jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Tianyan Xu | Yixuan Xue | Harold S. Park | Jin-Wu Jiang
Source: Journal of Applied Physics (article, 2025-03-17)
Field: Materials Science — Materials Chemistry

Phonon dispersion filter: A physics-inspired feature selection for machine learning potentials How to improve the accuracy and precision of machine learning potential functions while reducing their computational cost has long been a subject of considerable interest. In this regard, a common approach is to reduce the number of descriptors through feature selection and dimensionality reduction, thereby improving computational efficiency. In our paper, we propose a descriptor selection method based on the material’s phonon spectrum, which is called a phonon dispersion filter (PDF) method. Compared to other mathematics-based machine learning feature selection methods, the PDF method is a more physics-based feature selection approach. Taking graphene and bulk silicon as examples, we provide a detailed introduction to the screening process of the PDF method and its underlying principles. Furthermore, we test the PDF method on two types of descriptors: Atom-centered symmetry functions descriptors and smooth overlap of atomic positions descriptors. Both demonstrate promising screening results. Phonon ; Dispersion (optics) ; Feature (linguistics) ; Feature selection ; Filter (signal processing) ; Physics ; Selection (genetic algorithm) ; Artificial intelligence ; Computer science ; Statistical physics ; Condensed matter physics ; Optics ; Computer vision Machine Learning in Materials Science ; Thermography and Photoacoustic Techniques ; Nuclear Physics and Applications
Authors: Austin C. Flick | Thomas W. Colburn | A. Carbone | Francisco Barrera | Wentong Cai | Reinhold H. Dauskardt
Source:  (article, 2025-06-08)
Field: Materials Science — Materials Chemistry

Accelerating Low-Cost Perovskite Module Manufacturing with High-Throughput Open-Air Techniques Low-cost perovskite PV module manufacturing is achieved with scalable open-air Rapid Spray Plasma Processing (RSPP). RSPP employs uniform ultrasonic spray deposition accompanied by multimodal plasma curing for high-throughput perovskite fabrication in open-air. The multiple plasma curing sources of heat, reactive gaseous species, and UV light enable perovskite curing times of < 1 second and linear throughputs of up to 20 cm/s. Machine learning optimization is employed for the establishment of ideal process parameters and solution chemistries, lending to recent champion open-air perovskite device performances of 19.54% and module performances of 17.43% across 10 cm deposition lengths. Accompanying technoeconomic analysis translating lab-scale open-air perovskite module fabrication to a 100 MW pilot-scale system anticipates >60% cost reduction with open-air spray-based perovskite module manufacturing compared to conventional solution deposition and vacuum-based processes. This enables target Levelized Cost of Energy (LCOE) of <$0.03/kWh for low-cost RSPP perovskite modules at only 10 – 15-year module lifetimes, providing a promising candidate for next-generation PV. Throughput ; Perovskite (structure) ; Computer science ; Materials science ; Embedded system ; Operating system ; Engineering ; Chemical engineering ; Wireless Catalytic Processes in Materials Science
Authors: Seungjun Lee | Eng Hock Lee | Young‐Kyun Kwon | Steven J. Koester | Phaedon Avouris | Vladimir Cherkassky | J. Tersoff | Tony Low
Source: ACS Nano (article, 2025-10-22)
Field: Materials Science — Materials Chemistry

Generalized Energy Band Alignment Model for van der Waals Heterostructures with a Charge Spillage Dipole The energy band alignment at the interface of van der Waals heterostructures (vdWHs) is a key design parameter for next-generation electronic and optoelectronic devices. Although the Anderson and midgap models have been widely adopted for bulk semiconductor heterostructures, they exhibit severe limitations when applied to vdWHs, particularly for type-III systems. Based on first-principles calculations for approximately 10<sup>3</sup> vdWHs, we demonstrate that these traditional models miss a critical dipole arising from interlayer charge spillage. We introduce a generalized linear response (gLR) model that includes this dipole through a quantum capacitance term while remaining analytically compact. With only two readily computed inputs, the charge neutrality level offset and the sum of the isolated-layer bandgaps, the gLR reproduces density functional theory (DFT) band line-ups with <i>r</i><sup>2</sup> ∼ 0.9 across type-I, -II, and -III stacks. Machine learning feature analysis confirms that these two descriptors dominate the underlying physics, indicating that the model is near-minimal and broadly transferable. The gLR framework therefore provides both mechanistic insight and a fast and accurate surrogate for high-throughput screening of the vast vdW heterostructure design space. Heterojunction ; Band offset ; van der Waals force ; Dipole ; Density functional theory ; Charge (physics) ; Semiconductor ; Electronic band structure ; Quantum 2D Materials and Applications ; Superconductivity in MgB2 and Alloys ; Boron and Carbon Nanomaterials Research
Authors: Yibo Gao | Liusheng Xiao | Xiaobin Li | Ke Duan | Yonglyu He | Li Li
Source: Composites Part A Applied Science and Manufacturing (article, 2025-05-28)
Field: Materials Science — Biomaterials

