jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ahmed Shoyeb Raihan | Zhichao Liu | Tanveer Hossain Bhuiyan | Imtiaz Ahmed
Source: ArXiv.org (preprint, 2025-03-27)
Field: Materials Science — Materials Chemistry

Confidence Adjusted Surprise Measure for Active Resourceful Trials (CA-SMART): A Data-driven Active Learning Framework for Accelerating Material Discovery under Resource Constraints Accelerating the discovery and manufacturing of advanced materials with specific properties is a critical yet formidable challenge due to vast search space, high costs of experiments, and time-intensive nature of material characterization. In recent years, active learning, where a surrogate machine learning (ML) model mimics the scientific discovery process of a human scientist, has emerged as a promising approach to address these challenges by guiding experimentation toward high-value outcomes with a limited budget. Among the diverse active learning philosophies, the concept of surprise (capturing the divergence between expected and observed outcomes) has demonstrated significant potential to drive experimental trials and refine predictive models. Scientific discovery often stems from surprise thereby making it a natural driver to guide the search process. Despite its promise, prior studies leveraging surprise metrics such as Shannon and Bayesian surprise lack mechanisms to account for prior confidence, leading to excessive exploration of uncertain regions that may not yield useful information. To address this, we propose the Confidence-Adjusted Surprise Measure for Active Resourceful Trials (CA-SMART), a novel Bayesian active learning framework tailored for optimizing data-driven experimentation. On a high level, CA-SMART incorporates Confidence-Adjusted Surprise (CAS) to dynamically balance exploration and exploitation by amplifying surprises in regions where the model is more certain while discounting them in highly uncertain areas. We evaluated CA-SMART on two benchmark functions (Six-Hump Camelback and Griewank) and in predicting the fatigue strength of steel. The results demonstrate superior accuracy and efficiency compared to traditional surprise metrics, standard Bayesian Optimization (BO) acquisition functions and conventional ML methods. Surprise ; Benchmark (surveying) ; Active learning (machine learning) ; Bayesian probability ; Resource (disambiguation) ; Process (computing) Machine Learning in Materials Science ; Statistical and Computational Modeling ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Fahimeh Ahmadi | Mohsen Hajihassani | Rosli Hussin | Sib Krishna Ghoshal
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Ceramics and Composites

Structural and optical attributes of erbium ions-doped magnesium-zinc-sulfophosphate glass: experimental and GEP-driven predictive model evaluation Erbium ; Raman spectroscopy ; Amorphous solid ; Photonics ; Cluster analysis ; Complement (music) ; Band gap ; Raman scattering ; Refractive index ; Absorption (acoustics) Glass properties and applications ; Luminescence Properties of Advanced Materials ; Phase-change materials and chalcogenides
Authors: Jinxin Wang (1696837) | Yuanyuan Dou (8472507) | Jiahua Zhang (567192) | Mingyue Chen (1408384) | Zhen Song (412533) | Quanlin Liu (1412533)
Source: Figshare (article, 2025-10-08)
Field: Chemistry — Inorganic Chemistry

Deciphering theKey Factors Governing Mn<sup>4+</sup> Zero-Phonon Line Characteristicsvia Machine Learning Decoding ofHost–Mn<sup>4+</sup> Interactions Achieving wide color gamut in liquid crystal displays relies critically on narrow-band red-emitting phosphors. Mn<sup>4+</sup>-activated phosphors are promising candidates due to their sharp emission, yet modulating their zero-phonon line wavelengths remains challenging. This study employs machine learning to decode host–Mn<sup>4+</sup> interactions across 65 distinct hosts (42 fluorides, 6 fluoroxides, 17 oxides). By extracting 29 structural descriptors and leveraging a random forest regression model, we identify nine key features governing ZPL wavelengths. Electronegativity-related parameters dominate (77.83% cumulative importance), while geometric factors (bond angles, distances) also contribute significantly. The model achieves high accuracy (test MAE = 4.133 nm, <i>R</i><sup>2</sup> = 0.928), revealing that high electronegativity in secondary-coordination ions enhances Mn–ligand covalency, reducing the E<sub>g</sub> → <sup>4</sup>A<sub>2g</sub> transition energy and redshifting the emission peak wavelengths. This work identifies key design principles for Mn<sup>4+</sup>-activated fluoride, oxide, and oxyfluoride phosphors, enabling a targeted strategy for discovering next-generation narrow-band red emitters. Key (lock) ; Random forest ; Decoding methods ; Line (geometry) ; Gamut ; Bottleneck ; Energy (signal processing) Inorganic Fluorides and Related Compounds ; Lanthanide and Transition Metal Complexes ; Luminescence Properties of Advanced Materials
Authors: Cameron Chin | Matthew Peres Tino | Nasser Mohieddin Abukhdeir
Source: International Journal of Computational Materials Science and Engineering (article, 2025-12-24)
Field: Materials Science — Materials Chemistry

