jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Tieu-Long Phan | Nhu-Ngoc Nguyen Song | P. Stadler
Source: ArXiv.org (article, 2026-01-05)
Field: Materials Science — Materials Chemistry

SynRXN: An Open Benchmark and Curated Dataset for Computational Reaction Modeling We present SynRXN, a unified benchmarking framework and open-data resource for computer-aided synthesis planning (CASP). SynRXN decomposes end-to-end synthesis planning into five task families, covering reaction rebalancing, atom-to-atom mapping, reaction classification, reaction property prediction, and synthesis route design. Curated, provenance-tracked reaction corpora are assembled from heterogeneous public sources into a harmonized representation and packaged as versioned datasets for each task family, with explicit source metadata, licence tags, and machine-readable manifests that record checksums, and row counts. For every task, SynRXN provides transparent splitting functions that generate leakage-aware train, validation, and test partitions, together with standardized evaluation workflows and metric suites tailored to classification, regression, and structured prediction settings. For sensitive benchmarking, we combine public training and validation data with held-out gold-standard test sets, and contamination-prone tasks such as reaction rebalancing and atom-to-atom mapping are distributed only as evaluation sets and are explicitly not intended for model training. Scripted build recipes enable bitwise-reproducible regeneration of all corpora across machines and over time, and the entire resource is released under permissive open licences to support reuse and extension. By removing dataset heterogeneity and packaging transparent, reusable evaluation scaffolding, SynRXN enables fair longitudinal comparison of CASP methods, supports rigorous ablations and stress tests along the full reaction-informatics pipeline, and lowers the barrier for practitioners who seek robust and comparable performance estimates for real-world synthesis planning workloads. Benchmarking ; Benchmark (surveying) ; Workflow ; Task (project management) ; Metric (unit) ; Representation (politics) ; Resource (disambiguation) ; Reuse ; CASP Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: M. A. Deyab | M. M. El Rabiei | H. H. Mohamed | Q. Mohsen | M. Atef
Source: Scientific Reports (article, 2026-03-03)
Field: Materials Science — Materials Chemistry

Corrosion mitigation of carbon steel in acidic solution using ionic liquids based on chemical, electrochemical, and characterization studies This study investigates the corrosion inhibition performance of three ionic liquids, 1-butyl-3-methylimidazolium acetate (Inh A), 1-butyl-3-methylimidazolium hexafluorophosphate (Inh B), and 1-butyl-3-methylimidazolium tetrafluoroborate (Inh C), on carbon steel in 1.0 M HCl. The investigation employed weight loss measurements and electrochemical techniques to evaluate corrosion rates and inhibition efficiency. 1H-NMR and elemental analysis confirmed the chemical structures of the ionic liquids. Surface studies, including energy-dispersive X-ray spectroscopy (EDX) and scanning electron microscopy (SEM), verified the adsorption of these inhibitors on the C-steel surface. Results demonstrated that all ionic liquids effectively reduced corrosion rates, with Inh A achieving the highest inhibition efficiency of 96.79% at 60 °C. Elevated temperatures increased the protective capabilities of the ionic liquids. This comprehensive approach establishes the efficacy of these ionic liquids as effective corrosion inhibitors in acidic environments and provides insights into their structural characteristics and adsorption mechanisms. Ionic liquid ; Corrosion ; Carbon steel ; Adsorption ; Hexafluorophosphate ; Electrochemistry ; Tetrafluoroborate Corrosion Behavior and Inhibition ; Ionic liquids properties and applications ; Concrete Corrosion and Durability
Authors: Niraj Aryal | Sheng Zhang | Wei‐Guo Yin | Gia-Wei Chern
Source: Physical review. B./Physical review. B (preprint, 2026-01-23)
Field: Materials Science — Materials Chemistry

