jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Venkat Kapil
Source: The Journal of Chemical Physics (article, 2026-02-02)
Field: Chemistry — Physical and Theoretical Chemistry

Harmonic-to-anharmonic thermodynamic integration made simple using REG TI Standard harmonic-to-anharmonic thermodynamic integration (TI) is known to develop a near singularity in the integrand for solids exhibiting diffusive degrees of freedom, such as rotating functional groups or migrating defects. This pathology results in numerical challenges for estimating absolute free energies within a single thermodynamic cycle. In this work, we introduce a simple regularization that removes this singularity and yields a well-behaved integrand that can be accurately evaluated on a uniform grid. The approach-termed Regularized End point Gradient (REG) TI-is demonstrated on a model system and on predicting the relative stability of paracetamol polymorphs for which quasi-free methyl rotations lead to a near singularity in standard TI. We expect REG TI to simplify anharmonic free energy calculations for solids and to potentially enable their automation. Singularity ; Thermodynamic integration ; Regularization (linguistics) ; Simple (philosophy) ; Anharmonicity ; Stability (learning theory) ; Thermodynamic system ; Point (geometry) Crystallography and molecular interactions ; Numerical methods for differential equations ; Energetic Materials and Combustion
Authors: S. N. PRAJAPATI
Source: Research Square (preprint, 2026-04-07)
Field: Materials Science — Materials Chemistry

Strain-decoupled transport in MoS₂/hBN heterostructures Heterojunction ; Strain engineering ; Thermal conductivity ; Berry connection and curvature ; Thermoelectric materials ; Decoupling (probability) ; Boron nitride ; Band gap 2D Materials and Applications ; Thermal properties of materials ; Topological Materials and Phenomena
Authors: Xiangyang Feng | Weiwei Lou | Guandong Wu | Xin Ming | Bo Wang | Jiahao Lu | Lei He | Pengxiang Zhou | Lu Chen | Yingbo Yan | Chendong Ge | Shiqi Wu | Xueyin Chen | Peng Xu | Fei Xing | Ji Zhou | Zhen Xu | Zhanqin Yu | Wenfei Shen | Jianguo Tang | Lin Qiu | Yingjun Liu | Chao Gao
Source: Advanced Functional Materials (article, 2026-01-15)
Field: Materials Science — Materials Chemistry

Highly Thermally Conductive and High Shear Strength Graphene Fiber Composites via Molecular Conjugation Interface Engineering for Extreme Thermal Management ABSTRACT Highly thermally conductive graphene fibers (GFs) hold exceptional promise for next‐generation high‐flux thermal management systems, yet their integration into advanced composites remains fundamentally limited by insufficient interfacial compatibility with polymer resin. This limitation severely restricts both thermal and mechanical transfer efficiency and compromises stability under extreme thermal cycling. Herein, a molecularly tailored conjugation interfaces engineering through strategically selected silane‐based molecular tethers grafted onto the wrinkled GF surfaces to establish covalent bridges into the epoxy network, effectively addressing the interfacial bonding dilemma between GFs and polymer resin is introduced. The resultant GF composites achieve a synergistic enhancement in interfacial shear strength increased by 61.6% (from 55.2 to 89.2 MPa) and a record‐level in‐plane thermal conductivity of 571.1 W m −1 K −1 . Critically, the thermal conductivity retention consistently exceeds 98% throughout 100 thermal shock cycles (25 to 125°C), confirming exceptional interfacial stability and thermal fatigue resistance. Furthermore, molecular dynamics (MD) simulations reveal that rigid benzene ring of molecular tethers interphase enables exceptional interfacial thermal conductance of 373.56 MW m −2 K −1 between graphene and epoxy, while flexible or mismatched molecular tethers agents induce disorder and weaken spectral coupling. This molecular conjugation interface engineering unlocks the interfacial design and chemistry for improving thermomechanical performances of GF composites, paving the way for robust and high‐flux thermal management. Graphene ; Thermal conductivity ; Thermal stability ; Electrical conductor ; Molecular dynamics ; Polymer ; Interfacial thermal resistance ; Interphase ; Thermal Thermal properties of materials ; Thermal Radiation and Cooling Technologies ; Thermal Expansion and Ionic Conductivity
Authors: Samuel F. J. Blair | Minahil Khan | Christopher P. Reardon | Steven Johnson | Thomas F. Krauss
Source: ACS Nano (article, 2026-03-06)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Electrically Tunable Optofluidic Metasurface The dynamic control of light at the nanoscale has been a longstanding challenge in photonics. More recently, fluidics has been added to the toolkit, leveraging liquid properties as an additional degree of freedom for tunability. Here, we present a tunable metasurface architecture that integrates an optofluidic layer. We use a guided mode resonance platform, whereby a silicon nitride grating is coated with indium tin oxide (ITO) as the active material, operating in the visible-NIR wavelength range (∼800 nm). Our design employs an all-dielectric structure with a fluidic gate to overcome the efficiency-loss trade-off typical of other dynamic metasurfaces. We demonstrate spectral and phase tuning, achieving a near-2π phase shift with a low voltage swing (±3 V), as well as maintaining a strong resonance amplitude (<i>R</i> > 80%) with an all-pass filter configuration. These results establish a foundation for high-performance tunable metasurfaces with broad cross-disciplinary applications. Fluidics ; Grating ; Microfluidics ; Indium tin oxide ; Guided-mode resonance ; Resonance (particle physics) ; Voltage ; Nanoscopic scale ; Phase (matter) Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Optical Coatings and Gratings
Authors: Pierre Kawak | William F. Drayer | David Simmons
Source: arXiv (Cornell University) (article, 2026-02-05)
Field: Materials Science — Materials Chemistry

