jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xia Jiang | Chunlin Xu | Dong Wang
Source: The Journal of Chemical Physics (article, 2026-03-05)
Field: Materials Science — Materials Chemistry

Improved Debye–Callaway model for calculating the lattice thermal conductivity of anisotropic two-dimensional crystals Thermal management in two-dimensional (2D) materials is pivotal, given their extensive technological utility in miniaturized devices. However, an expedient yet precise method for predicting the thermal transport properties of 2D materials remains to be established. In this study, we derive analytical formulas for calculating the scattering rate of three-phonon Umklapp processes, specifically tailored to 2D materials, based on the continuous elasticity assumption, quasi-harmonic approximation, and central force approximation. These formulas enable the construction of a refined Debye-Callaway model that incorporates the effects of both three-phonon Umklapp scattering and boundary scattering, eliminating the need for any fitting parameters. Notably, we explicitly disentangle the contributions of optical phonons, treating them within a framework analogous to the Einstein model. This improved model facilitates efficient and accurate predictions of the anisotropic lattice thermal conductivities in 2D materials. The results generated by our model for 12 2D crystals show excellent agreement with those from fully first-principles calculations reported in the literature. In particular, this study clarifies how the relaxation time fitting parameter adopted in previous work depends on the crystal structure and dimensional characteristics of the materials. It also provides an understanding of the logarithmic divergence of lattice thermal conductivity with respect to the size of 2D crystals within a framework involving only three-phonon scattering processes and Debye approximation. Owing to its low computational cost and high prediction precision, this model can serve as a valuable tool for high-throughput screening and machine learning applications in the identification of 2D materials with tailored thermal conductivity. Thermal conductivity ; Anisotropy ; Scattering ; Lattice (music) ; Logarithm ; Thermal ; Boundary value problem ; Debye model ; Phonon Thermal properties of materials ; Advanced Thermoelectric Materials and Devices ; Thermography and Photoacoustic Techniques
Authors: Mingyang Zhao | Zhisen Feng | Bin Lei | Xiaoqi Chen | Zhenfeng Tong
Source: Computational Materials Science (article, 2026-01-09)
Field: Materials Science — Materials Chemistry

Physically interpretable models for predicting lattice parameters, tunnel geometry, and symmetry in hollandite-type materials Lattice constant ; Lattice (music) ; Lattice energy ; Tetragonal crystal system ; Polynomial regression ; Linear regression ; Multivariate statistics ; Bayesian multivariate linear regression ; Bottleneck ; Monoclinic crystal system Nuclear materials and radiation effects ; Advanced Condensed Matter Physics ; Machine Learning in Materials Science
Authors: Zhenxu Hao | Z. Li | Xing Li | Yanfang Gao | Lijun Li | Ridong He
Source: Journal of Power Sources (article, 2026-01-07)
Field: Materials Science — Materials Chemistry

Multi-physics coupling model for molten hydroxide direct carbon fuel cells: Performance prediction and validation under dual gradient distribution of concentration and temperature Isothermal process ; Power density ; Electrolyte ; Voltage ; Multiphysics ; Coupling (piping) ; Thermal ; Cascade ; Homogenization (climate) Advancements in Solid Oxide Fuel Cells ; Fuel Cells and Related Materials ; Electrocatalysts for Energy Conversion
Authors: Porhouy Minh | Sapna Sarupria
Source: arXiv (Cornell University) (preprint, 2026-02-05)
Field: Materials Science — Materials Chemistry

Path Sampling for Rare Events Boosted by Machine Learning The study by Jung et al. (Jung H, Covino R, Arjun A, et al., Nat Comput Sci. 3:334-345 (2023)) introduced Artificial Intelligence for Molecular Mechanism Discovery (AIMMD), a novel sampling algorithm that integrates machine learning to enhance the efficiency of transition path sampling (TPS). By enabling on-the-fly estimation of the committor probability and simultaneously deriving a human-interpretable reaction coordinate, AIMMD offers a robust framework for elucidating the mechanistic pathways of complex molecular processes. This commentary provides a discussion and critical analysis of the core AIMMD framework, explores its recent extensions, and offers an assessment of the method's potential impact and limitations. Rare events ; Sampling (signal processing) ; Path (computing) ; Mechanism (biology) ; Core (optical fiber) ; Importance sampling Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Gaussian Processes and Bayesian Inference
Authors: Sadaf Noreen | Mamduh J. Aljaafreh | Ashour M. Ahmed
Source: The European Physical Journal Plus (article, 2026-03-05)
Field: Materials Science — Materials Chemistry