Machine learning approach for vibronically renormalized electronic band structures We present a machine learning (ML) method for efficient computation of vibrational thermal expectation values of physical properties from first principles. Our approach is based on the non-perturbative frozen phonon formulation in which stochastic Monte Carlo algorithm is employed to sample configurations of nuclei in a supercell at finite temperatures based on a first-principles phonon model. A deep-learning neural network is trained to accurately predict physical properties associated with sampled phonon configurations, thus bypassing the time-consuming {\em ab initio} calculations. To incorporate the point-group symmetry of the electronic system into the ML model, group-theoretical methods are used to develop a symmetry-invariant descriptor for phonon configurations in the supercell. We apply our ML approach to compute the temperature dependent electronic energy gap of silicon based on density functional theory (DFT). We show that, with less than a hundred DFT calculations for training the neural network model, an order of magnitude larger number of sampling can be achieved for the computation of the vibrational thermal expectation values. Our work highlights the promising potential of ML techniques for finite temperature first-principles electronic structure methods. Electronic band structure ; Physics ; Artificial intelligence ; Computer science ; Condensed matter physics Machine Learning in Materials Science
Authors: Dian Jin | Yancheng Yuan | Xiaoming Tao
Source: ArXiv.org (article, 2026-02-04)
Field: Materials Science — Materials Chemistry

Efficient Equivariant High-Order Crystal Tensor Prediction via Cartesian Local-Environment Many-Body Coupling End-to-end prediction of high-order crystal tensor properties from atomic structures remains challenging: while spherical-harmonic equivariant models are expressive, their Clebsch-Gordan tensor products incur substantial compute and memory costs for higher-order targets. We propose the Cartesian Environment Interaction Tensor Network (CEITNet), an approach that constructs a multi-channel Cartesian local environment tensor for each atom and performs flexible many-body mixing via a learnable channel-space interaction. By performing learning in channel space and using Cartesian tensor bases to assemble equivariant outputs, CEITNet enables efficient construction of high-order tensor. Across benchmark datasets for order-2 dielectric, order-3 piezoelectric, and order-4 elastic tensor prediction, CEITNet surpasses prior high-order prediction methods on key accuracy criteria while offering high computational efficiency. Tensor (intrinsic definition) ; Cartesian tensor ; Equivariant map ; Cartesian coordinate system ; Benchmark (surveying) ; Coupling (piping) ; Key (lock) Machine Learning in Materials Science ; Quantum many-body systems ; Model Reduction and Neural Networks
Authors: Emdadul  Haque Chowdhury | Md Shahed Hossain Sohan | Carlos Ulises Gonzalez-Valle | Adri C. T. van Duin | Bladimir Ramos-Alvarado
Source: Nanoscale (article, 2026-01-01)
Field: Materials Science — Surfaces, Coatings and Films

Understanding surface wettability: insights from experiments, molecular simulations, and first-principles theory Wettability plays a central role in surface science with far-reaching implications for engineering technologies, biomedical applications, and natural systems. Despite decades of investigation, wettability research remains fragmented across experimental characterization, molecular dynamics simulations, and quantum mechanical calculations, with no unified framework linking observations across length and time scales. This fragmentation originates from inconsistencies in experimental protocols, force field parameterization strategies, and electronic structure descriptions, which have contributed to long-standing debates regarding the intrinsic wetting behavior of many technologically relevant surfaces. This review critically synthesizes advances in experimental measurements, atomistic simulations, and first principles modeling to identify areas of agreement, unresolved controversies, and persistent knowledge gaps in wettability research. Emphasis is placed on the breakdown of classical wetting models at nanometric scales, the non-uniqueness of the contact angle as a sole wettability descriptor, and the role of complementary thermodynamic, structural, and dynamic quantities in characterizing solid-liquid affinity. The review examines how interfacial modeling choices, including surface preparation, interaction potentials, mixing rules, and electronic structure approximations, systematically influence predicted wettability and contribute to inconsistent conclusions across studies. By integrating interfacial chemistry, molecular-scale structure, and macroscopic observables, this work provides a conceptual roadmap for designing wettability studies that are more consistent, reproducible, and predictive across methodologies and length scales. Surface (topology) ; Wetting ; Natural (archaeology) ; Natural science ; Solid surface Surface Modification and Superhydrophobicity ; Nanopore and Nanochannel Transport Studies ; Polymer Surface Interaction Studies
Authors: Vishal Lakhanpal | Deliang Long | Lee Cronin
Source: Dalton Transactions (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Spontaneous assemblies of gigantic polyoxomolybdates; from structure and properties to synthetic methods Gigantic polyoxomolybdates, dominated by the Molybdenum Blue (MB) family, constitute a key subclass of polyoxometalates (POMs) and play a fundamental role in elucidating structural construction principles and showcasing the diversity of POM architectures. Since the first structural determination of the iconic {Mo<sub>154</sub>} big wheel cluster, these giant polyoxomolybdates have been developed rapidly over the past three decades, emerging as one of the most versatile classes of POM clusters. Potential applications span materials science, electronic materials, and catalysis. This tutorial review summarises the fundamentals of MBs and their close relatives including the highly reduced Mo browns and Mo reds, their molecular and electronic structures, physical properties, and synthetic methods for production. Drawing on 67 references from pivotal studies over the last thirty years, this review serves as both an authoritative reference for early-career researchers and an accessible introduction for non-professionals interested in this dynamic field. Molybdenum ; Key (lock) ; Subclass ; Molybdenum blue Polyoxometalates: Synthesis and Applications ; TiO2 Photocatalysis and Solar Cells ; Iron oxide chemistry and applications
Authors: Jiahui Zhao | Bo Song | Lei Jiang
Source: Communications Materials (article, 2026-01-23)
Field: Materials Science — Materials Chemistry