A deep learning-assisted inverse design framework for nacre-like composites with excellent specific damping performance Materials science ; Composite material ; Inverse ; Mathematics ; Geometry Calcium Carbonate Crystallization and Inhibition ; Underwater Acoustics Research ; Polymer composites and self-healing
Authors: Anna Vidal-López (21098999) | Judith Mahringer (21169348) | Aleix Comas-Vives (1366110)
Source: Figshare (article, 2025-04-23)
Field: Materials Science — Materials Chemistry

Key Descriptorsof Single-Atom Catalysts Supportedon MXenes (Mo<sub>2</sub>C, Ti<sub>2</sub>C) Determining CO<sub>2</sub> Activation CO<sub>2</sub> activation is crucial to its upgrade to fuels and chemicals. In this work, we systematically studied CO<sub>2</sub> cleavage on single-atom catalysts (SACs) based on metals M = (Fe, Ru, Co, Rh, Ni, Pd, Pt, Cu, Ag, Au) supported on Mo<sub>2</sub>CO<sub><i>x</i></sub> (6/9 O ML) and Ti<sub>2</sub>CO<sub><i>x</i></sub> (7/9 O ML) MXenes via Density Functional Theory (DFT) calculations and Bader charge analysis to provide insights into the charge redistribution among the metal, MXene, interface, and CO<sub>2</sub> during the process. CO<sub>2</sub> activation involves a two-step mechanism, adsorbing at the M–MXene interface where it bends and acquires a highly anionic character and then breaks, forming CO* and O*. The energy barriers analyzed for the CO<sub>2</sub> activation on M/Mo<sub>2</sub>CO<sub><i>x</i></sub> and M/Ti<sub>2</sub>CO<sub><i>x</i></sub> surfaces show that Cu, Ni, Rh, and Pt on Mo<sub>2</sub>CO<sub><i>x</i></sub> and Cu, Ru, and Rh on Ti<sub>2</sub>CO<sub><i>x</i></sub> presented the lowest energy barriers. Comparing the two MXenes, the electrophilic nature of Mo atoms facilitates CO<sub>2</sub> cleavage, while the Ti atoms distribute charge differently, hindering the CO<sub>2</sub> activation process. The energy barriers toward CO<sub>2</sub> activation on M/Mo<sub>2</sub>CO<sub><i>x</i></sub> and M/Ti<sub>2</sub>CO<sub><i>x</i></sub> surfaces show that Cu, Ni, Rh, and Pt on Mo<sub>2</sub>CO<sub><i>x</i></sub> and Cu, Ru, and Rh on Ti<sub>2</sub>CO<sub><i>x</i></sub> presented the lowest energy barriers. Mo<sub>2</sub>CO<sub><i>x</i></sub> systems presented geometrical structures of the transition states that were more product-like aligning with the Hammond’s principle, implying exoenergetic processes and lower energy barriers in contrast to Ti<sub>2</sub>CO<sub><i>x</i></sub>. Moreover, the CO<sub>2</sub> activation on M/2D-Mo<sub>2</sub>C follows a Brønsted–Evans–Polanyi (BEP) relationship while M/2D-Ti<sub>2</sub>C breaks it, a crucial factor to identify better catalytic materials. The ExtraTreesRegressor machine learning algorithm effectively predicts adsorption and transition-state energies using a small set of descriptors. The findings underscore the importance of transition metal electronic states, charge transfer, and support structure effects for SACs on MXenes, providing valuable insights for the design of catalytic materials. This detailed analysis provides a deeper understanding of the mechanistic aspects of CO<sub>2</sub> activation, highlighting the role of single-atom metals and their interaction with metal-carbide surfaces. MXenes ; Catalysis ; Activation energy ; Density functional theory ; Electrophile ; Adsorption ; Charge (physics) ; Transition metal MXene and MAX Phase Materials ; Catalysts for Methane Reforming ; Catalytic Processes in Materials Science
Authors: Reyhaneh Sadat Motevallian | Parviz Parvin | Seyedeh Zahra Mortazavi | Ali Reyhani | Nafiseh Sadat Kalantari | Negin Nezamloo | Amir Jafargholi | Maria Polo | Mahdi Ebrahimi | Mehdi Sohrabi
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Polymers and Plastics