Predicting detonation pressure of energetic organic compounds using machine learning: a descriptor-based approach combining connectivity and electro-topological features Detonation ; Ranging ; Range (aeronautics) ; Key (lock) ; Dimension (graph theory) ; Random forest ; Current (fluid) ; Feature (linguistics) Machine Learning in Materials Science ; Energetic Materials and Combustion ; Crystallography and molecular interactions
Authors: Peiyuan Su | Jihao Zhang | Hao Wang | Yifeng Jiang | Sharmistha Paul Dutta | Mengxu Li | Hazem Yousef | P. Zalden | Kai Zhang | Ruixue Zhu | Xuan Liu | Yingqi Wang | Sophie E. Canton | Diana Bregenholt Jakobsen | Doriana Vinci | Wenkai Zhang | Jinggang Lan | Tsu‐Chien Weng | Wenge Yang | Michaël Wulff | Christopher J. Milne | Dmitry Khakhulin | Qingyu Kong
Source: Nature Communications (article, 2026-02-09)
Field: Materials Science — Materials Chemistry

Ultrafast solvent-modulated roaming mechanism in bromoform revealed by femtosecond X-ray solution scattering Roaming-mediated isomerization is a universal reaction mechanism in photochemistry, yet solvent-dependent pathways of roaming intermediates remain poorly understood, particularly for environmentally relevant halogen compounds involved in ozone depletion. Here, using femtosecond time-resolved X-ray solution scattering, we resolve the solvent-dependent roaming dynamics of CHBr<sub>3</sub> in methanol and methylcyclohexane. By combining multi-method experimental analysis, machine learning-assisted ab initio molecular dynamics simulations, and density functional theory calculations, we uncover distinct solvent-steered reaction pathways. In methanol, roaming enhances solute-solvent interactions, leading to solvolysis before a stable isomer forms. In methylcyclohexane, roaming facilitates isomerization to a long-lived iso-CHBr<sub>2</sub>-Br product. Direct dissociation into CHBr<sub>2</sub> + Br competes with both pathways in either solvent. By tracking bond-length oscillations and angular dynamics in real time, we visualize how the condensed-phase environment governs the branching ratio between competing pathways. Our findings establish solute-solvent interactions as key factors controlling roaming-mediated reactions in CHBr<sub>3</sub>, with broad implications for photochemical outcomes in solution. Roaming ; Femtosecond ; Isomerization ; Dissociation (chemistry) ; Density functional theory ; Reaction dynamics ; Ultrashort pulse ; Ab initio ; Reaction mechanism Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Photochromic and Fluorescence Chemistry
Authors: Oscar Bengtsson | Daniel Hewitt | Indranil Saha | Misbah Sarwar | Nicola Collis | Andreas Erlebach | Lukáš Grajciar | Christopher J. Heard | Ben Slater
Source: ChemRxiv (article, 2026-02-03)
Field: Materials Science — Materials Chemistry

Prediction of aluminium distributions in zeolites using basin-hopping and machine learning potentials In this work, we present a systematic approach to predicting the structures of three sodiated aluminosilicate zeolites (Chabazite, Mordenite and RHO) by combining a state-of-the-art neural network potential with a custom basin-hopping algorithm. We show that the lowest energy, thermally accessible configurations are rare in comparison to the number of possible structures, accounting for a few percent of viable structures. For Chabazite and Mordenite, it is found that there is a tendency for aluminium to preferentially aggregate with 2 or 3 silicon atoms separating aluminium atoms with a strong disincentive to form Al-O-Si-O-Al linkages. However, clear and contrary topological preferences are observed in RHO where Al-O-Si-O-Al linkages are strongly favoured at moderate Al concentrations. Crucially, randomly generated or non-basin hopping approaches to structure generation results in configurations that are highly metastable and activated with respect to minimum energy structures. Using such activated structures for modelling catalysis processes could potentially yield qualitatively incorrect predictions of reactivity and transport properties. Aluminium ; Metastability ; Aluminosilicate ; Mordenite ; Chabazite ; Artificial neural network ; Yield (engineering) Machine Learning in Materials Science ; Zeolite Catalysis and Synthesis ; Catalysis and Oxidation Reactions
Authors: Yuksel Cakir | Özgen Ü. Çolak
Source: Polymer Engineering and Science (article, 2026-03-07)
Field: Materials Science — Materials Chemistry

