jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Lei Zhu | Shujie Wang | Yun Wang | Liang Dong | Hailong Li | Yiya Wang | Xumin Ding
Source: Photonics (article, 2025-05-09)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A Dual-Band Tunable Electromagnetically Induced Transparency (EIT) Metamaterial Based on Vanadium Dioxide A dual-band tunable terahertz electromagnetically induced transparency (EIT) metamaterial is introduced. The EIT metamaterial consists of two rectangular split rings, two metal strips, and a patterned vanadium dioxide (VO2) located at the back. The rectangular split rings serve as the bright resonator to generate two resonance valleys at distinct frequencies. The metal strips act as the dark resonator and are indirectly activated via the coupling influence of the bright resonator. The EIT metamaterial’s response mechanism is analyzed via the field effect and the two-particle model, with theoretical fitting results showing strong agreement with the simulation results. Moreover, VO2’s conductivity is altered to dynamically control the EIT effect in both frequency bands. Two transparency windows, with modulation depths of 70% and 75%, are observed as the conductivity of VO2 decreases. Simultaneously, the simulation results reveal a favorable slow light effect, with group delays reaching 51 ps and 74 ps at the transparency windows. The proposed metamaterial holds considerable promise for future modulator, filter, and slow light device applications. Electromagnetically induced transparency ; Metamaterial ; Vanadium dioxide ; Optoelectronics ; Materials science ; Transparency (behavior) ; Slow light ; Optics ; Physics ; Photonic crystal ; Nanotechnology ; Computer science Metamaterials and Metasurfaces Applications ; Optical Wireless Communication Technologies ; Advanced Wireless Communication Technologies
Authors: Tom Stavert | Siddharth V. Patwardhan | Miguel Jorge
Source: Molecular Simulation (article, 2025-02-11)
Field: Materials Science — Materials Chemistry

Development of a coarse-grained model for the early stages of ordered mesoporous silica formation Understanding the early stages of the synthesis of ordered mesoporous silica materials is not only incredibly important to control the nanoporous structure of the material that forms, but can also inform the design of sustainable manufacturing. Computational modelling is an invaluable tool to study this process, enabling a move away from trial and error experimental studies towards a more rational computer-aided design procedure for these valuable nanomaterials. However, this is made challenging by the complexity of the self-assembly process that governs the early stages of synthesis, which takes place over a broad range of time and length scales that are inaccessible to current traditional atomistic models. In this work, a coarse-grained molecular dynamics model based on the Martini 3 force-field is developed following a systematic multi-scale strategy that can also be adopted for many similar systems which rely on a delicate balance of interactions between inorganic precursor species and a surfactant template. Self-assembly results with the new model are consistent with available experimental data on these systems. Mesoporous material ; Mesoporous silica ; Materials science ; Nanotechnology ; Chemical engineering ; Chemical physics ; Chemistry ; Catalysis ; Organic chemistry ; Engineering Mesoporous Materials and Catalysis ; Zeolite Catalysis and Synthesis ; Clay minerals and soil interactions
Authors: Tom Stavert | Miguel Jorge
Source: Journal of Molecular Liquids (article, 2025-04-28)
Field: Chemistry — Organic Chemistry

Understanding the pH-responsive behaviour of alkylamine surfactants Alkylamine surfactants exhibit interesting pH-responsive behaviour, which can be attributed to changes in the protonation state of the primary amine group. This behaviour is important to their use in a broad variety of applications, but it is challenging to isolate experimentally due to the need to add acid to control system pH. In this work, we achieve this by applying multi-scale computational modelling to study the self-assembly and phase behaviour of the alkylamine surfactant dodecylamine (DDA), validating our model against available experimental phase diagrams for DDA. Our results show that the degree of charge of the surfactant controls the delicate balance between hydrophilic/hydrophobic interactions and hence the interplay between self-assembly and solubility that is responsible for the rich phase behaviour observed in these systems. These findings have important implications as to how pH can be used to control the behaviour of alkylamine surfactants in various applications. • Developed new multi-scale model for self-assembly of pH-responsive surfactants. • Experimental phase diagrams for alkylamines are accurately reproduced. • Balance of interactions shifts as the head-group is protonated/deprotonated. • Change in interfacial curvature gives rise to intricate phase behaviour. Chemistry ; Chemical engineering ; Engineering Surfactants and Colloidal Systems ; Analytical Chemistry and Chromatography ; Environmental Chemistry and Analysis
Authors: Aditya Abhijit Kunte | Sarthak Khandelwal | Sandeep P. Patil
Source: Journal of Composites Science (article, 2025-06-17)
Field: Chemistry — Spectroscopy

