jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ruohan Zhang | Jiawen Yao | Muhammad Mateen | Abdul Wahab | Yang Xiaosen
Source: Microchimica Acta (review, 2025-12-19)
Field: Materials Science — Materials Chemistry

Nanomaterials in food safety and environmental monitoring: advances, challenges, and future directions Food safety ; Environmental safety ; Food industry ; Safety monitoring ; Focus (optics) ; Nanomaterials Carbon and Quantum Dots Applications ; Gold and Silver Nanoparticles Synthesis and Applications ; Melamine detection and toxicity
Authors: Nilesh S. Rajpurohit | Shruti Sinha | Ramesh Potnuri | Chinta Sankar Rao | Harshini Dasari
Source: RSC Advances (article, 2025-01-01)
Field: Materials Science — Polymers and Plastics

Microwave assisted catalytic co-pyrolysis of banana peels and polypropylene: experimentation and machine learning optimization The growing accumulation of agricultural and plastic waste poses serious environmental challenges, necessitating sustainable and efficient valorization strategies. This study investigates the microwave-assisted catalytic co-pyrolysis of banana peels and polypropylene, using graphite as a susceptor and potassium hydroxide as a catalyst. Experiments were conducted by varying biomass and plastic quantities and microwave power levels to study their effects on product yields and thermal performance. The process effectively converted waste materials into valuable products, with oil yield increasing with microwave power and optimized biomass-to-plastic ratios. The rate of mass loss and heating rate were found to significantly influence overall conversion efficiency. A support vector regression (SVR) model was developed to predict yields based on input parameters, achieving a coefficient of determination ranging from 0.81 to 0.99, which demonstrates the reliability of machine learning in capturing complex thermochemical behavior. 3D plots illustrated the nonlinear effects of process variables on yields. Fourier Transform Infrared Spectroscopy (FTIR) and X-ray Diffraction (XRD) analyses of char confirmed functional groups and crystalline phases, suggesting its suitability for applications like adsorbents or catalysts. Brunauer-Emmett-Teller (BET) analysis showed multilayer adsorption, while thermogravimetric analysis (TGA) highlighted distinct thermal degradation patterns of the feedstocks. These results affirm the promise of integrating experiments with ML for efficient waste-to-energy conversion. Polypropylene ; Pyrolysis ; Catalysis ; Pulp and paper industry ; Agriculture ; Plastic waste ; Waste management ; Environmental science ; Materials science ; Biochemical engineering ; Chemistry ; Process engineering ; Organic chemistry ; Engineering ; Biology Natural Fiber Reinforced Composites ; Recycling and Waste Management Techniques ; Thermochemical Biomass Conversion Processes
Authors: Ke Xu | Yuan Li | Dongliang Ding | Ting Liang | Jianyang Wu | Jianbin Xu
Source: The Journal of Physical Chemistry Letters (article, 2025-08-13)
Field: Materials Science — Materials Chemistry

Critical Size Transitions in Silicon Nanowires: Amorphization, Phonon Hydrodynamics, and Thermal Conductivity Understanding the intrinsic thermal transport properties of ultrathin semiconductor nanowires with varying diameters is crucial for the efficient thermal management of next-generation nanoelectronic devices. Here, we developed high-fidelity machine-learning potential (MLP) within the fourth-generation neuroevolution potential framework to elucidate the interplay between structural evolution, amorphous transition behavior, and thermal transport in silicon nanowires (SiNWs), resolving long-standing discrepancies between simulations and experiments. The structure of SiNWs below 1.1 nm in diameter undergoes a complete amorphous transformation, which originates from an amorphous surface structure of 5-6 atomic layers. We identify a nonmonotonic dependence of thermal conductivity on nanowire diameter due to competition between N (Normal) and U (Umklapp) phonon scattering processes. At frequencies <1 THz, N-process scattering rates exceed U-process rates by 3 orders of magnitude in ultrafine SiNWs, enabling fluid-like phonon transport. This study underscores the transformative potential of high-fidelity MLP in unraveling complex nanoscale material behaviors. Nanowire ; Phonon ; Materials science ; Thermal conductivity ; Amorphous solid ; Semiconductor ; Scattering ; Condensed matter physics ; Silicon ; Nanotechnology ; Phonon scattering ; Chemical physics ; Optoelectronics ; Optics ; Crystallography ; Physics ; Chemistry ; Composite material Thermal properties of materials ; Advancements in Semiconductor Devices and Circuit Design ; Advanced Thermoelectric Materials and Devices
Authors: Yawen Wang | Longhua Ding | Mingyue Du | Jian Zhang | Ping Qiu | Longwei Wang | Aizhu Wang | Hongbo Zhao | Xin Yu
Source: Sensors and Actuators B Chemical (article, 2025-08-29)
Field: Materials Science — Materials Chemistry

