jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yulin Ni | Yue Wang | Chenguang Yang | Xiuyu Zhang | Junjie Xu
Source: IEEE/ASME Transactions on Mechatronics (article, 2025-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Adaptive Approximate Inverse Control of Fractional-Order Hysteric Systems and Its Application on Independently Developed Giant Magnetostrictive Motion Platform Focusing on the high-precision motion control of time-delay nonlinear systems actuated by smart-material-based actuators and inspired by the flourishing development of fractional-order control theory, this article proposes an adaptive fractional-order command filter backstepping approximate inverse control scheme with the following key features. First, to the best of the authors’ knowledge, by combining the limited coverage lemma with the neural network, the unknown time-delay functions in the fractional-order nonlinear systems are precisely online estimated for the first time. This leads to the control problem of fractional-order nonlinear systems with time delays being overcome. Second, a new fractional-order filter is constructed to compute fractional differentiation of the designed virtual control laws, and simultaneously, the compensation signal is designed to eliminate the negative impact of filtering errors on the controller. Third, the approximate inverse model of asymmetric hysteresis for fractional-order asymmtric hysteretic nonlinear systems is derived, and the asymmetric hysteresis nonlinearities are precisely mitigated. Fourth, the independently developed giant magnetostrictive actuator-based motion platform is constructed, and a new fractional-order model of this platform is developed. Then, the motion control experiments are conducted to show the effectiveness of the proposed control scheme. Control theory (sociology) ; Backstepping ; Nonlinear system ; Inverse ; Filter (signal processing) ; Actuator ; Inverse filter ; Adaptive control ; Motion control Magnetic Properties and Applications ; Piezoelectric Actuators and Control ; Electric Motor Design and Analysis
Authors: Devan Kuppusamy | Praveen Rajkumar | C.J.G. Garcia | S. Mitra
Source: Journal of Emerging Investigators (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Analysis of quantitative classification and properties of X-ray binary systems X-ray astronomy is a field of research focusing on astronomical objects that emit X-ray radiation. The first interstellar X-ray source, Scorpius X-1, was discovered in 1962. Since then, hundreds of X-ray binaries (XRBs) have been discovered inside the Milky Way, with many unusual properties. These binary systems are currently classified as either high- or low-mass XRBs. However, it is useful to the field to have a classification system consisting of more properties than just mass to have a more descriptive classification of a stellar object. We aimed to analyze the patterns in several variables contributing to an XRB’s properties to find better-fit ways to classify these systems. We hypothesized that there would be a large difference in classes of XRBs across several variables, not just mass, but including the characteristics of a companion star. Utilizing several machine learning algorithms, we analyzed several variables describing an XRB. Dimensionality reduction was used to find correlations between the most important variables defining a star such as temperature, orbital period, metallicity, and redshift, and the classification groups each star falls under. We found that temperature is a quantitative parameter to classify these systems. There are three main groups of temperatures that XRBs fall into. These being stars with a metallicity &amp;lt;-2 and &amp;lt;100000K, those with a metallicity &amp;gt;-2 and &amp;lt;100000K, and those with a metallicity &amp;gt;-2 and &amp;gt;100000K. Characterizing XRBs are important to find star parameters that explain the chemical evolution of the galaxy and understanding of how binary parameters evolve toward detectable mergers. Metallicity ; Binary number ; Galaxy ; Binary star ; Milky Way ; Field (mathematics) ; Stars X-ray Diffraction in Crystallography ; Chemical Thermodynamics and Molecular Structure
Authors: Xuchen Yu (10302218) | Tingbo Zhang (11653444) | Liang Ma (37793) | Qionghua Zhou (2208739) | Jinlan Wang (1235505)
Source: Figshare (article, 2025-02-10)
Field: Materials Science — Materials Chemistry