Comprehensive Overview on the Computational, Experimental, Numerical, and Theoretical Assessments of Silica Aerogel Composites Silica aerogel (SiA) composites have gained importance due to their ability to overcome the challenges of pure SiA while retaining its superior properties. Their growing significance calls for a closer examination of its assessment methods and performance optimization strategies. Deeper understanding of various assessment methods is essential as it assists in the accurate prediction of the operational stability and environmental resilience of these composites. Addressing performance optimization also remains crucial for the mitigation of structural limitations in SiA composites. This review highlights the advancements and explores the strategies for evaluating the mechanical, thermal, flammability, and radiative properties of SiA composites. It offers an in-depth discussion, revealing not only their thermomechanical behavior, but also their remarkable resistance to fire and radiation. Additionally, this review also examines the development and refinement of theoretical and numerical models. Further, a systematic comparison of continuum mechanics-based simulations with nanoscale (molecular dynamics) simulations reveals critical insights into their accuracies, limitations, and applicability in modeling SiA composites. Exciting insights on the assessments and properties of SiA composites are explored across several experimental, theoretical, numerical, and computational studies. This review also provides an in-depth discussion of performance optimization strategies, limitations, and future prospects while briefly highlighting applications relevant to each assessment. Finally, it presents a distinctive comparative analysis of decade-long studies for each assessment, offering key insights to guide future studies. Aerogel ; Composite material ; Materials science Aerogels and thermal insulation ; Surface Modification and Superhydrophobicity ; Building materials and conservation
Authors: Marija Cvetnić | Tamara Rinkovec | Robert Vianello | Gordan Horvat | Nikola Bregović | Vladislav Tomišić
Source: Molecules (article, 2025-06-04)
Field: Chemistry — Spectroscopy

A Detailed Thermodynamic Description of Ion Pair Binding by a Calix[4]arene Derivative Containing Urea and Amide Functionalities Receptors capable of binding both positive and negative ions are an important domain of supramolecular chemistry with valuable application potential. A Complete thermodynamic description of the equilibria related to ion pair recognition is beneficial in developing the optimized receptor systems, although it represents a difficult task that is rarely resolved due to various coupled processes. Here, we present a comprehensive study of ion pair (NaCl, NaHSO<sub>4</sub>, and NaH<sub>2</sub>PO<sub>4</sub>) binding by a ureido-amide calix[4]arene host in acetonitrile using a series of experimental techniques and molecular dynamics simulations. We devoted particular attention to characterizing the side processes (ion association and salt precipitation) and included them in the models describing ion pair complex formation. For this purpose, a multimethod approach (potentiometry, conductometry, ITC, flame AES) was employed, generating reliable data which provided insight into the thermodynamic effect of each included equilibrium. Positive cooperativity was observed in the context of NaCl and NaHSO<sub>4</sub> binding by the studied calixarene. Computational results related to the NaCl complex in acetonitrile revealed that favorable Coulombic interactions, changes in affinity for solvent molecule inclusion, and intramolecular hydrogen bonding contributed to cation-induced cooperativity. Chemistry ; Supramolecular chemistry ; Calixarene ; Hydrogen bond ; Cooperativity ; Acetonitrile ; Context (archaeology) ; Cooperative binding ; Molecule ; Amide ; Molecular recognition ; Isothermal titration calorimetry ; Intramolecular force ; Computational chemistry ; Crystallography ; Physical chemistry ; Stereochemistry ; Binding site ; Organic chemistry Molecular Sensors and Ion Detection ; Mass Spectrometry Techniques and Applications ; Analytical Chemistry and Sensors
Authors: Agam Shayit | Can Liao | Shiv Upadhyay | Hang Hu | Tianyuan Zhang | A. Eugene DePrince | Chao Yang | Xiaosong Li
Source: Nature Communications (article, 2025-12-10)
Field: Materials Science — Materials Chemistry