AMDAT: An Open-Source Molecular Dynamics Analysis Toolkit for Supercooled Liquids, Glass-Forming Materials, and Complex Fluids AMDAT (Amorphous Molecular Dynamics Analysis Toolkit) is an open-source C++ toolkit for post-processing molecular dynamics trajectories, focused on high-performance static and dynamic analyses of amorphous, glassy, and polymer materials, including supercooled liquids and complex fluids. In this paper, we describe AMDAT's design for efficient long-timescale analysis via in-memory trajectory handling and exponential time sampling, and we demonstrate representative workflows for widely used observables such as radial distribution functions, structure factors, intermediate scattering functions, and neighbor correlations. Supercooling ; Molecular dynamics ; Observable ; Trajectory ; Complex system ; Complex dynamics ; Dynamics (music) ; Complex fluid Material Dynamics and Properties ; Advanced Physical and Chemical Molecular Interactions ; Polymer crystallization and properties
Authors: Seohyeon Shin | HanJun Choi | Jun-Hyung Park | Hong Kook Kim | Mansu Kim
Source: ArXiv.org (article, 2026-04-06)
Field: Materials Science — Materials Chemistry

MolDA: Molecular Understanding and Generation via Large Language Diffusion Model Large Language Models (LLMs) have significantly advanced molecular discovery, but existing multimodal molecular architectures fundamentally rely on autoregressive (AR) backbones. This strict left-to-right inductive bias is sub-optimal for generating chemically valid molecules, as it struggles to account for non-local global constraints (e.g., ring closures) and often accumulates structural errors during sequential generation. To address these limitations, we propose MolDA (Molecular language model with masked Diffusion with mAsking), a novel multimodal framework that replaces the conventional AR backbone with a discrete Large Language Diffusion Model. MolDA extracts comprehensive structural representations using a hybrid graph encoder, which captures both local and global topologies, and aligns them into the language token space via a Q-Former. Furthermore, we mathematically reformulate Molecular Structure Preference Optimization specifically for the masked diffusion. Through bidirectional iterative denoising, MolDA ensures global structural coherence, chemical validity, and robust reasoning across molecule generation, captioning, and property prediction. Property (philosophy) ; Language model ; Graph ; Security token ; Space (punctuation) ; Autoregressive model ; Diffusion ; Probabilistic logic Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Islam M. Ahmady | Javad B. M. Parambath | Elsiddig A. E. Elsheikh | Gwangmin Kim | Changseok Han | Alejandro Pérez García | Ahmed A. Mohamed
Source: Applied Microbiology and Biotechnology (article, 2026-01-12)
Field: Materials Science — Materials Chemistry

