: Materials Science — Materials Chemistry

Extraction of a structural short-range order descriptor from nanobeam electron diffraction patterns using a transfer learning approach Amorphous solids exhibit structural short-range order despite lacking long-range crystalline order, with this structural descriptor found to be important for determining mechanical properties. Nanobeam electron diffraction offers a potential route for experimental characterization of structural short-range order, yet efforts to date have been primarily qualitative in nature. In this work, machine learning approaches based on transfer learning are used to enable quantitative analysis of nanobeam electron diffraction data from amorphous solids. A ResNet-18 model is trained on virtual diffraction patterns taken from different locations within simulated metallic glasses and amorphous grain boundary complexions in the Cu-Zr alloy system that were created with hybrid molecular dynamics and Monte Carlo simulations. The disorder parameter is found to be a superior target structural descriptor compared to traditional Voronoi indices for this task. The model achieves a low validation mean absolute error across diffraction patterns corresponding to different interaction volumes, demonstrating excellent performance and potential transferability. Testing was performed using other simulated nanobeam electron diffraction data as well as experimental nanobeam electron diffraction patterns, showing that the model can reliably capture spatial variations in local structural state. As a whole, this framework is able to overcome the challenges in the quantitative experimental characterization of structural short-range order, enabling improved characterization of amorphous solids and the exploration of structure-property relationships. Diffraction ; Amorphous solid ; Electron diffraction ; Characterization (materials science) ; Monte Carlo method ; Electron backscatter diffraction ; Reverse Monte Carlo Machine Learning in Materials Science ; Metallic Glasses and Amorphous Alloys ; Advanced Electron Microscopy Techniques and Applications
Authors: Jinchen Yang | JinTao Tan | Weiwei Shao | Jinmin Guo | Xiuli Zhu | Wei Wu | Xiaoliang Zhang | Jinlong Wang | Xiaochun Li
Source: Modelling and Simulation in Materials Science and Engineering (article, 2026-03-23)
Field: Materials Science — Materials Chemistry

Molecular dynamics study on the behavior of boron in tungsten Abstract We systematically investigated the behavior of boron (B) in tungsten (W) from both thermodynamic and kinetic perspectives using the machine-learning potential. Our results show that interstitial B tends to aggregate in W. In the presence of vacancy defects and self-interstitial atoms (SIAs), B preferentially forms clusters within vacancies, followed by binding with SIAs, exhibiting a strong anchoring effect. This study provides valuable insights for the selection and design of plasma-facing materials. Tungsten ; Boron ; Molecular dynamics ; Kinetic energy ; Vacancy defect Fusion materials and technologies ; Advanced materials and composites ; Boron and Carbon Nanomaterials Research
Authors: Wei Liu | Xiumin Chen | Kangming Wu | R. Q. Yu | Xinglong Fu | Ruijiao Jia | Yunmin Chen | Jichang Kang
Source: Computational Materials Science (article, 2026-02-23)
Field: Materials Science — Materials Chemistry

Deep potential molecular dynamics study of boron nanostructure formation in aluminum melts Boron ; Nanostructure ; Molecular dynamics ; Aluminium ; Cluster (spacecraft) ; Diffusion Boron and Carbon Nanomaterials Research ; nanoparticles nucleation surface interactions ; Microstructure and mechanical properties
Authors: Shaan Pakala | Aldair E. Gongora | Brian Giera | Evangelos E. Papalexakis
Source: Open MIND (preprint, 2026-02-11)
Field: Materials Science — Materials Chemistry