Alpha Particle Fluence-Dependent Physico-Chemical Properties of Makrofol Surface after Electrochemical Etching Fluence ; Etching (microfabrication) ; Isotropic etching ; Particle (ecology) ; Alpha particle ; Electrochemistry ; Materials science ; Chemical engineering ; Chemistry ; Analytical Chemistry (journal) ; Composite material ; Nuclear physics ; Physics ; Physical chemistry ; Irradiation ; Environmental chemistry ; Electrode ; Engineering Polymer Nanocomposite Synthesis and Irradiation ; Ion-surface interactions and analysis ; Electron and X-Ray Spectroscopy Techniques
Authors: Marco Franescangeli | Joaquín del Río Fernández | Daniel Mihai Toma | Pol Baños Castelló | Marc Nogueras Cervera | Enoc Martinez | Neus Vidal Oliveras | Jacopo Aguzzi
Source: Instrumentation viewpoint (article, 2025-01-01)
Field: Materials Science — Biomaterials

Ecological comparison between artificial reefs with automated procedures Waste materials from metallurgic industry were attempted to be reused for constructions. Unfortunately, white slag showed low performance. Here we present a work on how this industrial by-product was successfully used to manufacture and deploy Artificial Reefs (ARs) in coastal marine environment. The OBSEA cabled observatory cameras monitored two white slag ARs’ prototypes and a concrete biotope with cameras capturing photos each minute from 18th July 2023 to 24th October 2024. The species classification and counting were automated using machine learning algorithms, and ANalyses Of VAriance (ANOVA) were performed on AR Multiparametric Index (ARMI) values to compare ARs ecological performances. Bigger and older ARs proved to perform better. However, camera focus (i.e.; benthic vs benthic-pe-lagic realms) has to be considered in these analyses. The computation of ARMI indices from automated image processing can delineate a pipeline to inform project managers on the status of ARs marine restoration in real-time. This is in accordance with European and global policies goals, as Marine Strategy Framework Directive and United States Decade of Ocean Science for Sustainable Development. Artificial reef ; Benthic habitat ; Reef ; Benthic zone ; Pipeline (software) ; Work (physics) ; Pipeline transport Calcium Carbonate Crystallization and Inhibition ; Marine Bivalve and Aquaculture Studies ; Marine Biology and Environmental Chemistry
Authors: Xin Liu (43569) | Yongbo Zhao (3548570) | Huimin Guo (350020) | Changgong Meng (1448854)
Source: Figshare (article, 2025-06-06)
Field: Chemistry — Inorganic Chemistry

A Machine-Learning-BasedInvestigation on the Formationand Evolution of Silicic Acid Oligomers Structurally Analogous toZeolite Building Units A machine-learning (ML) based model that is capable of predicting the formation free energy of a silicic acid oligomer (OSA) with its SMILES string was developed and was used to investigate the formation and evolution of OSAs structurally analogous to zeolite secondary building units and composite building units (S/CBU-OSAs) to understand the mechanistic pathways for the integration of OSAs into zeolites. It was shown that most intermolecular condensation (IEC) and intramolecular condensation (IAC) forming the first cycle in OSAs are exergonic and are the major pathways for OSAs evolution. The exergonicity of an IAC depends strongly on the degree of condensation and distortion. The formation of an S/CBU-OSA is more exergonic than the competing formation of a non-S/CBU-OSA, and the exergonicity would be even greater if it were from a non-S/CBU-OSA. The work highlights the feasibility of using ML-based models to predict reaction thermodynamics and pinpoints the superior thermodynamic stability of S/CBU-OSAs. Exergonic reaction ; Zeolite ; Condensation ; Oligomer ; Silicic acid ; Intramolecular force ; Polymerization ; Degree of polymerization Zeolite Catalysis and Synthesis ; Catalysis and Oxidation Reactions ; Machine Learning in Materials Science
Authors: Xin Liu | Yongbo Zhao | Huimin Guo | Changgong Meng
Source: The Journal of Physical Chemistry Letters (article, 2025-06-05)
Field: Chemistry — Inorganic Chemistry