Investigating soil and ATCC bacterial strains for their ability to synthesize anisotropic gold nanoparticles The current study investigated 17 bacterial strains for their ability to synthesize gold nanoparticles (AuNPs) from the aryldiazonium gold(III) salt (DS-AuCl<sub>4</sub>). The study aims to investigate the ability of bacterial cell biomass in the stationary phase of growth to synthesize AuNPs at 28 °C and 37 °C. Eleven bacterial strains were isolated from soil and identified using the VITEK® 2 system and 16S rRNA sequencing. An additional six strains were obtained from the American Type Culture Collection (ATCC). The investigated Gram-positive and Gram-negative bacterial strains successfully produced anisotropic AuNPs at a cell density of 2.0 McFarland (6.0 × 10<sup>8</sup> CFU/mL). Nanoparticle formation was faster when samples were incubated at 37 °C than at 28 °C across all bacterial strains. The results of UV-vis spectroscopy confirmed the presence of AuNPs, with peaks observed centered at 550 nm. High-resolution transmission electron microscopy (HR-TEM) revealed a variety of morphologies, including spheres, rods, triangles, pentagons, hexagons, irregular shapes, and flower-like structures. Gram-positive and Gram-negative bacteria synthesized AuNPs of sizes 38.7 ± 26.0 and 34.0 ± 18.6 nm, respectively. Lattice-spacing analysis confirmed the formation of metallic AuNPs. Energy-dispersed X-ray spectroscopy (EDS) validated the presence of gold in the samples, and X-ray photoelectron spectroscopy (XPS) confirmed the elemental composition of AuNPs at 84.0 eV. These nanoparticles have potential applications in cancer therapy and diagnosis, antibacterial treatments, and drug delivery. KEY POINTS: • The AuNPs were synthesized using various bacterial strains • The gold precursor is aryldiazonium gold(III) salt • Various anisotropic morphologies were obtained. Colloidal gold ; Bacteria ; Transmission electron microscopy ; Bacterial cell structure ; Nanoparticle ; X-ray photoelectron spectroscopy ; Metal Nanoparticles: synthesis and applications ; Environmental remediation with nanomaterials ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Karel L. K. De Witte | Tom Braeckevelt | Massimo Bocus | Sander Vandenhaute | Véronique Van Speybroeck
Source: ArXiv.org (article, 2026-02-24)
Field: Materials Science — Materials Chemistry

A Novel NPT Thermodynamic Integration Scheme to Derive Rigorous Gibbs Free Energies for Crystalline Solids Thermodynamic Integration (TI) is the state-of-the-art computational technique for accurate Gibbs free energy predictions of solids. Conventional TI schemes start from an NVT harmonic reference and require three successive corrections to recover the Gibbs free energy of the real crystal in the NPT ensemble. However, the NVT-to-NPT correction neglects full cell flexibility. Here, we present a rigorous (and only) two-step TI scheme that operates entirely in the NPT ensemble, eliminating the need for the approximate NVT-to-NPT step. The key methodological advancement is the novel NPT reference that explicitly accounts for full cell fluctuations. The new approach is compared with the conventional one via two complementary case studies. For ice polymorphs, having simple cell-shape distributions, the new approach reproduces conventional TI results with excellent agreement. For CsPbI3, whose black phase exhibits complex cell-shape behavior, we demonstrate that our novel method provides more accurate Gibbs free energy differences than the conventional one. Moreover, the proposed framework maintains comparable computational cost while offering a simplified workflow. Overall, the new NPT TI scheme provides rigorous and direct Gibbs free energy calculations for solids. Gibbs free energy ; Thermodynamic integration ; Energy (signal processing) ; Scheme (mathematics) ; Phase (matter) ; Black box Machine Learning in Materials Science ; Perovskite Materials and Applications ; Advanced Thermoelectric Materials and Devices
Authors: Zubeda Nangrejo | Deedar Ali Jamro | Omme Habiba Abbasi | Dilawar Ali | Nawa Arshad | Muhammad Zakria | Muhammad Owais | Iftekhar Majeed | Muhammad Rashid
Source: Haya The Saudi Journal of Life Sciences (article, 2026-01-17)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Light-Controlled Defect Engineering for Enhanced Superconductivity in Layered Nickelate Quantum Materials Layered nickelate quantum materials have emerged as a promising platform for unconventional superconductivity. However, their superconducting response remains highly sensitive to lattice defects and carrier inhomogeneity. Conventional defect engineering relies on static chemical doping or strain, which lacks real-time tunability. This work introduces a dynamic and non-invasive strategy based on light-controlled defect engineering to enhance superconductivity in layered nickelates. We demonstrate that targeted optical excitation can reversibly manipulate defect states at the atomic scale. Photo-induced charge redistribution modifies local lattice distortions without permanent structural damage. This process enables controlled tuning of carrier density and electron phonon coupling. As a result, superconducting coherence is strengthened across the layered structure. The approach bridges optical control and quantum material engineering within a single framework. Spectroscopic and transport analyses reveal a measurable increase in critical temperature and superconducting stability under optimized illumination conditions. The enhancement originates from defect reconfiguration rather than thermal effects. Importantly, the induced changes persist over experimentally relevant timescales and remain fully reversible. This behavior distinguishes the method from irreversible chemical techniques. The proposed mechanism establishes light as an active control parameter for superconductivity. It also provides direct insight into the role of defects in nickelate quantum phases. Beyond nickelates, the framework can be generalized to other correlated electron systems where defect dynamics govern emergent properties. This study opens a pathway toward optically programmable superconductors and reconfigurable quantum devices. Superconductivity ; Control reconfiguration ; Quantum ; Doping ; Coherence (philosophical gambling strategy) ; Phonon ; Thermal ; Quantum technology ; Lattice (music) Iron-based superconductors research ; Electronic and Structural Properties of Oxides ; Physics of Superconductivity and Magnetism
Authors: Jesús Israel Morán-Cortés | F. Pacheco-Vázquez
Source: ArXiv.org (article, 2026-03-08)
Field: Materials Science — Materials Chemistry

