jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Saifian Farooq Bhat | M. Koch | Sachin Negi | Alexander Kubanek | Vibhav Bharadwaj
Source: ArXiv.org (article, 2026-01-28)
Field: Materials Science — Materials Chemistry

A Hybrid Jump-Diffusion Model for Coherent Optical Control of Quantum Emitters in hBN Hexagonal boron nitride (hBN) has emerged as a promising two-dimensional host for stable single-photon emission owing to its wide bandgap, high photostability, and compatibility with nanophotonic integration. We present a simulation-based study of temperature-dependent spectral dynamics and optical coherence in a mechanically decoupled quantum emitter in hBN. Employing a hybrid stochastic framework that combines Ornstein--Uhlenbeck detuning fluctuations with temperature-dependent, Gaussian-distributed discrete frequency jumps, motivated by experimentally observed spectral diffusion and blinking, we reproduce the measured evolution of inhomogeneous linewidth broadening and the progressive degradation of photon coherence across the relevant cryogenic range (5-30K). The model captures phonon-related spectral diffusion with a cubic temperature dependence and the onset of jump-like spectral instabilities at higher temperatures. By calibrating the hybrid diffusion, jump parameters to the experimentally measured full width at half maximum (FWHM) of the emission line and analyzing the second-order correlation function $g^{(2)}(τ)$ under resonant driving, we establish a unified phenomenological description that links stochastic detuning dynamics to the decay of optical coherence in a resonantly driven emitter. Analysis of $g^{(2)}(τ)$ under resonant driving reveals an additional dephasing rate $γ_{\mathrm{sd+j}}$ that rises monotonically with temperature and drive strength, leading to a predicted critical crossover to overdamped dynamics at $T_{\mathrm{crit}} \approx 25.91$~K. This hybrid framework provides a quantitative connection between accessible spectroscopic observables and the dominant noise mechanisms limiting coherent optical control in mechanically decoupled quantum emitters, exemplified in hBN and generalizable to similar emitters in other materials. Dephasing ; Laser linewidth ; Coherence (philosophical gambling strategy) ; Photon ; Observable ; Quantum ; Supercontinuum ; Spontaneous emission ; Mesoscopic physics Diamond and Carbon-based Materials Research ; Graphene research and applications ; Advanced Fiber Laser Technologies
Authors: B. Luo | Mantang Chen | Ziqian Q. Wang | Zi’an Xia | Yuxin Zhai | Linhao Jia | Hang Cui | Yuanjun Song | Qihua Xiong | J. Xiao | Ryuichi Shindou | Zhida Song | X. C. Xie | Pingfan Gu | Yu Ye | Zheng Han | D. M. Chen | Shaomian Qi | Jianhao Chen
Source: Nature Communications (article, 2026-03-21)
Field: Materials Science — Materials Chemistry

Flat band induced quasi-one-dimensional magnon transport in a two-dimensional spin lattice Flat bands in electronic systems have been widely explored for their ability to enhance electronic correlations and to induce exotic quantum states. However, the impact of flat magnon bands in spintronic devices remains largely unexplored. In this study, we experimentally demonstrate that intrinsic magnonic flat bands in the two-dimensional antiferromagnetic insulator CrOCl enable long-range, quasi-one-dimensional magnon transport within a strongly coupled two-dimensional spin lattice. We observed a magnon diffusion length exceeding 7 μm along the a-axis of CrOCl, rivaling the performance of low-damping three-dimensional magnets like α-Fe<sub>2</sub>O<sub>3</sub> and yttrium-iron garnet (YIG). In contrast, magnon diffusion along the b-axis of CrOCl is significantly suppressed, setting it apart from α-Fe<sub>2</sub>O<sub>3</sub> and YIG. Theoretical calculations attribute this quasi-1D spin transport to a nearly flat magnonic band along the b-axis and a highly dispersive band along the a-axis of the material, generating anisotropic magnon diffusion lengths agreeing with our experimental data. These results highlight the potential of flat magnon bands in van der Waals spin systems for the future development of energy-efficient and compact integrated spintronic devices. Magnon ; Spintronics ; van der Waals force ; Anisotropy ; Antiferromagnetism ; Spin (aerodynamics) ; Spin diffusion ; Ferromagnetism ; Spin wave 2D Materials and Applications ; Heusler alloys: electronic and magnetic properties ; Magnetic properties of thin films
Authors: Katharina Hopf | Juan Yang
Source: ArXiv.org (article, 2026-02-25)
Field: Materials Science — Materials Chemistry