An unsupervised shapelet-based method for quantification of nanostructured surface imaging Computational methods to quantify structure–property relationships of materials are lacking in comparison to the widespread availability of nanostructure image-based characterization techniques. There are several existing quantification methods to characterize self-assembled nanomaterials, however many techniques are infeasible for use in processing large-scale spatial variation and/or require significant manual image pre-processing. The use of shapelet functions to computationally quantify nanostructured materials is a promising approach that is generalized for arbitrary pattern types but the current state of the art, the response distance method, requires user/researcher supervision. This need for supervision, along with the computational complexity of the method, make it infeasible for use in fully-automated large-scale computational analysis of nanostructured surface images. The development of a fully-automated unsupervised method for quantification of nanostructured surfaces would enable the development of broadly applicable structure–property relationships for nanomaterials, leveraging large amounts of previously captured data (so-called meta-analyses). In this work, these issues with the shapelet-based response distance method are resolved through the development and validation of a multi-step machine learning process which enables unsupervised (fully-automated) analysis of nanostructure imaging. The presented method removes the need for any researcher knowledge regarding order symmetries and enables truly large-scale analysis (e.g., meta-analyses) of the vast amount of nanostructure imaging data currently available. The presented unsupervised shapelet-based response distance method also results in at least an order-of-magnitude reduction in computational complexity compared to the existing method, at the cost of some degree of generality. A software implementation of the presented unsupervised method is provided in the open-source shapelets Python package. Python (programming language) ; Unsupervised learning ; Nanostructure ; Computational complexity theory ; Reduction (mathematics) ; Process (computing) ; Software ; Self-organizing map X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Block Copolymer Self-Assembly
Authors: Haoyuan Wu | Han Zheng | Yuan Pu | Bei Yu
Source: ArXiv.org (preprint, 2025-02-18)
Field: Materials Science — Materials Chemistry

Circuit Representation Learning with Masked Gate Modeling and Verilog-AIG Alignment Understanding the structure and function of circuits is crucial for electronic design automation (EDA). Circuits can be formulated as And-Inverter graphs (AIGs), enabling efficient implementation of representation learning through graph neural networks (GNNs). Masked modeling paradigms have been proven effective in graph representation learning. However, masking augmentation to original circuits will destroy their logical equivalence, which is unsuitable for circuit representation learning. Moreover, existing masked modeling paradigms often prioritize structural information at the expense of abstract information such as circuit function. To address these limitations, we introduce MGVGA, a novel constrained masked modeling paradigm incorporating masked gate modeling (MGM) and Verilog-AIG alignment (VGA). Specifically, MGM preserves logical equivalence by masking gates in the latent space rather than in the original circuits, subsequently reconstructing the attributes of these masked gates. Meanwhile, large language models (LLMs) have demonstrated an excellent understanding of the Verilog code functionality. Building upon this capability, VGA performs masking operations on original circuits and reconstructs masked gates under the constraints of equivalent Verilog codes, enabling GNNs to learn circuit functions from LLMs. We evaluate MGVGA on various logic synthesis tasks for EDA and show the superior performance of MGVGA compared to previous state-of-the-art methods. Our code is available at https://github.com/wuhy68/MGVGA. Representation (politics) ; Computer science ; Verilog ; Computer hardware ; Political science ; Field-programmable gate array Machine Learning in Materials Science ; VLSI and Analog Circuit Testing ; Integrated Circuits and Semiconductor Failure Analysis
Authors: Haiyang Li | Seong Eun Yang | Jae Sung Son
Source: Journal of Materiomics (article, 2025-11-06)
Field: Materials Science — Materials Chemistry