Quantum correlation of channel-confined ions in graphene-based transistors for energy-efficient neuromorphic chips Abstract The rapid progress of artificial intelligence has exposed the inherent limitations of the conventional chip technology, particularly the high energy-consumption, driving the emergence of neuromorphic chips and ionics. Using K + ion-filled graphene channels, we investigate the mechanism underlying the graphene-based ion transistors by ab initio molecular dynamics simulations. Here we show that graphene electrons enable long-range correlation of confined ions, which provides a basis for the sensitive responses of transistors to the channel ion density (as modulated by a gate voltage). The ON/OFF switching effect specifically results from the competition between π-π stacking and cation-π interaction in the channels with different ion-filling densities. The nonlinear increasing of transport efficiency (i.e., signal amplification effect) is due to the ion density-depended collective oscillation of channel-confined ions. Additionally, resonance between channel-outside and channel-confined ions triggers rapid ion dehydration, enabling the transistor’s ultrahigh ion diffusivity. These atomic-level insights as a design principle for the ultralow energy-consumption neuro-morphic chips. Neuromorphic engineering ; Transistor ; Ion ; Graphene ; Oscillation (cell signaling) ; Stacking ; Ab initio ; Electron Graphene research and applications ; Advanced Memory and Neural Computing ; 2D Materials and Applications
Authors: Andrew Ian Duff | I. Radosavljević Evans | M.A. González | Bettina Schwaighofer
Source: Computer Physics Communications (article, 2026-02-26)
Field: Materials Science — Materials Chemistry

Extending automated potential development workflow for legacy DFT reuse: Application to Bi-based oxides We present an extension to the Automated Potential Development (APD) workflow, an active-learning (AL) framework for training machine-learned interatomic potentials (MLIAPs). A mode for using pre-existing ab initio molecular dynamics (AIMD) trajectories is added, accelerating the initial data-generation stage and substantially reducing computational cost. The active-learning cycle is also accelerated through an improved defect-sampling procedure that enhances active-learning coverage. In addition, the workflow has been extended to provide compatibility with Quantum ESPRESSO, broadening the range of historical calculations that can be reused. These extensions target workflow efficiency rather than changes in the fitting objective or target accuracy. The resulting potentials are intended for applications within the finite-temperature regime sampled by the reused AIMD data. Applied to two oxide-ion conductors, Bi 0.852 V 0.148 O 1.648 and Bi 0.852 P 0.148 O 1.648 , it enables multi-nanosecond simulations across a wide temperature range while requiring an order of magnitude fewer new DFT calculations. In Bi 0.852 V 0.148 O 1.648 , the method reproduces high-temperature DFT results and captures a low-temperature pre-diffusive regime consistent with quasielastic neutron scattering and nuclear magnetic resonance observations. Leveraging existing DFT data within this AL framework thus offers an efficient route to developing accurate MLIAPs for complex ionic conductors. Workflow ; Density functional theory ; Ab initio ; Ab initio quantum chemistry methods ; Compatibility (geochemistry) ; Software ; Range (aeronautics) ; Development (topology) Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Physics of Superconductivity and Magnetism
Authors: Ning Xu | Zhong-Kang Han | Yong Wang
Source: ChemRxiv (article, 2026-03-04)
Field: Materials Science — Materials Chemistry