A Machine-Learning-Based Investigation on the Formation and Evolution of Silicic Acid Oligomers Structurally Analogous to Zeolite Building Units A machine-learning (ML) based model that is capable of predicting the formation free energy of a silicic acid oligomer (OSA) with its SMILES string was developed and was used to investigate the formation and evolution of OSAs structurally analogous to zeolite secondary building units and composite building units (S/CBU-OSAs) to understand the mechanistic pathways for the integration of OSAs into zeolites. It was shown that most intermolecular condensation (IEC) and intramolecular condensation (IAC) forming the first cycle in OSAs are exergonic and are the major pathways for OSAs evolution. The exergonicity of an IAC depends strongly on the degree of condensation and distortion. The formation of an S/CBU-OSA is more exergonic than the competing formation of a non-S/CBU-OSA, and the exergonicity would be even greater if it were from a non-S/CBU-OSA. The work highlights the feasibility of using ML-based models to predict reaction thermodynamics and pinpoints the superior thermodynamic stability of S/CBU-OSAs. Silicic acid ; Zeolite ; Silicic ; Chemistry ; Chemical engineering ; Materials science ; Organic chemistry ; Engineering ; Geology ; Geochemistry ; Catalysis Zeolite Catalysis and Synthesis ; Catalysis and Oxidation Reactions ; Mesoporous Materials and Catalysis
Authors: Y. М. Yelekeyev | Boris Stepanov
Source: NNC RK Bulletin (article, 2025-04-01)
Field: Materials Science — Materials Chemistry

MATHEMATICAL MODELING OF SAFETY CONTROL PROCESSES AT NUCLEAR POWER PLANTS This paper examines the use of mathematical modeling to enhance the safety of nuclear power plants (NPPs). The study demonstrates that reaction time plays a crucial role in accident prevention: if an operator takes action within 30 seconds, the probability of preventing an accident is 95%, whereas a 5-minute delay reduces this probability to 30%. The impact of preventive maintenance and system redundancy on reliability is also analyzed. Without maintenance, the probability of equipment failure after 1000 hours of operation reaches 40%, whereas regular inspections reduce this to 15%, and redundancy further lowers it to 8%. Additionally, cascading failures were studied, showing that as the number of interdependent components increases from 3 to 10, the risk of total system failure rises from 15% to 80%. The paper provides practical recommendations for improving NPP reliability, including the automation of monitoring, the implementation of predictive algorithms, and the use of machine learning for failure forecasting. Nuclear power ; Control (management) ; Nuclear engineering ; Environmental science ; Computer science ; Engineering ; Physics ; Nuclear physics ; Artificial intelligence Graphite, nuclear technology, radiation studies ; Nuclear reactor physics and engineering ; Risk and Safety Analysis
Authors: K. A. Azuddin | Ahmad Kadri Junoh | Ammar Zakaria | M T A Rahman | Nik Mohd Izham Mohamed Nor | Hiromitsu Nishizaki | Nurul Farizah Azuddin | Latiffah Zakaria | ‪Mohd Yusoff Mashor | Paul Tersoo Terna | Denis Eka Cahyani
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Linear Binary Pattern-Based Morphological Identification for Fusarium Macroconidia Images Fusarium ; Identification (biology) ; Conidium ; Binary number ; Artificial intelligence ; Biology ; Pattern recognition (psychology) ; Computer science ; Botany ; Mathematics ; Arithmetic Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI
Authors: Yanfei Jia | Dongbo Wang | Xu Guo
Source: Mathematics (article, 2025-11-02)
Field: Materials Science — Materials Chemistry

High-Dimensional Numerical Methods for Nonlocal Models Nonlocal models offer a unified framework for describing long-range spatial interactions and temporal memory effects. The review briefly outlines several representative physical problems, including anomalous diffusion, material fracture, viscoelastic wave propagation, and electromagnetic scattering, to illustrate the broad applicability of nonlocal systems. However, the intrinsic global coupling and historical dependence of these models introduce significant computational challenges, particularly in high-dimensional settings. From the perspective of algorithmic strategies, the review systematically summarizes high-dimensional numerical methods applicable to nonlocal equations, emphasizing core approaches for overcoming the curse of dimensionality, such as structured solution frameworks based on FFT, spectral methods, probabilistic sampling, physics-informed neural networks, and asymptotically compatible schemes. By integrating recent advances and common computational principles, the review establishes a dual “problem review + method review” structure that provides a systematic perspective and valuable reference for the modeling and high-dimensional numerical simulation of nonlocal systems. Perspective (graphical) ; Probabilistic logic ; Numerical analysis ; Coupling (piping) ; Curse of dimensionality ; Core (optical fiber) ; Viscoelasticity Nonlocal and gradient elasticity in micro/nano structures ; Microstructure and mechanical properties ; Numerical methods in engineering
Authors: Chen, Haotian | Huang, Chenyang | Rodríguez, Alexander | Mistry, Aashutosh | Viswanathan, Venkatasubramanian
Source: ArXiv.org (article, 2025-11-07)
Field: Materials Science — Materials Chemistry