Hybrid Machine Learning Approach for Predicting Stress Relaxation Behavior of Epoxy Resins ABSTRACT Stress relaxation, the gradual reduction of stress under constant strain, is a key parameter in assessing long‐term mechanical performance of polymers and polymer matrix composites. Accurate prediction of relaxation behavior is vital in structural engineering and materials science. This study applies machine learning (ML) methods, specifically multilayer perceptron (MLP) networks and nonlinear regression, to model stress relaxation behavior of epoxy resin (Araldite LY 564) as a function of strain, temperature, and time. Also, a hybrid model is introduced, combining nonlinear regression to describe the overall exponential decay with an MLP network to capture residual deviations. Experimental relaxation data were supplemented with interpolated artificial data to enhance model training. Results show that both regression and MLP approaches predict relaxation behavior effectively, while the hybrid model demonstrates superior accuracy and robustness, making it a promising tool for long‐term material performance prediction. Augmenting the training set with artificial data improves long‐term prediction performance and produces results more consistent with the experimental data. Relaxation (psychology) ; Artificial neural network ; Epoxy ; Nonlinear system ; Multilayer perceptron ; Nonlinear regression ; Stress relaxation ; Perceptron ; Residual ; Stress (linguistics) Machine Learning in Materials Science ; Mechanical Behavior of Composites ; Epoxy Resin Curing Processes
Authors: Maninder Kaur | Nicolas T. Sandino | Jason P. Terry | Mahdi Ghafariasl | Yohannes Habtegiorgis Abate
Source: Advanced Optical Materials (article, 2026-01-22)
Field: Materials Science — Materials Chemistry

Excitonic Landscapes in Monolayer Lateral Heterostructures Revealed by Unsupervised Machine Learning Abstract Two‐dimensional (2D) in‐plane heterostructures including compositionally graded alloys and lateral heterostructures with defined interfaces display rich optoelectronic properties and offer versatile platforms to explore one‐dimensional (1D) interface physics and many‐body interaction effects. Graded Mo x W 1 − x S 2 alloys show smooth spatial variations in composition and strain that continuously tune excitonic emission, while MoS 2 –WS 2 lateral heterostructures contain atomically sharp interfaces supporting 1D excitonic phenomena. These single‐layer systems combine tunable optical and electronic properties with potential for stable, high‐performance optoelectronic devices. Hyperspectral and nano‐resolved photoluminescence (PL) imaging enable spatial mapping of optical features along with local variations in composition, strain, and defects, but manual interpretation of such large datasets is slow and subjective. Here, a fast and scalable unsupervised machine‐learning (ML) framework is introduced to extract quantitative and interpretable information from hyperspectral PL datasets of graded Mo x W 1 − x S 2 alloys and MoS 2 –WS 2 heterostructures. Combining principal‐component analysis (PCA), t‐distributed stochastic neighbor embedding (t‐SNE), and density‐based spatial clustering of applications with noise (DBSCAN), spectrally distinct domains associated with composition, strain, and defect variations are uncovered. Decomposition of representative spectra reveals multiple emission species, including band‐edge excitons and defect‐related transitions, demonstrating that ML‐driven analysis provides a robust and automated route to interpret rich optical properties of 2D materials. Heterojunction ; Exciton ; Photoluminescence ; Cluster analysis ; Hyperspectral imaging ; Monolayer ; Semiconductor ; Anisotropy 2D Materials and Applications ; Electronic and Structural Properties of Oxides ; Nanowire Synthesis and Applications
Authors: Jussi Isokuortti | Lea Nienhaus
Source: Chemical Science (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Controlling the fate of two triplet states: solid-state annihilator design for photon upconversion Photon upconversion, the process of converting low-energy light into higher-energy photons, offers transformative opportunities for energy conversion and optoelectronics. In this Perspective, we examine the molecular design principles of solid-state annihilators, emphasizing a unified excitonic framework that links upconversion with related technologies such as organic light-emitting diodes and singlet fission. We discuss key challenges, including controlling triplet exciton diffusion, maximizing singlet formation efficiency, and suppressing loss channels such as excimer and trap-state formation. Strategies that leverage precise intermolecular packing and tailored electronic coupling are highlighted as critical levers for dictating excited-state dynamics and optimizing photophysical outcomes. Looking forward, we propose that future breakthroughs will surpass the "solution-first approach" and hinge on integrating data-driven machine learning approaches with phonon engineering to rationally design materials with enhanced light emission and energy conversion efficiency. This framework provides guiding principles for the next generation of high-performance solid-state upconversion systems. Photon upconversion ; Annihilator ; Singlet state ; Photon ; Perspective (graphical) ; Two-photon excitation microscopy Luminescence and Fluorescent Materials ; Luminescence Properties of Advanced Materials ; Nonlinear Optical Materials Studies
Authors: Xuemei Yu
Source: Microchemical Journal (article, 2026-01-25)
Field: Materials Science — Materials Chemistry