Machine-Learning-Accelerated Particle-Swarm Optimization for New Ground-State Reconstructions on TiO2 (110) Determining the atomic structure of reduced rutile TiO2(110) remains a long-standing challenge due to the complex rearrangements of Ti and O atoms and the vast configurational space accessible under reducing conditions. Here, we employ an advanced global structuresearch framework that integrates deep-potential energy surfaces with particle-swarm optimization, enabling efficient and unbiased exploration across multiple reduction states. Our search identifies a previously unreported Ti3O2-(2×1) reconstruction that is thermodynamically more stable than the earlier Ti3O2-(1×2) model. Simulated STM images of this newly identified structure reproduce the experimentally observed rosette motifs with a high degree of similarity. Electronic-structure analysis shows that its enhanced stability originates from strengthened orbital hybridization between the occupied Ti3+ 3d states and the O 2p states. This work refines the atomic-level understanding of a widely studied TiO2(110) reconstruction and highlights the capability of machine-learning-accelerated global search to resolve complex oxide surface structures with direct experimental relevance. Surface reconstruction ; Reduction (mathematics) ; Stability (learning theory) ; Rutile ; Work (physics) ; Complex oxide ; Space (punctuation) ; Structural stability Machine Learning in Materials Science ; TiO2 Photocatalysis and Solar Cells ; Catalysis and Oxidation Reactions
Authors: Maryia Shymanovich | Alexander G. Squires | Jakoah Brgoch | David O. Scanlon
Source: ChemRxiv (article, 2026-03-09)
Field: Materials Science — Materials Chemistry

Disorder Tolerance in LED Phosphor Host CaAlSiN3 : Correlated Cation Disorder with Minimal Electronic Penalty In functional multinary nitrides, intrinsic cation disorder typically has severe electronic consequences, introducing gap states and driving band-gap reduction through local bonding irregularities. CaAlSiN3 (CASN), a benchmark nitride phosphor host for phosphor-converted light-emitting diodes, therefore presents a paradox: despite substantial Al/Si disorder generated during high-temperature synthesis, it maintains exceptional optical performance. Understanding the fundamental effects of intrinsic disorder in this system could therefore provide broader insight into its performance as a phosphor and the electronic structure of disordertolerant nitrides. Experiments typically treat the Al/Si disorder in CASN as random, whereas prior theoretical studies have been limited to a narrow set of ordered configurations, leaving its thermodynamic origin and electronic consequences unresolved. Here, we combine cluster-expansion Monte Carlo simulations with hybrid density functional theory to characterise Al/Si disorder in CASN across a range of temperatures. Despite the emergence of disorder, the electronic structure remains resilient, with a maximum band-gap reduction of only 3.8% and minimal carrier localisation. This contrasts with related ternary nitrides such as ZnGeN2 and ZnSnN2, in which less correlated cation disorder directly perturbs both band edges, driving pronounced band-gap collapse and carrier localisation. We attribute this tolerance to two features of CASN's crystal chemistry: a Ca d-dominated conduction band minimum decoupled from the disordered sublattice, and correlated disorder that suppresses the most electronically damaging local environments. These results establish CASN as a model system for disorder tolerance in multinary nitrides and suggest broader design principles for electronically robust phosphor hosts. Phosphor ; Density functional theory ; Ternary operation ; Electronic structure ; Band gap ; Nitride ; Range (aeronautics) ; Hybrid functional ; Monte Carlo method Luminescence Properties of Advanced Materials ; Inorganic Chemistry and Materials ; GaN-based semiconductor devices and materials
Authors: Debora Iapadre | Alessio Carioscia | Alessandro Brusa | Armando Carlone
Source: ChemRxiv (article, 2026-03-23)
Field: Chemistry — Organic Chemistry

Latest Approaches in Enantiodivergent Organocatalysis This Minireview focuses on recent and most impactful contributions in enantiodivergent organocatalysed reactions. Four different approaches based on achiral stimuli were examined as inversion strategies: solvent-controlled inversion, achiral reagent or additive inducted inversion, and minimal catalyst modification, discussing in detail for each example the mechanistic insights behind the chiral inversion. Other strategies to achieve enantiodivergence are also briefly discussed. Organocatalysis ; Context (archaeology) ; Identification (biology) ; Yield (engineering) ; Natural (archaeology) Asymmetric Synthesis and Catalysis ; Catalytic Cross-Coupling Reactions ; Asymmetric Hydrogenation and Catalysis
Authors: Ming Xiu. Koh | Sharon Chia Yen Lim | Shi-Yan Cheah | Mohammod Aminuzzaman | Chong Seng Shit | Chun‐Hu Chen | Ling Shing Wong | Lai-Hock Tey
Source: Nanotechnology Reviews (article, 2026-01-01)
Field: Chemistry — Inorganic Chemistry