Renormalised solutions to reaction-diffusion systems with interface conditions: Global existence and weak-strong uniqueness We introduce an extension of the concept of renormalised solutions for entropy-dissipating reaction-diffusion systems due to J. Fischer (Arch. Ration. Mech. Anal. 218, 2015) to systems coupled by nonlinear interface conditions. For this notion of solution, we establish global existence as well as a weak-strong stability estimate. Our framework allows to handle entropy-dissipating interfacial transmission rates without growth restrictions, including power-law nonlinearities as arising in the thermodynamic modelling of dissipative bulk-interface systems via generalised gradient structures. Our analysis relies on suitable extensions of the species' densities across the interface as well as on a non-local truncated variant of the relative entropy. Uniqueness ; Dissipative system ; Stability (learning theory) ; Interface (matter) ; Nonlinear system ; Extension (predicate logic) ; Work (physics) Solidification and crystal growth phenomena ; Thermoelastic and Magnetoelastic Phenomena ; Stability and Controllability of Differential Equations
Authors: Nicholas T. Wimer | Juliane Mueller | Sébastien Hamel | Vincenzo Lordi
Source: Journal of Cheminformatics (article, 2026-04-13)
Field: Materials Science — Materials Chemistry

Benchmarking the performance of uncertainty quantification methods for neural network-based interatomic potentials Abstract Machine-learned interatomic potentials (ML-IAPs) continue to gain popularity as accurate, computationally efficient replacements for traditional, physics-based interatomic potentials and expensive ab initio methods. Uncertainty quantification (UQ) of ML-IAPs is a growing area of research as UQ is critical in many applications of IAPs, such as developing curated datasets, active learning-based data augmentation, self-improving models, and estimating the uncertainty of molecular dynamics simulations. In this paper, we construct and benchmark a series of different neural network potentials (NNPs) with varying network architectures to determine the performance of these models with respect to both the mean and uncertainty calibration error. Each NNP method is specifically designed to predict either epistemic or aleatoric uncertainty with particular focus on the differences in behavior between the epistemic and aleatoric uncertainty estimates. We benchmark these methods using multiple datasets common in the ML-IAP literature. The results show that the aleatoric uncertainty from single-shot model architectures is a competitive alternative to ensemble-based epistemic uncertainty predictions in regions of sufficient data-density. However, in regions where the representative data is sparse, aleatoric uncertainty models tend to overpredict and epistemic methods tend to underpredict the actual model error. We conclude that the type of UQ is crucial when discussing performance of probabilistic model results as different methods have different performance characteristics depending on the regime in which they are evaluated. Therefore, the type of UQ method should be carefully evaluated against both the data characteristics and requirements for the intended application. Uncertainty quantification ; Benchmarking ; Benchmark (surveying) ; Probabilistic logic ; Measurement uncertainty ; Calibration ; Uncertainty analysis ; Propagation of uncertainty Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Advanced Graph Neural Networks
Authors: Emilio Méndez | Léna Triestram | Dune André | François‐Xavier Coudert | Rocio Semino
Source: arXiv (Cornell University) (preprint, 2026-04-10)
Field: Chemistry — Inorganic Chemistry