Integrated colorimetric immunoassay for PSA using confined Pt nanozymes and smartphone-assisted machine learning analysis Immunoassay ; Computer science ; Chemistry ; Nanotechnology ; Chromatography ; Materials science ; Medicine Advanced Nanomaterials in Catalysis ; Advanced biosensing and bioanalysis techniques ; Biosensors and Analytical Detection
Authors: Giacomo Tenti | Kousuke Nakano | Michele Casula
Source: Journal of Chemical Theory and Computation (article, 2025-09-24)
Field: Materials Science — Materials Chemistry

Self-Consistency Error Correction for Accurate Machine Learning Potentials from Variational Monte Carlo Variational Monte Carlo (VMC) can be used to train accurate machine learning interatomic potentials (MLIPs), enabling molecular dynamics (MD) simulations of complex materials on time scales and system sizes previously unattainable. VMC training sets are often based on partially optimized wave functions (WFs) to circumvent expensive energy optimizations of the whole set of WF parameters. However, frozen variational parameters lead to VMC forces and pressures not consistent with the underlying potential energy surface, a bias called the self-consistency error (SCE). Here, we demonstrate how the SCE can spoil the accuracy of MLIPs trained on these data, taking high-pressure hydrogen as the test case. We then apply a recently introduced SCE correction [<i>Phys. Rev. B</i> <b>2024</b> <u><i>109</i></u>, 205151] to generate unbiased VMC training sets based on a Jastrow-correlated single determinant WF with frozen Kohn-Sham orbitals. The MLIPs generated within this framework are significantly improved and can approach in quality those trained on data sets built with fully optimized WFs. Our conclusions are further supported by MD simulations, which show how MLIPs trained on SCE-corrected data sets systematically yield more reliable physical observables. Our framework opens the possibility of constructing extended high-quality training sets with VMC. Monte Carlo method ; Set (abstract data type) ; Energy (signal processing) ; Training set ; Quality (philosophy) ; Variational Monte Carlo ; Data set ; Test set Machine Learning in Materials Science ; Nuclear Physics and Applications ; Nuclear Materials and Properties
Authors: Yuki Kusano | Hiroyuki Yoshida
Source: Journal of Electron Spectroscopy and Related Phenomena (article, 2025-12-29)
Field: Materials Science — Surfaces, Coatings and Films

Automated analysis of low-energy inverse photoelectron spectra using machine learning The electron affinity (EA) of semiconductors is the energy of the conduction band edge with respect to the vacuum level and represents the electron conduction level. The EAs of organic semiconductors are most precisely determined by low-energy inverse photoelectron spectroscopy (LEIPS). However, data analysis to determine EA from a LEIPS spectrum requires skilled analysts and time-consuming procedures. In this study, we developed a machine learning-based automated analysis method. The vacuum level was determined with an accuracy of 0.01 eV by analyzing the low-energy electron transmission (LEET) spectrum using linear regression. The onset of the LEIPS spectrum was determined by applying Random Forest and Gradient Boosting as learning models to 253 experimental LEIPS spectra derived from measurements of 27 organic semiconductors as the training dataset. By implementing the smoothing process and augmenting the training data in the energy direction, we achieved a prediction accuracy of within ±0.2 eV (±0.1 eV) for 90 % (70 %) and 91 % (74 %) of the test data as the average of six partitions for the Gradient Boosting and Random Forest models, respectively. The fully automated determination of EA is realized by the combined use of LEET and LEIPS spectral onset analyses. • Developed a machine learning-based method for automated EA determination from LEIPS spectra. • Vacuum level determined with 0.01 eV accuracy using regression analysis of LEET spectra. • Spectral onset predicted using Random Forest and Gradient Boosting models trained on 253 experimental LEIPS spectra. • Spectral smoothing and data augmentation improved prediction accuracy to within ±0.2 eV for over 90 % of test data. • Method enables fast, reliable EA analysis without expert intervention and is applicable to other spectroscopic techniques. Gradient boosting ; Smoothing ; Random forest ; Boosting (machine learning) ; Spectral line ; Inverse ; Test data ; Data point ; X-ray photoelectron spectroscopy Electron and X-Ray Spectroscopy Techniques ; Organic Electronics and Photovoltaics ; Machine Learning in Materials Science
Authors: Xingchen Shen | Jiongzhi Zheng | Michael Marek Koza | Petr Levinský | J. Hejtmánek | Philippe Boullay | B. Raveau | Jinghui Wang | Jun Li | Pierric Lemoine | Christophe Candolfi | Emmanuel Guilmeau
Source: Nature Communications (article, 2025-12-17)
Field: Materials Science — Materials Chemistry