Computational methods for designing geometry-engineered thermoelectric devices Thermoelectric devices (TEDs) have attracted great attention due to their capability to directly convert heat into electricity or vice versa, enabling applications in waste heat recovery and active cooling. The energy conversion efficiency of TEDs is dictated by both intrinsic material properties and device-level structural features that govern heat and charge transport. Recently, the geometric design of thermoelectric (TE) legs has emerged as a transformative strategy to regulate thermal and electrical transport and ultimately improve efficiency. These geometric designs are increasingly developed through computational modeling and numerical simulations. This article provides a comprehensive overview of computational modelling methods for designing geometry-optimized TEDs, emphasizing how advanced geometric designs and modelling techniques are used to enhance energy conversion efficiency and thermal management. We cover fundamental principles, theoretical modelling, and various computational methods, including gradient-based and non-gradient-based techniques as well as machine learning approaches. Key factors influencing device performance are identified, and representative case studies illustrate the impact of innovative leg geometries on power density and reliability. Finally, we summarize current challenges and propose future research directions to advance the geometric optimization of TEDs through intelligent, robust, and efficient computational frameworks. Thermoelectric effect ; Efficient energy use ; Electricity ; Thermoelectric materials ; Thermoelectric generator ; Computational model ; Thermal ; Key (lock) ; Energy transformation Advanced Thermoelectric Materials and Devices ; Advanced Thermodynamics and Statistical Mechanics ; Thermal properties of materials
Authors: Hercules Pereira Coelho | D.F. Pessoa | IGOR JURANDIR UBALDO VIANA PEREIRA | Pedro Ferreira | Rita C. O. Sebastião | Victor de Alvarenga Oliveira
Source: Journal of Thermal Analysis and Calorimetry (article, 2025-10-01)
Field: Materials Science — Materials Chemistry

Thermal decomposition of a Brazilian laterite nickel ore: non-isothermal kinetic methods and quantitative mineralogical analysis Laterite ; Thermal decomposition ; Goethite ; Nickel ; Thermogravimetric analysis ; Activation energy ; Nucleation ; Analytical Chemistry (journal) ; Thermal analysis Thermal and Kinetic Analysis ; Extraction and Separation Processes ; Metal Extraction and Bioleaching
Authors: Zarna
D. Ponkiya (22478874) | Thanasee Thanasarnsurapong (14076226) | Adisak Boonchun (14076232) | Prafulla K. Jha (4441372)
Source: Figshare (article, 2025-10-22)
Field: Materials Science — Materials Chemistry

MachineLearning Force Field Accelerated Predictionof Low Lattice Thermal Conductivity in Strained WS<sub>2</sub> Monolayer Due to its excellent electronic properties, the WS<sub>2</sub> monolayer is promising for electronic and optoelectronic devices. However, its high lattice thermal conductivity (κ<sub><i>L</i></sub>) limits its application in thermoelectric and thermal insulation. One effective strategy to reduce κ<sub><i>L</i></sub> is the application of biaxial tensile strain. In this work, we perform an efficient approach that integrates on-the-fly machine learning force fields with the Peierls-Boltzmann transport equation to accurately and efficiently evaluate κ<sub><i>L</i></sub> of the WS<sub>2</sub> monolayer. This method reproduces density functional theory results with substantial computational savings. Extending this, we predicted the κ<sub><i>L</i></sub> of the WS<sub>2</sub> monolayer under 2–20% biaxial tensile strain, beyond which dynamical instability arises. The room temperature κ<sub><i>L</i></sub> shows a nonmonotonic decrease with strain, reaching a minimum of 11.18 W/mK at 16%. Thermal transport analysis reveals that reduced phonon velocity and strong phonon–phonon scattering at 16% strain led to such low κ<sub><i>L</i></sub>. The 16% strained WS<sub>2</sub> monolayer is confirmed to be dynamically, thermally, and mechanically stable for practical applicability. The finding suggests the 16% strained WS<sub>2</sub> monolayer as a potential energy-conversion material, offering an efficient approach to explore and engineer thermal transport in 2D materials. Monolayer ; Phonon ; Thermal conductivity ; Thermoelectric effect ; Phonon scattering ; Thermal ; Thermoelectric materials ; Scattering 2D Materials and Applications ; Thermal properties of materials ; Advanced Thermoelectric Materials and Devices
Authors: ShuWei Tang | Guowei Wang | Shulin Bai | Pengfei Zhang | Peng Ai | Song Pei | Yilong Xiao | Yujie Bao | Zhanpeng Xu | Da Wan
Source: Applied Physics Letters (article, 2025-12-29)
Field: Materials Science — Materials Chemistry