MachineLearning Assisted Design of Type-II Two-DimensionalHeterostructures for Photocatalytic Water Splitting Type-II two-dimensional (2D) heterostructures are promising for photocatalytic water splitting but face exploration challenges due to high experimental/computational costs. Here, we propose an efficient data-driven approach for the rapid discovery of type-II van der Waals heterostructures (vdWHs) without the need for preoptimization of structures or precise stacking information. To meet this end, a specially designed matrix descriptor is developed to capture the important interlayer interactions. Coupled with a one-dimensional convolutional neural network, this descriptor can well describe weak interlayer interactions in heterostructures, allowing direct prediction of bandgap and band edge positions of arbitrary 2D heterostructures. 800 potential candidates are successfully screened out of nearly 10<sup>5</sup> heterostructures for type-II vdWHs, and further comprehensive band structure and optical absorption spectra calculations reveal the potential of WS<sub>2</sub>/Rh<sub>2</sub>Br<sub>6</sub> and Al<sub>2</sub>S<sub>2</sub>/PtS<sub>2</sub> as water splitting photocatalysts. This work provides a data-driven approach to energy materials discovery and offers a cost-effective alternative to traditional methods. Stacking ; Heterojunction ; Band gap ; Water splitting ; Absorption (acoustics) ; Photocatalysis ; Enhanced Data Rates for GSM Evolution ; van der Waals force 2D Materials and Applications ; Advanced Photocatalysis Techniques ; Machine Learning in Materials Science
Authors: E. Brando | E.T. Dubois | M. Rochedy | J. Bouchet | Fabien Bruneval | Jean-Paul Crocombette | Julien Tranchida
Source: Journal of Nuclear Materials (article, 2025-11-07)
Field: Materials Science — Materials Chemistry

Atomistic investigation of the temperature and stoichiometry dependence of the heat-capacity in nuclear fuel UO <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"> <mml:msub> <mml:mrow/> <mml:mrow> <mml:mn>2</mml:mn> <mml:mo linebreak="badbreak" linebreakstyle="after">+</mml:mo> <mml:mi>x</mml:mi> </mml:mrow> </mml:msub> </mml:math> Molecular dynamics ; Stoichiometry ; CALPHAD ; Interatomic potential ; Oxygen ; Function (biology) Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Radioactive element chemistry and processing
Authors: HUANG Yajing | Yongping Zheng | Huang Zhigao
Source: Acta Physica Sinica (article, 2025-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Thermodynamic properties of hydrogen decrepitation process in Nd&lt;sub&gt;2&lt;/sub&gt;Fe&lt;sub&gt;14&lt;/sub&gt;B permanent magnet materials Recycling is a sustainable strategy for the efficient utilization of rare earth resources. Hydrogenation milling has been widely adopted because of its high efficiency and environmental benefits. However, the formation of unstable phases in the hydrogenation process significantly reduces recovery efficiency, which presents new challenges for process optimization. In this study, a combination of first-principles calculations and machine learning methods is employed to systematically investigate the thermodynamic behavior of key rare earth hydrides, such as NdH&lt;sub&gt;2&lt;/sub&gt;, NdH&lt;sub&gt;3&lt;/sub&gt;, and Nd&lt;sub&gt;2&lt;/sub&gt;H&lt;sub&gt;5&lt;/sub&gt; in the hydrogenation milling process using the Debye model for lattice vibrations. The results show that a temperature centered at about 630 K at a pressure of 600 kPa may offer ideal operational conditions for the hydrogenation milling process. Under these conditions, NdH&lt;sub&gt;2&lt;/sub&gt; can undergo spontaneous hydrogenation, and the formation of unstable phases can be effectively suppressed, thereby improving rare earth recovery efficiency. This study also reveals the potential adverse effects of excessively high temperatures on the stability and reactivity of NdH&lt;sub&gt;2&lt;/sub&gt;, further emphasizing the importance of operating within a specific temperature range. These findings provide new insights into the thermodynamic mechanisms of the hydrogenation process in Nd&lt;sub&gt;2&lt;/sub&gt;Fe&lt;sub&gt;14&lt;/sub&gt;B permanent magnet material. Furthermore, they offer theoretical guidance for the optimization of industrial hydrogenation milling parameters. Magnet ; Hydrogen ; Materials science ; Process (computing) ; Chemistry ; Thermodynamics ; Physics ; Mechanical engineering ; Engineering ; Computer science ; Operating system Magnetic Properties of Alloys ; Superconducting Materials and Applications ; Magnetic and Electromagnetic Effects
Authors: Kaiwen Huang | Zhen Yuan | Yuchuan Wang | Yuli Wang | Jianjun Yuan | Rong Xia
Source:  (article, 2025-11-21)
Field: Materials Science — Materials Chemistry