Accelerated discovery of crystalline materials with record ultralow lattice thermal conductivity via a universal descriptor Ultralow glass-like lattice thermal conductivity in crystalline materials is crucial for enhancing energy conversion efficiency in thermoelectrics and thermal insulators. We introduce a universal descriptor for thermal conductivity that relies only on the atomic number in the primitive cell and the sound velocity, enabling fast and scalable materials screening. Coupled with high-throughput workflows and universal machine learning potentials, we identify the candidate materials with ultralow thermal conductivity from over 25, 000 materials. We further validate this approach by experimentally confirming record-low thermal conductivity values of 0.15-0.16 W/m·K from 170 to 400 K in the halide metal CsAg<sub>2</sub>I<sub>3</sub>. Combining inelastic neutron scattering with first-principles calculations, we attribute the ultralow thermal conductivity to the intrinsically small sound velocity, strong anharmonicity, and structural complexity. Our work illustrates how a universal descriptor, combined with high-throughput screening, machine-learning potential and experiment, enables the efficient discovery of materials with ultralow thermal conductivity. Thermal conductivity ; Thermoelectric materials ; Phonon ; Inelastic neutron scattering ; Work (physics) ; Thermal Thermal properties of materials ; Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices
Authors: Viviana Palacio‐Betancur | Nicholas E. Jackson
Source: Journal of the American Chemical Society (article, 2025-12-13)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Molecular Charge Topologies Govern Polar Nematic Ordering Polar nematic phases exhibit spontaneous macroscopic polarization while retaining the orientational order of conventional nematics, offering opportunities for responsive optical and electronic materials. The molecular determinants of polar order remain elusive, as traditional design strategies focus on the magnitude of the dipole moment without reliable prediction. We analyze charge topologies of a curated data set of 236 nematic compounds by computing their σ-profiles and charge-weighted Graph Fourier Transforms (GFT). We show that machine learning classification using a single order parameter derived from GFT can accurately distinguish polar and apolar nematic formers and outperforms dipolar and σ-profile metrics. This framework provides a physically interpretable and computationally lightweight approach capable of identifying topological motifs governing spontaneous polarization. Liquid crystal ; Polar ; Dipole ; Network topology ; Polarization (electrochemistry) ; Topology (electrical circuits) ; Chemical polarity ; Charge (physics) Liquid Crystal Research Advancements ; Advanced Materials and Mechanics ; Machine Learning in Materials Science
Authors: Lin Han | Zhendong Li | Zhunyun Tang | Xiaoxia Wang | Jin Li | Chaoyu He | Chao Tang | Tao Ouyang
Source: Applied Physics Letters (article, 2025-11-17)
Field: Materials Science — Materials Chemistry