Rattling-induced anharmonicity and multi-valley enhanced thermoelectric performance in layered SmZnSbO material Layered rare-earth oxides have become promising candidates for high-performance thermoelectric (TE) materials on account of the distinctive electronic structures and anisotropic transport properties. In this work, the phonon dynamics, carrier transport, and TE performance of the layered SmZnSbO compound are comprehensively evaluated using first-principles calculations, machine learning interatomic potentials, Boltzmann transport theory, and the two-channel model. The coexistence of weak interlayer van der Waals interactions, robust intralayer covalent bonding interactions, and rattling-like vibrations of Zn atoms synergistically induces significant lattice anharmonicity, resulting in a decreased lattice thermal conductivity (0.84 W/mK@900 K within the framework of the two-channel model) for the SmZnSbO compound. The natural quantum well architecture formed by the alternative conductive [Zn2Sb2]2− layer and the insulated [Sm2O2]2+ layer endows quasi-two-dimensional transport characteristics, enabling a high carrier mobility of 34.1 cm2/Vs. Moreover, the multi-valley electronic band structure with an indirect bandgap of 0.80 eV simultaneously optimizes electrical conductivity (σ) and Seebeck coefficient (S), resulting in an enhanced power factor. Benefiting from these synergistic features, the layered SmZnSbO compound achieves optimal dimensionless figures of merit (ZTs) of 1.47 and 1.40 for the p-type and n-type doping circumstances at 900 K. The current work not only elucidates the thermal and electronic transport mechanisms for the SmZnSbO compound but also establishes a paradigm for designing high-efficiency layered oxide TE materials through combined strategies of quantum confinement, phonon engineering, and multi-valley band convergence. Seebeck coefficient ; Thermoelectric effect ; Thermoelectric materials ; Phonon ; Band gap ; Thermal conductivity ; Electron mobility ; van der Waals force ; Doping ; Electronic band structure Advanced Thermoelectric Materials and Devices ; Topological Materials and Phenomena ; Heusler alloys: electronic and magnetic properties
Authors: Xuemei Zhang | Jingyu Li | Shuping Guo | Lulu Huang | Mi Qin | Jianbo Zhu | Xiaoqiang Ma | Zhixin Hui | Yongsheng Zhang
Source: Journal of Materiomics (article, 2025-11-06)
Field: Materials Science — Materials Chemistry