Numerically exact configuration interaction at quadrillion-determinant scale The combinatorial growth of configuration interaction (CI) has long limited this formally exact quantum chemistry method to only the smallest molecules. Here, we report a numerically exact CI calculation exceeding one quadrillion (10<sup>15</sup>) determinants, made possible by a lossless categorical compression strategy within the small-tensor-product distributed active space (STP-DAS) framework. This approach overcomes the traditional memory bottlenecks of CI by a numerically exact compression of the wavefunction representation and reformulating the most computationally demanding matrix-vector operations. Using this method, we performed a fully relativistic CI calculation of the ground state of HBrTe with over 10<sup>15</sup> complex-valued determinants in just 34.5 h on 1000 computing nodes-the largest CI calculation ever reported. We further achieved fast computation for systems with hundreds of billions of determinants on only a few compute nodes. Extensive benchmarks confirm that the method retains full numerical exactness while cutting memory and computational cost by orders of magnitude. Compared to previous state-of-the-art CI calculations, this work achieves a 1000 times increase in CI space, a 10<sup>6</sup>-fold increase in floating-point operations performed, and a 10<sup>6</sup>-fold improvement in computational speed. Computation ; Lossless compression ; Wave function ; Representation (politics) ; Scale (ratio) ; Categorical variable ; Configuration interaction ; Quantum computer ; Quantum Machine Learning in Materials Science ; Quantum many-body systems ; Cold Atom Physics and Bose-Einstein Condensates
Authors: Aaron S. Brewster | Daniel W. Paley⧓ | Asmit Bhowmick | David Moreau | I.D. Young | Derek Mendez | Daniel Tchoń | Billy K. Poon | Nicholas K. Sauter
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-05-04)
Field: Materials Science — Materials Chemistry

<i>cctbx.xfel</i> : a suite for processing serial crystallographic data The <i>cctbx.xfel</i> suite of processing programs and tools allows fast, visual analysis of serial diffraction images from synchrotrons and XFELs. Built on <i>DIALS</i> and <i>cctbx</i>, <i>cctbx.xfel</i> is designed for real-time and post-experiment processing with a fully featured graphical user interface. Users can quickly identify hitrates, view diffraction patterns, analyze unit-cell isomorphism using clustering, and merge data using a metadata tagging approach that allows on-the-fly organization and visualization of processing results. This paper describes the fundamental algorithms and command-line programs used by <i>cctbx.xfel</i>, including the two main program <i>dials.stills_process</i>, which performs spot-finding, indexing, geometric refinement, and integration, and <i>cctbx.xfel.merge</i>, which performs scaling, post-refinement, and merging. A discussion of merging statistics is presented and newer features are described, including random sub-sampling for indexing multi-lattice hits and Δ<i>CC</i> <sub>1/2</sub> filtering to remove outliers. Finally we show a complex, heterogeneous sample containing hexagonal and monoclinic isoforms in <i>P</i>6<sub>3</sub> and <i>P</i>2<sub>1</sub>. The isoforms are separated by unit cell clustering, and for each isoform we resolve a (pseudo-)merohedral indexing ambiguity. Computer science ; Cluster analysis ; Visualization ; Suite ; Graphical user interface ; Outlier ; Data mining ; Artificial intelligence ; Programming language Enzyme Structure and Function ; X-ray Diffraction in Crystallography ; Hydrocarbon exploration and reservoir analysis
Authors: Saurabh S. Sawant | François Léonard | Zhi Yao | Andrew Nonaka
Source: npj Computational Materials (article, 2025-04-24)
Field: Materials Science — Materials Chemistry