Machine Learning Based Identification of Solvents from Post-Desiccation Patterns We introduce an optimized protocol of fracture pattern classification using an artificial neural network to identify the solvent involved in the desiccation cracking process of starch-liquid slurries, even after it has been completely evaporated. For this purpose, image analysis techniques were used to characterize patterns obtained from drying suspensions using single solvents (water, ethanol, acetone) and two-component solvents (water-ethanol mixtures at different concentrations). Frequency histograms were generated based on nine morphological features, taking into account their size, shape, geometry and orientational ordering. Subsequently, we used these histograms as input data into artificial neural network variants to determine the set of features that lead to the higher accuracy in solvent identification. We obtained an average accuracy of $96(\pm 1)\%$ considering all solvents in the analysis. The highest accuracy was obtained with sets of features that include the crack area distribution. The proposed protocol can help to determine the combination of features that optimize pattern recognition in other fields of science and engineering. Artificial neural network ; Histogram ; Pattern recognition (psychology) ; Set (abstract data type) ; Data set ; Process (computing) ; Identification (biology) Crystallization and Solubility Studies ; Nanomaterials and Printing Technologies ; Machine Learning in Materials Science
Authors: Nikolas Capra | Célestin Bourgery | Jerry M. Parks | Dana L. Carper | John F. Cahill | Joshua K. Michener | Flora Meilleur
Source: bioRxiv (Cold Spring Harbor Laboratory) (article, 2026-03-12)
Field: Materials Science — Biomaterials

Structural and oligomeric characterization of substrate- and product-selective nylon hydrolases Enzymatic degradation of synthetic polymers has attracted broad interest because it offers environmental and manufacturing advantages compared to traditional mechanical and chemical breakdown approaches. Enzymes are highly specific and reaction conditions are generally aqueous and require low pressure and temperature, resulting in lower energy consumption and lower chemical waste production. Here we report the biochemical and structural characterization of three newly discovered enzymes capable of nylon hydrolysis: Nyl10, Nyl12 and Nyl50. Using solution characterization techniques, we found that the enzymes adopt a single oligomeric state consistent with a tetramer over a wide range of concentrations. X-ray crystallographic structures of all three enzymes support the association into tetramers. Comparison of ligand-bound X-ray crystal structures of Nyl10 and Nyl12 with the previously determined structure of Nyl50 identified key structural determinants involved in ligand binding. Noticeably, a flexible loop found in several polyamide degrading enzymes is observed to flip towards (closed conformation) and away (open conformation) from the active site upon ligand binding. Analysis of adduct and surrogate substrate-bound enzyme complex structures provide a model for substrate binding directionality. Finally, activity assays showed that both Nyl10 and Nyl12 can hydrolyze ester bonds, and that Nyl12 has the highest activity toward PA66, identifying it as the best candidate for protein engineering for efficient nylon hydrolysis. Tetramer ; Enzyme ; Ligand (biochemistry) ; Characterization (materials science) ; Substrate (aquarium) ; Polymer ; Hydrolysis ; Adduct ; Chemical modification biodegradable polymer synthesis and properties ; Microplastics and Plastic Pollution ; Peptidase Inhibition and Analysis
Authors: Peeraches Buathongvong | Chanakan Phewklieng | Khemthat Srisujaritpanich | Pattara Somnuake | Wikoramet Teeka | Sirirat Wacharawichanant
Source: Current Applied Science and Technology (article, 2026-02-05)
Field: Materials Science — Biomaterials