Artificial intelligence assisted chemometric analysis and microchemical characterization of sustainable performance enhancing sports materials Process (computing) ; Embodied energy ; Sustainability ; Efficient energy use ; Characterization (materials science) ; Environmentally friendly ; Energy consumption ; Raw material ; Performance indicator Machine Learning in Materials Science ; Polymer Foaming and Composites ; Polymer crystallization and properties
Authors: Yanxue Chao | Jiani Han | Yaodong Yu | Jierui Zhou | Jianping Lai | Lei Wang
Source: ACS Catalysis (article, 2026-02-12)
Field: Materials Science — Materials Chemistry

Function-Oriented Design of Metal Electrocatalytic Materials: From Reaction Mechanisms to Active Site Engineering Electrocatalysis represents a core technology for the conversion and storage of renewable energy. Traditional research has relied heavily on the trial-and-error approach or simple modification of known materials, lacking a guiding framework that can systematically link reaction mechanisms with material design. To address this gap, this review aims to establish a unified, function-oriented integrated design framework. This framework adheres to the core logic of “mechanism-driven demand─function definition─site engineering”: starting from the intrinsic mechanism of the target reaction, it identifies the rate-determining steps and key intermediates; then reversely defines the electronic structure, geometric configuration, and dynamic response characteristics required of active sites; and ultimately guides the rational construction of catalytic materials. Organizing the discourse around the core functions of active sites, this paper systematically analyzes a series of important systems, ranging from hydrogen/oxygen reactions and carbon–nitrogen cycles to the electrocatalytic transformation of organic molecules. By clarifying the inherent correlations between structure, function, and performance across different systems, the review summarizes universal design principles for overcoming the performance limits of conventional materials through the synergistic optimization of multidimensional descriptors and the regulation of dynamic active states. Finally, this paper highlights several emerging research directions, including the rational programming of dynamic active sites, machine learning-assisted inverse design, and the systematic regulation of interfacial microenvironments. It is anticipated that this work will provide a structured methodological guide for advancing electrocatalytic materials from relatively fragmented empirical exploration toward more systematic, rational construction. Rational design ; Electrocatalyst ; Mechanism (biology) ; Active site ; Work (physics) ; Core (optical fiber) ; Key (lock) Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; CO2 Reduction Techniques and Catalysts
Authors: Emmanuel Yankson | Madison T Shaffer | Thomas F. Guarr | David P. Hickey
Source: ChemRxiv (article, 2026-02-05)
Field: Chemistry — Electrochemistry