Force Field-Agnostic Phase Classification of Zeolitic Imidazolate Framework Polymorphs Zeolitic Imidazolate Frameworks (ZIFs) are a family of metal--organic frameworks that feature metal centers tetrahedrally linked to imidazole-based ligands and adopt zeolite-like topologies. ZIFs formed by Zinc cations and imidazolate linkers exhibit a remarkable degree of polymorphism, which can be modulated by varying synthesis parameters or thermodynamic conditions (i.e., temperature and pressure). Computer simulations provide a unique way of studying these materials and their phase transitions from the microscopic standpoint, revealing their underlying molecular mechanisms. However, studying these mechanisms requires to be able to classify the phase of each molecular entity in an agnostic and automatic fashion, which is particularly challenging when the two phases involved are structurally very similar. In this work, we systematically study neural network classifiers to classify ZIF phases on-the-fly during molecular dynamics simulations. We test a variety of input features, differing both in the dimensionality and nature of the descriptors and in the kind of force field used for building the training/testing database. We reveal that even with low-dimensional descriptors the classification is highly accurate, while the use of high-dimensional descriptors leads to an even better performance. Training the classifier with configurations coming from different force fields we can remove force field bias and enhance the classifier performance and general applicability. Finally, we apply our classifiers to reveal mechanistic details of the ZIF-4-cp $\xrightarrow{}$ ZIF-4-cp-II phase transition. Zeolitic imidazolate framework ; Classifier (UML) ; Curse of dimensionality ; Force field (fiction) ; Imidazolate ; Molecular dynamics ; Artificial neural network ; Field (mathematics) Metal-Organic Frameworks: Synthesis and Applications ; Zeolite Catalysis and Synthesis ; Machine Learning in Materials Science
Authors: Abdul Qadir | Ramazan Asmatulu
Source: Preprints.org (preprint, 2026-04-10)
Field: Materials Science — Materials Chemistry

Comprehensive Review of Hard Ceramic Coatings for Aerospace Alloys: Fabrication, Characterization, and Future Perspectives Hard ceramic coatings are essential for extending the operational limits of metal components in the extreme thermal and mechanical conditions of the aerospace and defense sectors. While considerable research exists on individual synthesis and characterization methods, a critical knowledge gap persists in bridging experimental fabrication with predictive computational modeling, a gap that limits the rational design of next-generation coating systems. This review addresses this gap by critically synthesizing the lifecycle of aerospace coatings from atomic-scale design to industrial deployment. Unlike prior reviews that focus on either fabrication or individual coating chemistries in isolation, this work uniquely integrates Integrated Computational Materials Engineering (ICME) with emerging machine learning (ML) strategies to provide a unified design-to-deployment framework. The principal ceramic material systems, Tungsten Carbide (WC), Boron Nitride (BN), Boron Carbide (B₄C), Silicon Carbide (SiC), Alumina (Al₂O₃), and Zirconia (ZrO₂) are discussed within the context of their specific roles in protecting aerospace-grade alloys. A central contribution is the multiscale computational framework, spanning Density Functional Theory (DFT), Molecular Dynamics (MD), mesoscale modelling, Finite Element Analysis (FEA), and ML-driven inverse design, which collectively accelerate the prediction of thermal breakdown, multi-axial stress responses, and coating lifetime. By relating these advances to gas turbine engines, airframes, and supersonic and hypersonic aviation systems, this review offers a clear research roadmap. Future research should prioritize the development of ultra-high-temperature ceramics (UHTCs), multifunctional self-healing coatings, and data-driven approaches to surface engineering. The goal is to move the field beyond traditional trial-and-error methods toward a more predictive framework based on fundamental physics and accelerated by machine learning techniques. Aerospace ; Thermal barrier coating ; Bridging (networking) ; Coating ; Context (archaeology) ; Ceramic ; Boron nitride ; Silicon carbide ; Turbine blade Machine Learning in Materials Science ; High-Temperature Coating Behaviors ; Advanced ceramic materials synthesis
Authors: Peng Liu | Donghai Mei
Source: Applied Catalysis A General (article, 2026-04-01)
Field: Chemistry — Inorganic Chemistry

Energy-entropy compensation: What is missing in theoretical quantifications of hydrogenation and dehydrogenation catalyzed by zeolites Catalysis ; Dehydrogenation ; Heterogeneous catalysis ; Zeolite ; Reaction conditions ; Reaction mechanism Zeolite Catalysis and Synthesis ; Catalysis and Oxidation Reactions ; Catalysts for Methane Reforming
Authors: Jiexi Song | Diwei Shi | Aixian She | Chongde Cao | Fengyuan Xuan
Source: arXiv (Cornell University) (preprint, 2026-04-10)
Field: Materials Science — Materials Chemistry