Charge defect design within PbTe grain boundaries to influence the mechanical properties Defect engineering is a key strategy for optimizing the thermoelectric (TE) properties of PbTe-based materials, and investigating charged defects in PbTe grain boundaries (GBs) is crucial for understanding its thermoelectric properties. In this study, focusing the PbTe(111)<11 >/PbTe(111)< 2> GBs, we perform a high-throughput investigation of the formation energies with various charged point (intrinsic and extrinsic) defects and their effects on the mechanical properties, the shear modulus. The GBs can facilitate the formation of the charged point defects (such as , ), indicating the accumulations of the defects within the GBs region. Such defect accumulation can strongly increase the phonon scatterings. Furthermore, charge defects within Te–PbTe GBs lower the shear modulus to <33.1 GPa, due to the weakening interactions between Pb–Te bonds. The soft bonds around GBs will induce the stronger anharmonicity and further suppress the lattice thermal conductivity. Employing the machine learning method, we establish the relationship between the shear modulus and physical descriptors, which can efficiently screen or design the various purposes of PbTe compounds. Our work bridges the gap in understanding charged defects at grain boundaries in PbTe-based thermoelectric materials and giving rise to the design methodology to achieve high promising thermoelectric performance through charged defect influenced mechanical properties. • The GBs promote charged defect formation, indicating their accumulation within the Te–PbTe GBs region. • Charge defects within Te–PbTe GBs lower the shear modulus to <33.1 GPa by weakening Pb–Te bonds. • A ML model establishes physical features' relationship to shear modulus to evaluate its predictive performance. Grain boundary ; Thermoelectric effect ; Shear modulus ; Crystallographic defect ; Phonon ; Modulus ; Thermoelectric materials Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Topological Materials and Phenomena
Authors: D. Ogawa | Ryuichiro Akagi | Keitaro Sodeyama | Y. K. Takahashi
Source: Science and Technology of Advanced Materials Methods (article, 2025-09-02)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Intrinsic magnetic properties for SmFe <i> <sub>12-x</sub> T <sub>x</sub> </i> thin films via high-throughput experiments and machine learning techniques The development of next-generation permanent magnets has become critical due to the limited performance improvements in Nd-Fe-B magnets and concerns over rare earth supply. ThMn 12-type rare-earth intermetallic compounds have emerged as promising alternatives, offering superior performance and reduced rare earth content. This study systematically investigates the magnetic properties of Sm(Fe 12−xTx)-based thin films synthesized via combinatorial sputtering. Various stabilizing elements (e.g. Ti, V, Co, Cr) were analyzed to explore their effects on phase stability, saturation magnetization (μ0Ms), anisotropy field (μ0Hs), and Curie temperature (Tc). High-throughput structural and magnetic characterizations, coupled with machine learning (ML) predictions, facilitated efficient data acquisition and analysis. Experimental results reaffirmed trends such as μ0Ms enhancement with Co and phase-stabilization capabilities of Ti and V. Novel insights into additives like Cr and Ta revealed potential Tc improvements. ML regression models (Random Forest and XGBoost) identified electronegativity as a key factor influencing μ0Ms. Predictive analyses successfully estimated μ0Ms trends and ThMn 12 phase stability for unexplored compositions, enhancing the active learning framework for material discovery. This work highlights the synergy of combinatorial deposition, high-throughput data collection, and ML-assisted prediction in accelerating the exploration of magnetic materials. Future extensions to multi-element systems and other magnetic phases are expected to expedite the discovery of high-performance magnets for motors and energy applications. A novel high-throughput evaluation method reveals key intrinsic properties of ThMn<sub>12</sub>-type magnets, enabling efficient discovery and optimization of high-performance permanent magnets via combinatorial experiments and machine learning Throughput ; Computer science ; Materials science ; Nanotechnology ; Telecommunications ; Wireless Magnetic Properties of Alloys ; Magnetic properties of thin films ; Magnetic Properties and Applications
Authors: Jocelyn Ochoa | Alauna C. Wheeler | Nicholas Javier C. Licauco | Jasmine Vu | J. Emmanuel Flores-Calderón | Timothy J. Atherton | Benjamin J. Stokes | Linda S. Hirst
Source: Figshare (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Combining fluorescence microscopy and machine learning for a powerful method to analyse nematic domain growth in quantum dot nanocomposites Nanoparticle transport by a moving liquid crystal phase boundary was recently shown to facilitate the formation of nanoparticle-based porous micro-assemblies (capsules, foams, etc). A key challenge is that it can be very difficult to accurately image this process and thus to gain better insight into the mechanisms that control particle transport and assembly. The first stage in micro-assembly formation involves nematic domain nucleation and growth from an isotropic phase that includes dispersed nanoparticles. Here, we report a fluorescence method to quantify domain growth using luminescent nanoparticles (surface-modified quantum dots) to visualise domain growth following a controlled temperature quench. This technique provides enhanced resolution of domain edges when compared with birefringence imaging, especially for very small domains (&lt;2 µm). We compare manual and automated measures of domain growth using ilastik, a machine-learning-enabled segmentation tool. This high-throughput method allows us to measure the growth rate of thousands of domains simultaneously with a high degree of confidence. From these measurements, we report time dependent nematic domain size distributions and growth exponents at two different quantum dot concentrations and cooling rates. The results match well with theoretical predictions and show that under our experimental conditions, the nanoparticles do not significantly impact domain growth. Nucleation ; Birefringence ; Liquid crystal ; Quantum dot ; Domain (mathematical analysis) ; Nanoparticle ; Particle (ecology) ; Optical microscope ; Phase (matter) Quantum Dots Synthesis And Properties ; Liquid Crystal Research Advancements ; Block Copolymer Self-Assembly