Tensor Methods: A Unified and Interpretable Approach for Material Design When designing new materials, it is often necessary to tailor the material design (with respect to its design parameters) to have some desired properties (e.g. Young's modulus). As the set of design parameters grow, the search space grows exponentially, making the actual synthesis and evaluation of all material combinations virtually impossible. Even using traditional computational methods such as Finite Element Analysis becomes too computationally heavy to search the design space. Recent methods use machine learning (ML) surrogate models to more efficiently determine optimal material designs; unfortunately, these methods often (i) are notoriously difficult to interpret and (ii) under perform when the training data comes from a non-uniform sampling of the design space. We suggest the use of tensor completion methods as an all-in-one approach for interpretability and predictions. We observe classical tensor methods are able to compete with traditional ML in predictions, with the added benefit of their interpretable tensor factors (which are given completely for free, as a result of the prediction). In our experiments, we are able to rediscover physical phenomena via the tensor factors, indicating that our predictions are aligned with the true underlying physics of the problem. This also means these tensor factors could be used by experimentalists to identify potentially novel patterns, given we are able to rediscover existing ones. We also study the effects of both types of surrogate models when we encounter training data from a non-uniform sampling of the design space. We observe more specialized tensor methods that can give better generalization in these non-uniforms sampling scenarios. We find the best generalization comes from a tensor model, which is able to improve upon the baseline ML methods by up to 5% on aggregate $R^2$, and halve the error in some out of distribution regions. Interpretability ; Tensor (intrinsic definition) ; Generalization ; Sampling (signal processing) ; Set (abstract data type) ; Tensor contraction ; Material Design Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Tensor decomposition and applications
Authors: Taichi KATO | Yoshinori Shiihara | Hideki Mori | Tomohito Tsuru | Daisuke MATSUNAKA
Source: Journal of the Society of Materials Science Japan (article, 2026-02-15)
Field: Materials Science — Materials Chemistry

A Fundamental Study on Machine Learning Potential Based on Atomic Energy Learning with EAM Potential Machine-learning-based molecular dynamics (ML-MD) has emerged as a promising approach to resolving the trade-off between accuracy and computational cost in atomic simulations. The most conventional method in ML-MD, total energy learning (TEL), trains models by matching the sum of predicted atomic energies to the total energy of the system. However, because calculating total energy requires information from all atoms in the system, TEL poses limitations for flexible data refinement. To address this issue, we have developed atomic energy learning (AEL), which enables atomic-level data selection by directly using each atom’s energy as training data. As a fundamental study, we compare AEL and TEL using datasets based on the Embedded Atom Method (EAM). The results demonstrate the effectiveness of selective data sampling in controlling model accuracy and reveal the advantage of AEL in evaluating atomic energies near defects through targeted data selection. Energy (signal processing) ; Atom (system on chip) ; Atomic energy ; Atomic model ; Experimental data ; Matching (statistics) ; Potential energy Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Advanced Materials Characterization Techniques
Authors: J. Richard Kyle | et al.
Source:  (article, 2026-01-27)
Field: Materials Science — Materials Chemistry

Supplemental Material: Growth and physical transport of molybdenite spherules in a Na-Al-F-rich melt, Cave Peak porphyry Mo deposit, Texas, USA X-ray diffraction spectra of aluminofluoride minerals, scanning electron microscopy, Re-Os geochronology, computed tomography scanning parameters, theoretical X-ray attenuation of relevant minerals, high resolution X-ray computed tomography animation of four attenuation groups, machine learning protocol and descriptive statistics of molybdenite grains, Stokes calculations of particle settling velocity, and supplemental references on methods and material properties.&lt;p&gt;&lt;/p&gt; Molybdenite ; Molybdenite ; Attenuation ; Attenuation ; Scanning electron microscope ; Scanning electron microscope ; Cave ; Cave ; Diffraction ; Diffraction X-ray Diffraction in Crystallography ; Nuclear materials and radiation effects ; Clay minerals and soil interactions
Authors: Shuang Shan | Longfei Guo | Chongyang Wang | Zhen Li | Xinjie Wu | Haibin Wei | Fuyi Chen
Source: Computational Materials Science (article, 2026-02-01)
Field: Materials Science — Materials Chemistry

Deep learning potential reveals surface dislocation nucleation in AgPd Nanoalloy during atomic rearrangement Nucleation ; Dislocation ; Icosahedral symmetry ; Molecular dynamics ; Bimetallic strip ; Partial dislocations ; Lattice (music) ; Stress (linguistics) ; Surface stress Microstructure and mechanical properties ; Machine Learning in Materials Science ; nanoparticles nucleation surface interactions
Authors: Junjie Wu | Timothy J. Rupert
Source: ArXiv.org (article, 2026-01-28)
Field: Materials Science — Materials Chemistry