Selective Random Structure Search (SRSS): Unbiased Exploration of Polymorphs in Crystals Crystal structure prediction has traditionally relied on prototype-based seeding, approaches that often bias sampling toward known low-energy basins and overlook metastable polymorphs with unconventional symmetries. Here, we introduce Selective Random Structure Search (SRSS), a high-throughput, unbiased framework designed to explore the configurational space of crystalline materials across all dimensions. SRSS combines symmetry-constrained random generation with feature-based diversity selection and rapid relaxation and stability evaluation via universal machine-learning interatomic potentials (uMLIPs). Applied to diverse systems, including bulk system SiC and BaPtAs, 2D layered compounds NbSe2, and 1D nanotubes GaN, SRSS successfully recovers known ground states while revealing numerous previously unreported, dynamically stable polymorphs. Notable discoveries include complex cage-like SiC polytypes, low-energy BaPtAs polymorphs beyond experimental records, a semiconducting orthorhombic phase of 2D-NbSe2, and distinct armchair/zigzag GaN nanotubes. Crucially, the entire workflow operates efficiently on standard CPU resources without GPU acceleration, demonstrating that rigorous, hypothesis-free polymorph discovery is accessible even in resource-limited settings. SRSS thus establishes a robust, scalable platform for mapping the full landscape of crystal stability, bridging the gap between exhaustive search and computational feasibility. Orthorhombic crystal system ; Metastability ; Crystal structure prediction ; Scalability ; Workflow ; Crystal structure ; Stability (learning theory) ; Bridging (networking) Machine Learning in Materials Science ; Boron and Carbon Nanomaterials Research ; 2D Materials and Applications
Authors: Yeonwoo Jang | Naline Bellier | Kevin Kent Vincent Canlas | Ju Yeon Lee | Yujin Kim | Hansoo Park | Jong Ho Moon
Source: Nano Convergence (article, 2026-04-13)
Field: Materials Science — Biomaterials

Simulation-guided design of peptide–metal coordination interfaces for next-generation metallo-immunotherapy Metal ions are crucial regulators of immune signaling, metabolism, and redox homeostasis, but their therapeutic deployment in cancer immunotherapy is limited by systemic toxicity and inadequate spatiotemporal control. Peptide–metal coordination interfaces offer a programmable solution by combining sequence-encoded recognition with tunable coordination chemistry, enabling controlled metal speciation, bioavailability, and stimulus-responsive functions in complex biological environments. This review summarizes how biologically relevant ions, including Mn2+, Zn2+, Cu2+, and Fe2+/Fe3+, modulate innate and adaptive immunity through direct reprogramming of macrophages, dendritic cells, T cells, and natural killer cells, as well as through indirect remodeling of the tumor microenvironment via immunogenic cell death, redox perturbation, hypoxia alleviation, and disruption of immunosuppressive pathways. We discuss essential peptide–metal coordination principles, including natural binding motifs, synthetic coordination primitives, and stimulus-responsive switching mechanisms that enable dynamic regulation of metal–ligand interactions. To accelerate rational discovery, we outline a simulation-guided computational toolbox integrating docking-based prescreening, molecular dynamics, and metadynamics for stability assessment, quantum mechanical/molecular mechanics approaches for electronic-level accuracy, and machine learning workflows for multi-parameter optimization across high-dimensional design spaces. Finally, we survey the application landscape of peptide–metal platforms, from self-assembled ion-reservoir architectures to hybrid systems incorporating lipidic, polymeric, inorganic, or biomimetic carriers for improved pharmacokinetics and combinatorial functionality. Collectively, this perspective connects coordination chemistry with immunoengineering and highlights simulation-guided strategies for designing adaptive metallo-immunotherapeutic nanoplatforms with spatiotemporal precision and translational potential. Reprogramming ; Metadynamics ; Systems biology ; Workflow ; Synthetic biology ; Rational design ; Toolbox ; Acquired immune system Supramolecular Self-Assembly in Materials ; Supramolecular Chemistry and Complexes ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Ankit Mathanker | Jiawei Guo | Bryan R. Goldsmith | Joel B. Varley | Nitish Govindarajan
Source: ACS electrochemistry. (article, 2026-04-13)
Field: Materials Science — Materials Chemistry