ELEQTRONeX: A GPU-accelerated exascale framework for non-equilibrium quantum transport in nanomaterials Non-equilibrium electronic quantum transport is crucial for existing and envisioned electronic, optoelectronic, and spintronic devices. Encompassing atomistic to mesoscopic length scales in the same nonequilibrium device simulations has been challenging due to the computational cost of high-fidelity coupled multiphysics and multiscale requirements. In this work, we present ELEQTRONeX (ELEctrostatic Quantum TRansport modeling Of Nanomaterials at eXascale), a massively parallel GPU-accelerated framework for self-consistently solving the nonequilibrium Green’s function formalism and electrostatics in complex device geometries. By customizing algorithms for GPU multithreading, we achieve significant improvement in computational time, and excellent scaling on up to 512 GPUs and billions of spatial grid cells. We validate our code by computing band structures, current-voltage characteristics, conductance, and drain-induced barrier lowering for various 3D configurations of carbon nanotube field-effect transistors, and demonstrate its suitability for complex device/material geometries where periodic approaches are not feasible, such as arrays of misaligned carbon nanotubes requiring fully 3D simulations. Exascale computing ; Computer science ; Quantum ; Parallel computing ; Nanomaterials ; Computational science ; Nanotechnology ; Physics ; Materials science ; Supercomputer ; Quantum mechanics Graphene research and applications ; Molecular Junctions and Nanostructures ; Quantum and electron transport phenomena
Authors: Trieu Nguyen | Aleksandra Karolak
Source: Biophysical Journal (article, 2025-02-01)
Field: Materials Science — Materials Chemistry

BPS2025 - Molecular design with generative AI: A transformer graph variational autoencoder approach Autoencoder ; Transformer ; Generative grammar ; Computer science ; Generative model ; Artificial intelligence ; Engineering ; Artificial neural network ; Electrical engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Yifei Qin | Xue-Xin Wei | Mingyuan Xu | Jia‐Qiang Wu | Miru Tang | Ting Ran | Hongming Chen
Source: ACS Omega (article, 2025-09-15)
Field: Materials Science — Materials Chemistry

Comprehensive Benchmark Study of Diffusion-Based 3D Molecular Generation Models Deep diffusion models have emerged as powerful tools for de novo molecular generation in three-dimensional (3D) conformational space, leveraging forward noising and reverse denoising processes to learn the probability distributions of molecular geometries and their physicochemical properties. However, systematic comparisons of their performance remain limited. Here, we present a comprehensive benchmark of nine state-of-the-art diffusion-based 3D molecular generative models trained on two representative data sets, QM9 and GEOM-Drugs. Evaluation was performed using four types of metrics, encompassing both two-dimensional (2D) structural features and 3D geometric characteristics. Our results demonstrate that nearly all models perform worse on 3D metrics compared to 2D metrics. Most generated 3D structures exhibit significant deviations from the energy-minimized reference, highlighting the persistent challenges in achieving accurate 3D spatial modeling. Furthermore, their effectiveness declines when producing 3D structures of larger and more complex molecules. Among the models, MiDi and EQGAT-diff consistently outperform the other models, with MiDi showing particularly robust performance. In summary, this study highlights recent advances in deep diffusion models for 3D molecular generation while delineating the key challenges that need to be addressed by next-generation approaches. Benchmark (surveying) ; Key (lock) ; Deep learning ; Generative grammar ; 3d model ; 3D modeling Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Yuchen Zhu | Shuwang Li | Jihong Chen | Donghai Zhao | Xiaorui Wang | Yitong Li | Yifei Liu | Yue Kong | Beichen Zhang | Chang Liu | Tingjun Hou | Chang‐Yu Hsieh
Source: Nature Computational Science (article, 2025-11-13)
Field: Materials Science — Materials Chemistry

SynGFN: learning across chemical space with generative flow-based molecular discovery Chemical space ; Limiting ; Generative grammar ; Key (lock) ; Generative model ; Space (punctuation) ; Generative Design Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Computational Drug Discovery Methods
Authors: François Cornet | P. S. DESHMUKH | Bardi Benediktsson | Mikkel N. Schmidt | Arghya Bhowmik
Source: Machine Learning Science and Technology (article, 2025-05-22)
Field: Materials Science — Materials Chemistry