Extraction of a structural short-range order descriptor from nanobeam electron diffraction patterns using a transfer learning approach Amorphous solids exhibit structural short-range order despite lacking long-range crystalline order, with this structural descriptor found to be important for determining mechanical properties. Nanobeam electron diffraction offers a potential route for experimental characterization of structural short-range order, yet efforts to date have been primarily qualitative in nature. In this work, machine learning approaches based on transfer learning are used to enable quantitative analysis of nanobeam electron diffraction data from amorphous solids. A ResNet-18 model is trained on virtual diffraction patterns taken from different locations within simulated metallic glasses and amorphous grain boundary complexions in the Cu-Zr alloy system that were created with hybrid molecular dynamics and Monte Carlo simulations. The disorder parameter is found to be a superior target structural descriptor compared to traditional Voronoi indices for this task. The model achieves a low validation mean absolute error across diffraction patterns corresponding to different interaction volumes, demonstrating excellent performance and potential transferability. Testing was performed using other simulated nanobeam electron diffraction data as well as experimental nanobeam electron diffraction patterns, showing that the model can reliably capture spatial variations in local structural state. As a whole, this framework is able to overcome the challenges in the quantitative experimental characterization of structural short-range order, enabling improved characterization of amorphous solids and the exploration of structure-property relationships. Diffraction ; Amorphous solid ; Electron diffraction ; Characterization (materials science) ; Monte Carlo method ; Electron backscatter diffraction ; Reverse Monte Carlo Machine Learning in Materials Science ; Metallic Glasses and Amorphous Alloys ; Advanced Electron Microscopy Techniques and Applications
Authors: Zhenyu Zhu | Yabei Wu | Shunbo Hu | Yin Wang
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Application of Literature Datasets and Knowledge Transfer to Accelerate Material Design Field (mathematics) ; Adaptation (eye) ; Predictive modelling ; Data modeling ; Transfer of learning ; Experimental data ; Domain adaptation ; Material properties Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Heusler alloys: electronic and magnetic properties
Authors: Agustin Salcedo | Giovanni Pireddu | Mathieu Salanne | David Lambertin
Source: ChemRxiv (article, 2026-01-28)
Field: Materials Science — Materials Chemistry

Modeling fission product nucleation in molten NaCl using universal machine-learning potentials The design of molten salt nuclear reactors (MSRs) requires a detailed understanding of the formation, precipitation, and transport of solid fission products in the fuel salt. However, the experimental characterization of these processes remains challenging and expensive. Molecular simulations can be leveraged to obtain insight into these systems. In particular, machine learning potentials (MLPs) offer a promising path to obtain relevant insight at an acceptable computational cost. Here, we use a pre-trained foundation MLP to study Ru and Mo atoms in NaCl as a model system for solid fission products in MSR fuel salts. We find the MLP shows good agreement with ab initio calculations for isolated atoms in the salt and allows for sufficiently long simulations to observe their nucleation into nanoparticles and study the local structure around them. This approach provides a practical framework for studying fission product behavior in molten salts relevant to MSR design. Nuclear fission product ; Nucleation ; Fission products ; Molten salt ; Product (mathematics) ; Molecular dynamics ; Fission ; Ab initio quantum chemistry methods Nuclear Materials and Properties ; Machine Learning in Materials Science ; Molten salt chemistry and electrochemical processes
Authors: Jongwon Kim | S Woo | Sooheon Kim | Jinan Jeong | Keuntae Baek | Ilgu Kim | Jaisang Ahn | Suhee Shin | Hongyun So
Source: Journal of Materials Research and Technology (article, 2026-02-21)
Field: Materials Science — Materials Chemistry

Prediction of corrosion behavior of stainless steels with effective corrosion factors using a SHAP-based deep learning framework This study presents a data-driven framework that quantitatively predicts and physically interprets the corrosion behavior of stainless steels using X-ray fluorescence (XRF) compositional data. A deep neural network (DNN), coupled with SHAP (SHapley Additive exPlanations) analysis, provided physically interpretable insights into interactions between alloy composition and processing factors, surpassing basic statistical predictions. Input variables, including exposure time, XRF-measured composition, steel type, and manufacturer, were utilized to train a fully connected DNN model following systematic preprocessing. The framework attained high prediction accuracy (R 2 ≈ 0.97), enabling reliable estimation of corrosion behavior based on pre-exposure composition. SHAP analysis illustrated nonlinear contributions of Si, V, P, Nb, and Ti, consistent with metallurgical principles. Ni and Co displayed minor, yet composition-sensitive effects on the corrosion behavior, in accordance with known metallurgical trends. These results demonstrate that corrosion, previously deemed random and uncontrollable, can be systematically predicted and physically elucidated through data-driven learning, thereby establishing a foundation for AI-guided alloy design and process optimization. Corrosion ; Alloy ; Artificial neural network ; Deep learning ; Foundation (evidence) ; Process (computing) Machine Learning in Materials Science ; Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Niamh O'Neill | Benjamin X. Shi | William C. Witt | Blake Armstrong | William J. Baldwin | Paolo Raiteri | Christoph Schran | Angelos Michaelides | Julian D. Gale
Source: arXiv (Cornell University) (article, 2026-03-06)
Field: Materials Science — Materials Chemistry