Steric and Electronic Effects Govern Electron Transfer Rates in Pyridinium Electrolytes N-substituted pyridiniums are promising candidates for electrochemical energy storage applications due to their high solubility and low redox potentials; however, they often exhibit heterogeneous electron transfer (ET) kinetics that are insufficient for high power applications.Rational molecular design to improve ET rates remains challenging because the quantitative relationships between substituent-level structure and heterogeneous ET kinetics are under described for substituted pyridinium systems.While first-principles ET models provide mechanistic descriptors, they do not provide direct mapping of molecular substituents onto kinetic parameters.Here, we combine electrochemical characterization of a series of 2,6-dialkyl-4arylpyridinium derivatives with supervised machine learning to develop a predictive structurekinetics model for the heterogeneous ET rate constant (k 0 ).The resulting model relates k 0 to the percent buried volume of the pyridinium nitrogen atom (VB,N) and the Hammett m parameter, which yielded a strong performance for both the training set (13 compounds, R 2 = 0.85) and external validation set (four compounds R 2 = 0.92, Q 2 = 0.84).Notably, the model accurately predicts k 0 for a 2,6-diethylpyridium derivative that lies outside the structural and kinetic space of the training set.Electrochemical data were further analyzed within the Marcus-Hush-Levich framework to extract mechanistic parameters.Interpreting the machine-learning-derived structural descriptors through this first-principles model identifies outer-sphere reorganization energy (e.g., solvent polarization and ion pairing energy) as the primary contributor to the observed structurekinetics relationships.By connecting data-driven models with ET theory, this work provides a physically grounded framework for guiding molecular design to tune heterogeneous ET kinetics in organic redox systems. Steric effects ; Electronic effect ; Electron transfer ; Pyridinium ; Electrolyte ; Electronic structure Electrochemical Analysis and Applications ; Advanced Battery Materials and Technologies ; Spectroscopy and Quantum Chemical Studies
Authors: Oscar Bengtsson | Daniel Hewitt | Indranil Saha | Misbah Sarwar | Nicola Collis | Andreas Erlebach | Lukáš Grajciar | Christopher J. Heard | Ben Slater
Source: ChemRxiv (article, 2026-02-05)
Field: Materials Science — Materials Chemistry

Prediction of aluminium distributions in zeolites using basin-hopping and machine learning potentials In this work, we present a systematic approach to predicting the structures of three sodiated aluminosilicate zeolites (Chabazite, Mordenite and RHO) by combining a state-of-the-art neural network potential with a custom basin-hopping algorithm. We show that the lowest energy, thermally accessible configurations are rare in comparison to the number of possible structures, accounting for a few percent of viable structures. For Chabazite and Mordenite, it is found that there is a tendency for aluminium to preferentially aggregate with 2 or 3 silicon atoms separating aluminium atoms with a strong disincentive to form Al-O-Si-O-Al linkages. However, clear and contrary topological preferences are observed in RHO where Al-O-Si-O-Al linkages are strongly favoured at moderate Al concentrations. Crucially, randomly generated or non-basin hopping approaches to structure generation results in configurations that are highly metastable and activated with respect to minimum energy structures. Using such activated structures for modelling catalysis processes could potentially yield qualitatively incorrect predictions of reactivity and transport properties. Aluminium ; Metastability ; Aluminosilicate ; Mordenite ; Chabazite ; Artificial neural network ; Yield (engineering) Machine Learning in Materials Science ; Zeolite Catalysis and Synthesis ; Catalysis and Oxidation Reactions
Authors: Ahmet  Kaan Toksoy | Hikmet  Sinan Ustun | Musa  Talha Izgi | Onur Altuntaş
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Depth of Penetration Estimation of Armor Ceramics Under Different Ballistic Threats by Using Machine Learning Algorithms Projectile ; Decision tree ; Residual ; Random forest ; Artificial neural network ; Armour ; Penetration (warfare) High-Velocity Impact and Material Behavior ; Boron and Carbon Nanomaterials Research ; Machine Learning in Materials Science
Authors: Łukasz Śladewski | Jaroslaw Milewski | K. Świrski | Aliaksandr Martsinchyk
Source: International Journal of Advances in Signal and Image Sciences (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Modelling Architecture Parameters Of A Solid Oxide Fuel Cell Using A Hybrid-Artificial Neural Network An Artificial Neural Network (ANN) can predict and be applied to simulate an object’s behavior with no algorithmic solution merely by utilizing available experimental data. The error back-propagation algorithm was used for an ANN training procedure. There are SOFC features that cannot be expressed in numerical form or where numerical expression is difficult to obtain, i.e. electrolyte type, anode type, cathode type etc. In those situations, a hybrid model can be applied. The hybrid model (H-ANN) consists of the ANN model and mathematical expressions. The H-ANN can predict cell voltage with knowledge of minimum physical factors. Artificial neural network ; Solid oxide fuel cell ; Anode ; Cathode ; Fuel cells ; Hybrid neural network ; Voltage ; Hybrid system Advancements in Solid Oxide Fuel Cells ; Machine Learning in Materials Science ; Neural Networks and Applications