Elongation-at-Break Prediction Method for Cable Insulation Based on the MAC-NIR Framework The mechanical integrity of cable insulation is a critical factor affecting the operational reliability of power systems, where elongation at break serves as an important indicator of material toughness and service life. Conventional tensile testing methods are time-consuming and destructive, making them unsuitable for rapid evaluation. To overcome these limitations, a Monte Carlo–Adjusted Calibration for NearInfrared (MAC-NIR) framework is proposed for the prediction of elongation at break in cross-linked polyethylene (XLPE) cable insulation. The framework integrates optical-path correction, spectral-dimension adjustment, and Monte Carlo data augmentation within a one-dimensional convolutional neural network (1D-CNN). Path-length normalization mitigates thickness-related optical variations, while spectral up-/down-sampling controls model complexity. The Monte Carlo strategy expands the dataset and balances data distribution, improving convergence and generalization performance. Experimental results show that the proposed method achieves high prediction accuracy, with an average relative deviation below 5% on blind samples. The MAC-NIR framework demonstrates strong robustness and application potential for quantitative evaluation of cable insulation performance, providing methodological support for condition assessment and intelligent diagnosis of power cables. Monte Carlo method ; Robustness (evolution) ; Normalization (sociology) ; Reliability (semiconductor) ; Calibration ; Ultimate tensile strength ; Experimental data ; Artificial neural network High voltage insulation and dielectric phenomena ; Thermal Analysis in Power Transmission ; Electrical Fault Detection and Protection
Authors: Richard Head | Saiful Islam | Jennifer Martin
Source: Pharmacology Research & Perspectives (review, 2025-07-26)
Field: Chemistry — Spectroscopy

A Unified Explanation for Drug Repurposing and Pharmacological Pleiotropy Based on Classical and Statistical Thermodynamics Drug repurposing is an authentic, emerging, and growing aspect of drug development when the demand for new therapeutic solutions is high. Many repurposed drugs have been discovered by serendipity or a non-ordered process driven by chance and sharp observation. These discoveries provide strong evidence for the existence of pharmacological pleiotropy, a highly ordered process well described by thermodynamics. Pleiotropy is an efficient way of propagating information and maintaining the specificity of a biological message and has been a cornerstone in genetics research over decades. While the definition, scale, diversity, and complexity associated with drug repurposing are well documented, pharmaceutical pleiotropy that is fundamental to our understanding of drug repurposing remains less explored. In this review, we examine pharmacological pleiotropy and its underpinning thermodynamics in drug repurposing. Additionally, we have drawn upon the universality of thermodynamics to provide insights into pharmaceutical pleiotropy. We suggest that, in serendipitous drug discovery, information in the repurposed drug often exceeds what was thought available with the rational design of the drug. Our interest in repurposing is on leveraging this information and knowledge generally once a therapeutic benefit from a new chemical entity (NCE) has been demonstrated. This requires a different process from standard drug discovery, and this repurposed pathway is the focus of our manuscript. In this review, we propose that drug repurposing can be defined using Information theory (Shannon entropy), Boltzmann statistical entropy, and the thermodynamic principles for spontaneity described by Gibbs free energy of binding. We conclude that therapeutics including repurposed drugs are facilitators of information and instructional transfer and that the distinguishing features of pharmacology, Information theory, and statistical mechanics are intimately linked. With advances in artificial intelligence and machine learning, with their strong links to Information theory and statistical mechanics, now is an appropriate time to further explore these relationships. Pleiotropy ; Drug repositioning ; Repurposing ; Computer science ; Drug ; Biology ; Pharmacology ; Ecology Molecular spectroscopy and chirality ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Md Rosaidul Mawla | Radmim Ryean | Abdus Sattar Mollah | Sarwar Saif | Anisur Rahman | Md. Shafiqul Islam
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Evaluation of Radioactive Release from the Rooppur Nuclear Power Plant Resulting from Hypothetical Accidents Along with Prediction by Machine Learning Nuclear power plant ; Nuclear power ; Nuclear engineering ; Computer science ; Environmental science ; Artificial intelligence ; Engineering ; Nuclear physics ; Physics Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Nuclear and radioactivity studies
Authors: Georganna Benedetto (14607894) | Patrick Damacet (11863920) | Elissa O. Shehayeb (11863914) | Gbenga Fabusola (22378814) | Cory M. Simon (1669942) | Katherine A. Mirica (1473688)
Source: Figshare (article, 2025-10-06)
Field: Chemistry — Inorganic Chemistry