Differentiable Electrochemistry: A paradigm for uncovering hidden physical phenomena in electrochemical systems Despite the long history of electrochemistry, there is a lack of quantitative algorithms that rigorously correlate experiment with theory. Electrochemical modeling has had advanced across empirical, analytical, numerical, and data-driven paradigms. Data-driven machine learning and physics based electrochemical modeling, however, have not been explicitly linked. Here we introduce Differentiable Electrochemistry, a mew paradigm in electrochemical modeling that integrates thermodynamics, kinetics and mass transport with differentiable programming enabled by automatic differentiation. By making the entire electrochemical simulation end-to-end differentiable, this framework enables gradient-based optimization for mechanistic discovery from experimental and simulation data, achieving approximately one to two orders of improvement over gradient-free methods. We develop a rich repository of differentiable simulators across diverse mechanisms, and apply Differentiable Electrochemistry to bottleneck problems in kinetic analysis. Specifically, Differentiable Electrochemistry advances beyond Tafel and Nicholson method by removing several limitations including Tafel region selection, and identifies the electron transfer mechanism in Li metal electrodeposition/stripping by parameterizing the full Marcus-Hush-Chidsey formalism. In addition, Differentiable Electrochemistry interprets Operando X-ray measurements in concentrated electrolyte by coupling concentration and velocity theories. This framework resolves ambiguity when multiple electrochemical theories intertwine, and establishes a physics-consistent and data-efficient foundation for predictive electrochemical modeling. Differentiable function ; Tafel equation ; Electrochemistry ; Bottleneck ; Electrolyte ; Representation (politics) ; Ambiguity ; Electrochemical kinetics Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Electrocatalysts for Energy Conversion
Authors: Sasan Daryanavard Chounchenani | Morteza Mehrabian | Javad Rezapour
Source: Fuel (article, 2025-08-05)
Field: Materials Science — Materials Chemistry

Optimization of the steam-to-fuel ratio in an arc-shaped solid oxide fuel cell fed by biogas using artificial neural networks • The influence of biogas-fed SOFC curvature on power density and performance. • Enhanced reforming reactions through curvature-induced effects. • Comparison of arc-shaped and straight biogas-fed SOFCs by detailed simulations. • Optimal S/C ratio as a function of bending angle and voltage using an ANN. Biogas-fed SOFCs present a sustainable energy solution, but their performance optimization depends on overcoming key operational challenges. This study examines channel curvature and the steam-to-carbon (S/C) ratio, critical for preventing carbon deposition and enhancing system efficiency. Three-dimensional simulations reveal that channel curvature improves system performance by up to 12.07% under carbon-safe conditions. Curvature enhances the direct reforming process in the anode support layer (ASL) by generating a perpendicular velocity component and applying centrifugal force. This accelerates reforming reactions, increases hydrogen production, and enhances the fuel cell’s electrochemical performance and power density. The bending impact factor increases with bending angle but decreases with higher voltage and S/C ratios. Conversely, the optimal S/C ratio rises with voltage but declines with greater curvature. In this study, an artificial neural network is used to establish a relationship for the optimal S/C ratio as a function of voltage and bending angle. Solid fuel ; Fuel cells ; Biogas ; Solid oxide fuel cell ; Arc (geometry) ; Chemical engineering ; Oxide ; Materials science ; Artificial neural network ; Process engineering ; Environmental science ; Chemistry ; Waste management ; Metallurgy ; Computer science ; Engineering ; Mechanical engineering ; Organic chemistry ; Combustion ; Anode ; Artificial intelligence ; Electrode Advancements in Solid Oxide Fuel Cells ; Fuel Cells and Related Materials ; Catalytic Processes in Materials Science