Estimating Potential-Dependent Physicochemical Properties at Metal–Electrolyte Interfaces Using Machine Learning Interatomic Potentials Metal–electrolyte interfaces play a central role in electrocatalysis, energy storage, and environmental remediation. Understanding the structure and properties of these interfaces is therefore essential to designing efficient electrochemical systems. Density functional theory (DFT)-based molecular dynamics (MD) can accurately capture interfacial structure but is restricted to short time scales and small system sizes. To overcome these limitations, we develop machine learning interatomic potentials (MLIPs) using the MACE architecture within an active learning workflow to model aqueous NaCl electrolytes in contact with Au, Cu, and Rh(111) electrodes. The resulting committee of MLIPs achieves DFT-level accuracy across 21 metal–electrolyte systems spanning a wide range of surface charge densities. MACE–MD simulations reproduce key interfacial properties obtained from ab initio MD, including water density profiles, water orientation, and chemisorbed water coverage. Our simulations reveal a universal trend across all metals: the total coverage of water and ions decreases with increasing surface charge density or potential, reaches a minimum at or slightly below the pzc, and increases thereafter. Two distinct capacitance regimes emerge for all electrodes, corresponding to potentials below and above this point. Ion-specific effects strongly influence interfacial structure. Cl– exhibits significantly stronger interactions with all metal surfaces than Na+, undergoing partial desolvation of up to 3.5 water molecules upon approaching the interface, compared to only 0.5 for Na+. These behaviors manifest in the vibrational density of states, where Cu and Rh show broad O–H stretching features at negative charge densities associated with Na+ accumulation and strengthened hydrogen bonding. Overall, this work demonstrates that MLIPs based on the MACE architecture enable long-time scale, first-principles-accurate simulations of metal–electrolyte interfaces and provide detailed mechanistic insight into their potential-dependent physicochemical properties. Charge density ; Density functional theory ; Molecular dynamics ; Capacitance ; Work (physics) ; Ion ; Ab initio ; Surface charge ; Aqueous solution Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Spectroscopy and Quantum Chemical Studies
Authors: Arsalan Hashemi | Nima Ghafari Cherati | Sadegh Ghaderzadeh | Y. Wang | Mahdi Ghorbani-Asl | Tapio Ala-Nissila
Source: Physical Review Materials (article, 2026-04-13)
Field: Materials Science — Materials Chemistry

Stabilization of hBN/SiC heterostructures with vacancies and transition-metal atoms When two-dimensional atomic layers of different materials are brought into close proximity to form van der Waals (vdW) heterostructures, interlayer interactions can strongly influence their physicochemical properties. These effects are particularly pronounced when the interface exhibits local order and near-perfect structural alignment, giving rise to moiré patterns. Using density-functional theory calculations, we investigate a bilayer heterostructure composed of hexagonal boron nitride (hBN) and silicon carbide (SiC). We predict that introducing a boron vacancy, <a:math xmlns:a="http://www.w3.org/1998/Math/MathML"> <a:msub> <a:mi>V</a:mi> <a:mi mathvariant="normal">B</a:mi> </a:msub> </a:math> , at specific lattice sites alters the interlayer interaction from weak vdW coupling to localized silicon-nitrogen covalent bonding. Motivated by this mechanism, we examine the binding of transition-metal adatoms and identify principles for enhancing surface reactivity and stabilizing isolated single-metal atoms. Machine-learning molecular dynamics at finite temperature further demonstrate rapid and effectively irreversible trapping of Cu at <c:math xmlns:c="http://www.w3.org/1998/Math/MathML"> <c:msub> <c:mi>V</c:mi> <c:mi mathvariant="normal">B</c:mi> </c:msub> </c:math> sites, and show how the <e:math xmlns:e="http://www.w3.org/1998/Math/MathML"> <e:msub> <e:mi>V</e:mi> <e:mi mathvariant="normal">B</e:mi> </e:msub> </e:math> :Cu ratio governs the transition from single-atom isolation to vacancy-directed aggregation. These results suggest the hBN/SiC heterostructure as a versatile platform for atomically precise transition-metal functionalization, with implications for catalytic energy-conversion materials. Heterojunction ; van der Waals force ; Hexagonal boron nitride ; Bilayer ; Covalent bond ; Lattice (music) ; Molecular dynamics ; Trapping Graphene research and applications ; Boron and Carbon Nanomaterials Research ; Thermal properties of materials