From Accurate Quantum Chemistry to Converged Thermodynamics for Ion Pairing in Solution Quantitative prediction of thermodynamic properties in solution is essential for translating atomistic simulations into reliable chemical insight. As an exemplar system, the behaviour of CaCO$_3$ in water has been widely studied to understand its mineralization in seawater, with potential implications for carbon-capture strategies. However, making accurate computational predictions has been a long-standing challenge, requiring both highly accurate electronic structure methods and extensive statistical sampling. Here, we combine advances in machine learning and electronic structure theory to fully resolve the ion pairing free energy of CaCO$_3$ with explicit solvation. We show that achieving quantitative agreement with experiment requires going beyond the standard density functional theory up to the "gold-standard" coupled cluster theory with single, double, and perturbative triple excitations [CCSD(T)]. We generate a set of systematically improvable models, enabling reliable insights into the initial association mechanism of Ca and CO$_3$ ions prior to nucleation while fully quantifying enthalpic and entropic effects. Our results demonstrate that CCSD(T)-level thermodynamic predictions of complex aqueous systems can now be routinely achieved. Density functional theory ; Ion ; Pairing ; Quantum chemistry ; Electronic structure ; Nucleation ; Molecular dynamics ; Coupled cluster ; Cluster (spacecraft) Machine Learning in Materials Science ; Calcium Carbonate Crystallization and Inhibition ; Spectroscopy and Quantum Chemical Studies
Authors: Khanh-Duy Cao-Phan | Hoang-Khang Dang | Huyen-Tran Tran-Quynh | Minh-Phuc Lam-Doan | Huyen-Trang Luu | Hong-Quan Bui
Source: Research Square (preprint, 2026-02-01)
Field: Chemistry — Analytical Chemistry

Fast and Accurate Meat Freshness Classification Using Depthwise Separable Convolution and SPPF Pooling ; Convolution (computer science) ; RGB color model ; Pattern recognition (psychology) ; Separable space ; Pyramid (geometry) ; Computation ; Convolutional neural network Spectroscopy and Chemometric Analyses ; Food Supply Chain Traceability ; Water Quality Monitoring Technologies
Authors: Zhonghua Guo | Yinyu Cheng | Gang Liu | Quanhong Ou
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Chemistry — Analytical Chemistry

FTIR spectroscopy coupled with machine learning for classification and quality evaluation of processed Polygonatum sibiricum Principal component analysis ; Random forest ; Support vector machine ; Pattern recognition (psychology) ; Chemometrics ; Feature selection ; Quality (philosophy) Spectroscopy and Chemometric Analyses ; Polysaccharides Composition and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Ji Huang | Jiali Chen
Source: ChemRxiv (article, 2026-02-17)
Field: Materials Science — Materials Chemistry

A minimal machine-learning correction of DFTB Mulliken charges towards DFT-level MBIS populations Density-functional tight binding (DFTB) enables efficient quantum-based simulations for large molecular systems, but atomic charges are typically reported via Mulliken population analysis and often deviate from density-based charge definitions at the density functional theory (DFT) level. Minimal basis iterative stockholder (MBIS) charges are attractive due to numerical stability and reduced basis-set sensitivity, yet MBIS is not available in standard DFTB workflows due to the lack of an explicit real-space electron density. Here we develop a minimal, physics-informed machine-learning (ML) correction that maps DFTB Mulliken charges to DFT reference MBIS charges at the atom-wise level. The model uses only atomic identity, DFTB Mulliken charge, and inexpensive local-geometry descriptors derived from the molecular structure, while enforcing exact molecular charge conservation at inference by a per-molecule shift. Using 5000 molecules from the QM9 database split into 4000/500/500 for training, validation, and testing, we show that nonlinearity alone provides limited improvement over an element-wise linear baseline, whereas incorporating local geometric descriptors yields a substantial accuracy gain, particularly for carbon and nitrogen. The proposed correction provides DFT-level charge quality at a computational cost comparable to standard DFTB calculations and can be readily integrated into existing DFTB-based workflows. Mulliken population analysis ; Charge (physics) ; Population ; Inference ; Nonlinear system ; Stability (learning theory) ; Density functional theory ; Atomic charge Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Protein Structure and Dynamics