The thermal transport properties of monolayer BiOsO3: The role of spin–orbit coupling and magnetic anisotropy Thermal transport in magnetic materials has attracted tremendous attention due to its importance in spintronics and thermal management. However, existing calculations of thermal conductivity of magnetic materials often simplify the complex non-collinear spin structures and realistic magnetic moment orientations by assuming collinear spin structures with spins aligned along the z-axis. To evaluate the reliability of such simplification, by means of machine learning potentials (MLPs) combined with the Boltzmann transport equation (BTE), we systematically investigate the thermal transport properties of monolayer BiOsO3, a ferromagnetic semiconductor with strong spin–orbit coupling (SOC) and large magnetic anisotropy (MA). The calculations show that SOC effect could significantly enhance the lattice thermal conductivity (2.83 W/mK) by a factor of approximately 2.2 compared to the case without SOC (1.29 W/mK). This obvious enhancement mainly originates from the fact that the introduction of SOC drives charge redistribution toward the inner Os–O bond network, thereby enhancing the symmetry of internal potential wells and suppressing phonon anharmonicity. Such behavior indicates that the previous simplified collinear approaches are insufficient to accurately describe the thermal transport in magnetic materials with strong SOC. In contrast, BiOsO3 exhibits a large magnetic anisotropy energy (MAE=−5.7 meV/Os). However, the orientation of spin easy axis hosts a weak influence on the thermal conductivity. These findings shed light on the thermal conductivity of BiOsO3, and could also provide meaningful guidance for studying thermal transport in magnetic materials. Thermal conductivity ; Spintronics ; Boltzmann equation ; Magnetic anisotropy ; Ferromagnetism ; Anisotropy ; Magnetic moment ; Phonon 2D Materials and Applications ; Thermal properties of materials ; Machine Learning in Materials Science
Authors: Chandra Chowdhury
Source: Materials Horizons (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Quantum support vector classifier for phase diagram prediction in quinary systems The integration of machine learning (ML) in materials science has accelerated the discovery and optimization of novel materials. However, classical ML approaches often face limitations in handling the increasing complexity and scale of modern datasets. Quantum machine learning (QML), leveraging quantum computing principles, offers a promising avenue to address these challenges. This study explores the application of the quantum support vector classifier (QSVC) to predict phase diagrams in the Al-Cu-Mg-Si-Zn quinary system. The prediction of the phase diagram of quinary systems contributes to the achievement of issues related to sustainable development goals by facilitating the creation of sophisticated materials that possess exceptional characteristics. This, in turn, promotes innovation and sustainable practices within the industrial sector. We used a comprehensive dataset from high-throughput CALPHAD calculations, and employed QSVC with advanced quantum feature transformations and kernel methods. Our results demonstrate significant improvements in predictive accuracy and efficiency compared to classical SVC, highlighting the potential of QML to advance material design. Quinary ; Phase diagram ; Quantum ; Classifier (UML) ; Materials science ; Artificial intelligence ; Computer science ; Phase (matter) ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Quantum Computing Algorithms and Architecture
Authors: Zhao Fan | Deepak Rawat | Piotr Zarzycki | Michael Whittaker | Mark Asta
Source: Proceedings of the National Academy of Sciences (article, 2025-06-20)
Field: Materials Science — Materials Chemistry

Multistage nucleation pathway in LiF molten salt mirrors the crystal–melt interface structure Despite over a century of studies, fundamental questions remain about the processes governing crystal nucleation from melts or solutions. Research over the past three decades has presented mounting evidence for kinetic pathways of crystal nucleation that are more complex than envisioned by the simplest forms of classical theory. Such observations have been presented for colloidal and elemental systems with covalent and metallic bonding. Despite the technological and geochemical importance of molten salts, similar studies for these ionically bonded systems are currently lacking. Here we develop a machine learning interatomic potential for a model ionic system: LiF. The potential features quantum-level accuracy for both liquid and multiple solid polymorphs over wide temperature and pressure ranges and accurately reproduces experimentally measured properties. Thanks to the efficiency of the potential, which enables microsecond-scale molecular dynamics simulations, induction times for nucleation of LiF solids from their melts are computed over a range of undercoolings. With the aid of a set of robust local order parameters established here, the simulations reveal that homogeneous crystal nucleation in undercooled melts preferentially initiates from liquid regions showing slow dynamics and high bond orientational order simultaneously, and the second-shell order of both precritical nuclei and the surface of postcritical nuclei is dominated by hexagonal close packing and body-centered cubic local structure, even though the nucleus core is dominated by face-centered cubic structure corresponding to the stable rocksalt crystal structure. Finally, we establish a connection between the crystallization pathway and the equilibrium crystal-melt interface structure. Nucleation ; Chemical physics ; Crystal (programming language) ; Crystallization ; Ionic bonding ; Crystal structure ; Molecular dynamics ; Materials science ; Interatomic potential ; Crystallography ; Thermodynamics ; Chemistry ; Ion ; Computational chemistry ; Physics Material Dynamics and Properties ; Machine Learning in Materials Science ; Crystallization and Solubility Studies
Authors: Rahul Mukherjee | Shubhabrata Datta
Source: Journal of Materials Engineering and Performance (article, 2025-08-25)
Field: Materials Science — Biomaterials