Metal–Organic Framework-Based ChemiresistiveArray Paired with Machine Learning Algorithms for the Detection andDifferentiation of Toxic Gases The development of low-power, sensitive, and selective gas sensors capable of detecting and differentiating toxic gases is pivotal for safety and environmental monitoring. This paper describes a chemiresistive sensor array comprising a series of three conductive hexahydroxytriphenylene-based metal–organic frameworks (MOFs) (M<sub>3</sub>(HHTP)<sub>2</sub> (M = Ni, Cu, Zn)) capable of detecting and differentiating parts-per-million (ppm) levels of carbon monoxide (CO), ammonia (NH<sub>3</sub>), sulfur dioxide (SO<sub>2</sub>), hydrogen sulfide (H<sub>2</sub>S), and nitric oxide (NO), as well as binary mixtures of SO<sub>2</sub> and H<sub>2</sub>S in dry nitrogen at room temperature. This capability arises from variations in the identity of the linking metal and the framework packing pattern across the materials in the array. To visualize the response pattern of the sensor array and map it to a predicted gas composition, principal component analysis and random forest classification are employed. Both machine learning techniques confirm the ability to discriminate CO, NH<sub>3</sub>, SO<sub>2</sub>, H<sub>2</sub>S, and NO analytes as well as binary SO<sub>2</sub>/H<sub>2</sub>S mixtures at ppm concentrations using the response of the array. Moreover, a feature importance method applied to the classifier assigns importance scores to each sensor in the array to quantify the impact of individual materials on analyte discrimination. Spectroscopic investigations provide insight into how the structural features of the MOFs influence sensing performance and ascertain material–analyte interactions governing sensing selectivity for SO<sub>2</sub>/H<sub>2</sub>S binary mixtures. Analyte ; Hydrogen sulfide ; Principal component analysis ; Classifier (UML) ; Binary classification ; Sensor array ; Binary number ; Carbon monoxide ; Random forest Metal-Organic Frameworks: Synthesis and Applications ; Gas Sensing Nanomaterials and Sensors ; Advanced Chemical Sensor Technologies
Authors: P. C. Canfield | Tyler J. Slade
Source: ArXiv.org (preprint, 2025-03-13)
Field: Materials Science — Materials Chemistry

Use of frit-disc crucible sets to make solution growth more quantitative and versatile The recent availability of step-edge, frit-disc crucible sets (generally sold as Canfield Crucible Sets or CCS) has led to multiple innovations associated with our group's use of solution growth. Use of CCS allows for the clean separation of liquid from solid phases during the growth process. This clean separation enables the reuse of the decanted liquid, either allowing for simple, economic, savings associated with recycling expensive precursor elements or allowing for the fractionation of a growth into multiple, small steps, revealing the progression of multiple solidifications. Clean separation of liquid from solid phases also allows for the determination of the liquidus line (or surface) and the creation, or correction, of composition-temperature phase diagrams. The reuse of clean decanted liquid has also allowed us to prepare liquids ideally suited for the growth of large single crystals of specific phases by tuning the composition of the melt to the optimal composition for growth of the desired phase, often with reduced nucleation sites. Finally, we discuss how solution growth and CCS use can be harnessed to provide a plethora of composition-temperature data points defining liquidus lines or surfaces with differing degrees of precision to either test or anchor artificial intelligence and/or machine learning based attempts to augment and extend the limited experimentally determined data base. Liquidus ; Crucible (geodemography) ; Decantation ; Reuse ; Nucleation ; Phase (matter) ; Fractionation ; Separation (statistics) ; Process (computing) Machine Learning in Materials Science ; Solidification and crystal growth phenomena ; Crystallization and Solubility Studies
Authors: Yunlong Wang | Liang Zhixin | Chi Ding | Junjie Wang | Zheyong Fan | Hui‐Tian Wang | Dingyu Xing | Jian Sun
Source: ArXiv.org (preprint, 2025-09-08)
Field: Materials Science — Materials Chemistry