Improving generative inverse design of molecular catalysts in small data regime Abstract Deep generative models are a powerful tool for exploring the chemical space within inverse-design workflows; however, their effectiveness relies on sufficient training data and effective mechanisms for guiding the model to optimize specific properties. We demonstrate that designing an expert-informed data representation and training procedure allows leveraging data augmentation while maintaining the required sampling controllability. We focus our discussion on a specific class of compounds (transition metal complexes), and a popular class of generative models (equivariant diffusion models), although we envision that the approach could be extended to other chemical spaces and model types. Through experiments, we demonstrate that augmenting the training database with generic but related unlabeled data enables a practical level of performance to be reached. Inverse ; Generative grammar ; Catalysis ; Computer science ; Environmental science ; Chemistry ; Mathematics ; Artificial intelligence ; Biochemistry ; Geometry Machine Learning in Materials Science
Authors: Chenglong Qin | Shiyin Ma | Liang Zhao | Jinde Liu | Jiang Gang
Source: International Journal of Hydrogen Energy (article, 2025-09-01)
Field: Materials Science — Materials Chemistry

Generative design of hydrogen storage multi-principal element alloys based on variational autoencoders Generative grammar ; Hydrogen storage ; Element (criminal law) ; Computer science ; Hydrogen ; Principal (computer security) ; Artificial intelligence ; Materials science ; Pattern recognition (psychology) ; Metallurgy ; Chemistry ; Alloy Hydrogen Storage and Materials ; Titanium Alloys Microstructure and Properties ; Nuclear Materials and Properties
Authors: Or Madar | Ohad Fried
Source:  (article, 2025-06-10)
Field: Materials Science — Materials Chemistry

Tiled Diffusion Image tiling—the seamless connection of disparate images to create a coherent visual field—is crucial for applications such as texture creation, video game asset development, and digital art. Traditionally, tiles have been constructed manually, a method that poses significant limitations in scalability and flexibility. Recent research has attempted to automate this process using generative models. However, current approaches primarily focus on tiling textures and manipulating models for single-image generation, without inherently supporting the creation of multiple interconnected tiles across diverse domains. This paper presents Tiled Diffusion, a novel approach that extends the capabilities of diffusion models to accommodate the generation of cohesive tiling patterns across various domains of image synthesis that require tiling. Our method supports a wide range of tiling scenarios, from self-tiling to complex many-to-many connections, enabling seamless integration of multiple images. Tiled Diffusion automates the tiling process, eliminating the need for manual intervention and enhancing creative possibilities in various applications, such as seamlessly tiling of existing images, tiled texture creation, and 360° synthesis. Computer science ; Diffusion ; Physics ; Thermodynamics Quasicrystal Structures and Properties
Authors: Zhiyuan Liu | Yanchen Luo | Han Huang | Enzhi Zhang | Sihang Li | Junfeng Fang | Yaorui Shi | Xiang Wang | Kenji Kawaguchi | Tat‐Seng Chua
Source: ArXiv.org (preprint, 2025-02-18)
Field: Materials Science — Materials Chemistry

NExT-Mol: 3D Diffusion Meets 1D Language Modeling for 3D Molecule Generation 3D molecule generation is crucial for drug discovery and material design. While prior efforts focus on 3D diffusion models for their benefits in modeling continuous 3D conformers, they overlook the advantages of 1D SELFIES-based Language Models (LMs), which can generate 100% valid molecules and leverage the billion-scale 1D molecule datasets. To combine these advantages for 3D molecule generation, we propose a foundation model -- NExT-Mol: 3D Diffusion Meets 1D Language Modeling for 3D Molecule Generation. NExT-Mol uses an extensively pretrained molecule LM for 1D molecule generation, and subsequently predicts the generated molecule's 3D conformers with a 3D diffusion model. We enhance NExT-Mol's performance by scaling up the LM's model size, refining the diffusion neural architecture, and applying 1D to 3D transfer learning. Notably, our 1D molecule LM significantly outperforms baselines in distributional similarity while ensuring validity, and our 3D diffusion model achieves leading performances in conformer prediction. Given these improvements in 1D and 3D modeling, NExT-Mol achieves a 26% relative improvement in 3D FCD for de novo 3D generation on GEOM-DRUGS, and a 13% average relative gain for conditional 3D generation on QM9-2014. Our codes and pretrained checkpoints are available at https://github.com/acharkq/NExT-Mol. Diffusion ; Molecule ; Mole ; Chemistry ; Computer science ; Physics ; Biochemistry ; Thermodynamics ; Organic chemistry Machine Learning in Materials Science