Durian husk-mediated green synthesis of Cr <sub>2</sub> O <sub>3</sub> nanoparticles exhibiting uncompetitive inhibition of pancreatic lipase for anti-obesity applications Abstract The global obesity epidemic underscores the urgent need for safer and more sustainable alternatives to conventional lipase inhibitors, which are limited by adverse side effects. In this study, chromium oxide nanoparticles (Cr 2 O 3 NPs) were synthesized via a microwave-assisted green route using durian ( Durio zibethinus or D. zibethinus ) husk extract (DHE), valorising agricultural waste as a natural reducing and stabilising agent. The nanoparticles were comprehensively characterised, confirming a rhombohedral eskolaite crystalline structure, quasi-spherical morphology with an average size of 65.93 ± 13.48 nm, and a bandgap energy of 3.32 eV. Functionally, the Cr 2 O 3 NPs exhibited strong antioxidant activity (91.94 % DPPH, 57.0 % ABTS) and significant pancreatic lipase inhibition (73.82 %). Kinetic analysis revealed an uncompetitive inhibition mechanism, representing the first report of durian husk-derived Cr 2 O 3 NPs with this enzymatic action. These findings highlight the dual antioxidant and enzymatic inhibitory potential of green-synthesised Cr 2 O 3 NPs and their promise as sustainable nanotherapeutics for obesity management within the framework of green nanomedicine and circular bioeconomy. Uncompetitive inhibitor ; Lipase ; Nanoparticle ; Antioxidant ; Husk ; Enzyme ; Chromium Pigment Synthesis and Properties ; Nanoparticles: synthesis and applications ; Graphene and Nanomaterials Applications
Authors: Павел Б. Сорокин | Boris I. Yakobson
Source: npj Computational Materials (article, 2026-01-06)
Field: Materials Science — Materials Chemistry

The properties, thermodynamics and application prospects of diamanes Diamane, a two-dimensional diamond-like carbon phase, illustrates how computational materials science can anticipate structures and transformations later verified experimentally. This Perspective summarizes theoretical predictions and experimental observations of its stability, chemically driven diamondization, and property modulation through functionalization and orientation. We highlight how atomistic simulations refine the understanding of formation pathways and guide potential applications in electronics, photonics, and energy systems. Perspective (graphical) ; Work (physics) ; Energy (signal processing) ; Non-equilibrium thermodynamics ; Property (philosophy) ; Carbon fibers Boron and Carbon Nanomaterials Research ; Graphene research and applications ; Electrocatalysts for Energy Conversion
Authors: Alin-Alexandru Andrei | Izabell Craciunescu | Lucian Barbu Tudoran | Rodica Turcu | George Marian Ispas | Gavril‐Ionel Giurgi | Alexandru Oprea | Mioara Zagrai | Cristian Sevcencu
Source: Polymers (article, 2026-03-23)
Field: Materials Science — Polymers and Plastics

Flexible Polypyrrole-Based Composite Films with Tailored Electrical and Mechanical Properties for Electrocardiographic Sensing Flexible electrode materials with tailored electrical and mechanical properties are essential for reliable electrocardiographic (ECG) sensing. In this work, p-toluenesulfonic-acid-doped polypyrrole (PPy-TSA) films were modified using polymeric and inorganic fillers, as well as their combinations (polyethylene glycol, graphene, carbon nanotubes, and zeolite), to tune their functional performance. The reference PPy-TSA film exhibits typical morphological and chemical characteristics of doped polypyrrole and serves as a reliable baseline for comparison. All composite films retain electrical conductivity within the range required for ECG applications while showing improved mechanical compliance (i.e., enhanced ability to conform to the skin and sustain deformation). Based on the optimized balance between electrical and mechanical properties, flexible ECG electrodes were fabricated using the TSA-doped PPy-based composite film. ECG recordings obtained with the several proposed electrodes show good agreement with those acquired using a commercial ECG electrode, demonstrating the potential of PPy-based composite films for flexible bioelectronic sensing applications. Polypyrrole ; Composite number ; Electrode ; Electrical resistivity and conductivity ; Conductive polymer ; Electrical conductor Conducting polymers and applications ; Advanced Sensor and Energy Harvesting Materials ; Polymer Nanocomposite Synthesis and Irradiation