GPUTB: Efficient Machine Learning Tight-Binding Method for Large-Scale Electronic Properties Calculations The high computational cost of ab-initio methods limits their application in predicting electronic properties at the device scale. Therefore, an efficient method is needed to map the atomic structure to the electronic structure quickly. Here, we develop GPUTB, a GPU-accelerated tight-binding (TB) machine learning framework. GPUTB employs atomic environment descriptors, enabling the model parameters to incorporate environmental dependence. This allows the model to transfer to different basis, xc-functionals, and allotropes easily. Combined with the linear scaling quantum transport method, we have calculated the electronic density of states for up to 100 million atoms in pristine graphene. Trained on finite-temperature structures, the model can be easily extended to millions of atom finite-temperature systems. Furthermore, GPUTB can also successfully describe h-BN/graphene heterojunction systems, demonstrating its capability to handle complex material with high precision. We accurately reproduce the relationship between carrier concentration and room temperature mobility in graphene to verify the framework's accuracy. Therefore, our GPUTB framework presents a delicate balance between computational accuracy and efficiency, providing a powerful computational tool for investing electronic properties for large systems with millions of atoms. Electronic structure ; Heterojunction ; Linear scale ; Atom (system on chip) ; Scaling ; Electronic systems ; Quantum Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques
Authors: Yash Barhate | Yun-Sun Kang | Neda Nazemifard | Ch. Renner | Yihui Yang | Charles D. Papageorgiou | Zoltan K. Nagy
Source: Computers & Chemical Engineering (article, 2025-12-10)
Field: Materials Science — Materials Chemistry

Digital design of crystallization processes using statistical machine learning Workflow ; Process (computing) ; Probabilistic logic ; Dependency (UML) ; Design of experiments ; Statistical model ; Process modeling Crystallization and Solubility Studies ; Machine Learning in Materials Science ; Process Optimization and Integration
Authors: Yunlong Wang | Liang Zhixin | Chi Ding | Junjie Wang | Zheyong Fan | Hui‐Tian Wang | Dingyu Xing | Jian Sun
Source: Computational Materials Today (article, 2025-09-28)
Field: Materials Science — Materials Chemistry

GPUTB: Efficient machine learning tight-binding method for large-scale electronic properties calculations The high computational cost of ab-initio methods limits their application in predicting electronic properties at the device scale. Therefore, an efficient method is needed to map the atomic structure to the electronic structure quickly. Here, we develop GPUTB, a GPU-accelerated tight-binding (TB) machine learning framework. GPUTB employs atomic environment descriptors, enabling the model parameters to incorporate environmental dependence. This allows the model to transfer to different basis, xc-functionals, and allotropes easily. Combined with the linear scaling quantum transport method, we have calculated the electronic density of states for up to 100 million atoms in pristine graphene. Trained on finite-temperature structures, the model can be easily extended to millions of atom finite-temperature systems. Furthermore, GPUTB can also successfully describe h-BN/graphene heterojunction systems, demonstrating its capability to handle complex material with high precision. We accurately reproduce the relationship between carrier concentration and room temperature mobility in graphene to verify the framework's accuracy. Therefore, our GPUTB framework presents a delicate balance between computational accuracy and efficiency, providing a powerful computational tool for investigating electronic properties for large systems with millions of atoms. Electronic structure ; Heterojunction ; Linear scale ; Atom (system on chip) ; Scaling ; Electronic systems ; Computation Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Surface and Thin Film Phenomena