Design of Biodegradable Magnesium Alloy Using Bi-objective Genetic Programming and Multi-objective Genetic Algorithm in Tandem Materials science ; Magnesium alloy ; Genetic algorithm ; Tandem ; Genetic programming ; Magnesium ; Alloy ; Metallurgy ; Mathematical optimization ; Composite material ; Computer science ; Machine learning ; Mathematics Magnesium Alloys: Properties and Applications ; Corrosion Behavior and Inhibition ; Aluminum Alloys Composites Properties
Authors: Mustafa Çağrı Bayır | Ebru Mensur
Source: Journal of Materials Science Materials in Electronics (article, 2025-06-01)
Field: Materials Science — Materials Chemistry

Electrocaloric property optimization of PMN-PT ceramics using predictive modeling Abstract An eXtreme Gradient Boosting (XGBoost) machine learning model was developed to predict the electrocaloric temperature change (Δ T EC ) of PMN-PT electroceramics based on processing parameters (PMN/PT ratio, sample type, calcination and sintering time/temperature) and measurement conditions (direct/indirect measurement, electric field, and measurement temperature). A dataset of 2863 data points was compiled from the experimental literature, and the model achieved a coefficient of determination ( R 2 ) of 0.97 and a mean absolute error (MAE) of 0.04 °C on the test set, demonstrating strong predictive performance. Feature importance analysis revealed that electric field and T-T C are the primary drivers of electrocaloric response, while processing conditions also significantly influence Δ T EC . A Bayesian Optimization framework was employed to systematically identify the optimal processing parameters for maximizing Δ T EC . Under experimentally realistic conditions (24 °C measurement temperature, 40 kV/cm electric field), the optimal PMN/PT composition (81.06%/18.94%) and processing parameters (calcination at 810 °C for 3 h, sintering at 1280 °C for 7 h) were identified, yielding a predicted Δ T EC of 1.40 °C, which exceeds most experimentally reported values under similar conditions. Additionally, a virtual dataset of 100.000 synthetic data points was generated and analyzed to explore high-performance regions within the electrocaloric design space. The results highlight the potential of machine learning in materials science, offering a scalable and data-driven approach to accelerate the discovery and optimization of advanced electrocaloric materials. Materials science ; Property (philosophy) ; Ceramic ; Composite material Ferroelectric and Piezoelectric Materials ; Dielectric materials and actuators ; Shape Memory Alloy Transformations
Authors: Yuzhen Lv | Guang-Ying Li | Lei Zhang | Xiaoli Tan | Tao Bo
Source: Journal of Nuclear Materials (article, 2025-09-30)
Field: Materials Science — Materials Chemistry

Machine learning-based force field development and microstructure-property investigation of NaF-BeF2 molten salt fuel Molten salt ; Molten salt reactor ; Melting point ; Molecular dynamics ; Work (physics) ; Salt (chemistry) Nuclear Materials and Properties ; Thermodynamic and Structural Properties of Metals and Alloys ; Thermal and Kinetic Analysis
Authors: Ainhoa Osa-Sanchez | Irantzu Barrio | Ganeko Bernardo‐Seisdedos | Amaia Méndez Zorrilla | Begonya García-Zapirain
Source: Food Analytical Methods (article, 2025-09-01)
Field: Chemistry — Analytical Chemistry

Detection of LTP in Food Using Near-Infrared Spectroscopy and Explainable Artificial Intelligence Spectroscopy ; Chemistry ; Infrared spectroscopy ; Long-term potentiation ; Infrared ; Functional near-infrared spectroscopy ; Biology ; Neuroscience ; Biochemistry ; Organic chemistry ; Physics ; Optics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Analytical Chemistry and Chromatography