Development of Carboxymethyl Cellulose/Starch Film Composites Using Cellulose from Sugarcane Bagasse and Titanium Dioxide for Active Food Packaging Applications The development of an active food packaging film from carboxymethyl cellulose (CMC), starch, glycerol, and microcrystalline cellulose (MCC) to enhance mechanical strength and moisture resistance, which was synthesized from sugarcane bagasse (SCB), and titanium dioxide (TiO2) was investigated. The MCC was chemically extracted from SCB via alkaline treatment, hydrogen peroxide oxidation, and acid hydrolysis, resulting in 92% cellulose purity, as confirmed by Fourier Transform Infrared (FT-IR) spectroscopy and X-ray diffraction (XRD). The biofilms were prepared using the solution casting method with 3 conditions: CMC/starch, CMC/starch/MCC, and CMC/starch/MCC/TiO2. Incorporating MCC and TiO2 significantly improved tensile strength (21.57 MPa to 42.79 MPa) and Young’s modulus (1258 MPa to 1568 MPa). These additions decreased water solubility from 22.1% to 0.7%. FT-IR analysis of the films showed enhanced CH bonding of CMC in high content without any reaction. Biodegradability tests showed complete degradation within 7 days. Shelf-life extension tests demonstrated that bananas wrapped with CMC/starch/MCC/TiO2 film lasted up to 12 days, compared to 5-6 days for unwrapped samples, with the addition of TiO2 effectively scavenging ethylene without compromising film integrity. These results demonstrate the feasibility of SCB-derived MCC for sustainable, high-performance food packaging. Carboxymethyl cellulose ; Microcrystalline cellulose ; Bagasse ; Ultimate tensile strength ; Titanium dioxide ; Cellulose ; Fourier transform infrared spectroscopy ; Food packaging ; Casting Nanocomposite Films for Food Packaging ; Advanced Cellulose Research Studies ; Natural Fiber Reinforced Composites
Authors: David Linteau | Saverio Moroni | Giuseppe Carleo | Markus Holzmann
Source: ArXiv.org (article, 2026-03-15)
Field: Materials Science — Materials Chemistry

Variance reduction for forces and pressure in variational Monte Carlo We present simple and practical strategies to reduce the variance of Monte Carlo estimators. Our focus is on variational Monte Carlo calculations of atomic forces and pressure in electronic systems, although we show that the underlying ideas apply more broadly to other observables, like pair-correlation and angular-distribution functions, and other methods, including molecular dynamics. For Pulay-type contributions, we show that a minor modification based on the Metropolis acceptance ratio softens the power-law divergence of the variance to a logarithmic one, and that inexpensive regularizations can further suppress outliers at the price of a controlled small bias. For Hellmann-Feynman forces, we derive compact variance-reduced estimators for periodic systems that are straightforward to implement in standard Monte Carlo codes. The approach is illustrated for high-pressure metallic hydrogen with more than a hundred atoms described by neural quantum states, including an application to molecular dynamics driven by the improved forces. Monte Carlo method ; Quantum Monte Carlo ; Variance reduction ; Monte Carlo method in statistical physics ; Estimator ; Monte Carlo molecular modeling ; Variance (accounting) ; Divergence (linguistics) ; Logarithm Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions ; Advanced Thermodynamics and Statistical Mechanics
Authors: Maixin Yang | Zhixiong Yang | Weibing Zhang
Source: Zhongguo kexue. Wulixue Lixue Tianwenxue (article, 2026-03-01)
Field: Materials Science — Materials Chemistry

Perovskite Formation Energy and Bandgap Prediction: A Graph Neural Network and Transfer Learning Framework 设计兼具高稳定性与高效率的新型钙钛矿材料是光电材料与光电器件领域的重要课题. 传统图神经网络模型能准确预测钙钛矿形成能, 但受限于样本数量无法准确预测带隙. 本文提出一种基于图神经网络与迁移学习的框架用于准确预测钙钛矿材料的形成能与带隙. 基于计算材料存储库（Computational Materials Repository, CMR）的钙钛矿数据, 本文使用了三种图神经网络模型（MEGNet、SchNet、CGCNN）预测钙钛矿材料形成能, 发现 MEGNet 预测效果最好, 其平均绝对误差 MAE 为 0.070 eV/atom, 比 SchNet、CGCNN 的预测结果的 MAE 分别降低了 0.055 eV/atom 和 0.088 eV/atom, 决定系数 R<sup>2</sup>为 0.986 比后两种分别提高 0.036 和 0.064. 在此基础上, 提取形成能数据集上训练得到的元素嵌入向量, 通过迁移学习方法对钙钛矿带隙进行预测. 与未使用迁移学习的模型相比, 迁移学习的模型将预测带隙的 MAE 从 0.42 eV降至 0.34 eV, 提高了钙钛矿带隙预测的精度. 该框架为高通量筛选高稳定性钙钛矿光伏材料提供了一种潜在途径. Perovskite (structure) ; Artificial neural network ; Graph ; Energy (signal processing) ; Transfer of learning ; Band gap Machine Learning in Materials Science ; Perovskite Materials and Applications ; Advanced Graph Neural Networks