jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ching-Wen Lou | Guodong Chen
Source:  (article, 2025-04-11)
Field: Chemistry — Inorganic Chemistry

Method For Generating Furniture Material Textures With the popularization of customized furniture design, efficiently generating high-quality material textures has become a key challenge to improve design efficiency. This article proposes a solution based on diffusion model, combining improved Stable Diffusion and TEXTure framework to achieve 2D image mapping generation and 3D texture direct synthesis, respectively. Combining the advantages of both, a design process of "2D rapid proposal -3D fine correction" is formed to balance efficiency and quality. This article provides a practical technical path for furniture material generation, promoting the digitalization and personalized development of the design process. In the future, it is necessary to further optimize the automatic adaptation ability of high reflection material generation and 3D texture. Texture (cosmology) ; Process (computing) ; Reflection (computer programming) ; Key (lock) ; Texture mapping ; Adaptation (eye) ; Path (computing) Pigment Synthesis and Properties ; Generative Adversarial Networks and Image Synthesis ; Architecture and Computational Design
Authors: Wentao Li | Yizhe Chen | Jiang Qiu | Xiaonan Wang
Source: ArXiv.org (preprint, 2025-04-12)
Field: Materials Science — Materials Chemistry

MatWheel: Addressing Data Scarcity in Materials Science Through Synthetic Data Data scarcity and the high cost of annotation have long been persistent challenges in the field of materials science. Inspired by its potential in other fields like computer vision, we propose the MatWheel framework, which train the material property prediction model using the synthetic data generated by the conditional generative model. We explore two scenarios: fully-supervised and semi-supervised learning. Using CGCNN for property prediction and Con-CDVAE as the conditional generative model, experiments on two data-scarce material property datasets from Matminer database are conducted. Results show that synthetic data has potential in extreme data-scarce scenarios, achieving performance close to or exceeding that of real samples in all two tasks. We also find that pseudo-labels have little impact on generated data quality. Future work will integrate advanced models and optimize generation conditions to boost the effectiveness of the materials data flywheel. Property (philosophy) ; Synthetic data ; Scarcity ; Field (mathematics) ; Generative grammar ; Data modeling Machine Learning in Materials Science
Authors: Naoki Shimosako | Eiji Miyazaki | Yukino Masuda | Kakuji Tojo | Miki Matsuoka | Masaya Moriyama | Taro Kinumoto
Source: Research Square (preprint, 2025-10-23)
Field: Materials Science — General Materials Science

Outgassing properties of bamboo-derived cellulose nanofiber sheets containing hemicellulose and lignin evaluated by ASTM E1559 for space applications Outgassing ; Hemicellulose ; Cellulose ; Lignin ; Nanofiber ; Raw material ; Spacecraft Material Properties and Applications ; Additive Manufacturing and 3D Printing Technologies
Authors: B. J. Kim | Islambek Ashyrmamatov | Umit Volkan Ucak | Juyong Lee
Source: ACS Omega (article, 2025-12-16)
Field: Materials Science — Materials Chemistry

Fluorophore Design via Generative Modeling in a Curated Subspace We present a fluorescence-oriented molecular design strategy that integrates multiple generative models within a chemically constrained and photophysically relevant subspace. Rather than relying on large-scale data sets or model-specific tuning, our approach builds on a curated training set constructed exclusively from atom-in-SMILES (AIS) fragments spanning chromophore-like molecules. The pipeline accommodates reinforcement learning-based ReLeaSE, which employs AIS-based tokenization, MolDQN, which utilizes a graph-based molecular representation, and the combinatorial optimization-based MolFinder, which operates on SMILES strings, each guided by a semiheuristic objective function targeting excitation energy, oscillator strength, and fluorophore similarity. Despite the compact training corpus, the pipeline yields structurally novel and optically active molecules, validated via QM calculations at the sTDA/CAM-B3LYP level. Our findings demonstrate that strategic data curation and property-driven integration can enable general-purpose models to succeed in specialized discovery tasks such as de novo fluorophore design. Pipeline (software) ; Fluorophore ; Set (abstract data type) ; Generative model ; Generative grammar ; Function (biology) ; Training set ; Subspace topology Machine Learning in Materials Science ; Advanced Fluorescence Microscopy Techniques ; Computational Drug Discovery Methods
Authors: Henrik Schopmans | Pascal Friederich
Source: Repository KITopen (Karlsruhe Institute of Technology) (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Temperature-Annealed Boltzmann Generators Efficient sampling of unnormalized probability densities such as the Boltzmann distribution of molecular systems is a longstanding challenge. Next to conventional approaches like molecular dynamics or Markov chain Monte Carlo, variational approaches, such as training normalizing flows with the reverse Kullback-Leibler divergence, have been introduced. However, such methods are prone to mode collapse and often do not learn to sample the full configurational space. Here, we present temperature-annealed Boltzmann generators (TA-BG) to address this challenge. First, we demonstrate that training a normalizing flow with the reverse Kullback-Leibler divergence at high temperatures is possible without mode collapse. Furthermore, we introduce a reweighting-based training objective to anneal the distribution to lower target temperatures. We apply this methodology to three molecular systems of increasing complexity and, compared to the baseline, achieve better results in almost all metrics while requiring up to three times fewer target energy evaluations. For the largest system, our approach is the only method that accurately resolves the metastable states of the system. Markov chain ; Boltzmann distribution ; Markov chain Monte Carlo ; Boltzmann constant ; Divergence (linguistics) ; Mode (computer interface) ; Metastability ; Sampling (signal processing) ; Markov process Machine Learning in Materials Science ; Generative Adversarial Networks and Image Synthesis ; Model Reduction and Neural Networks
Authors: Orkun Furat | S. V. Weber | Anina Dufter | Johannes Schubert | René Rekers | M. Łuczak | Erik Glatt | Andreas Wiegmann | Jürgen Janek | Anja Bielefeld | Volker Schmidt
Source: Advanced Intelligent Systems (article, 2025-09-09)
Field: Materials Science — Surfaces, Coatings and Films

Generative Adversarial Framework to Calibrate Excursion Set Models for the 3D Morphology of All‐Solid‐State Battery Cathodes This article presents a computational method for generating virtual 3D morphologies of functional materials using low‐parametric stochastic geometry models, that is, digital twins, calibrated with 2D microscopy images. These digital twins allow systematic parameter variations to simulate various morphologies, which can be deployed for virtual materials testing by means of spatially resolved numerical simulations of macroscopic properties. Generative adversarial networks (GANs) have gained popularity for calibrating models to generate realistic 3D morphologies. However, GANs often comprise numerous uninterpretable parameters, making systematic variation of morphologies for virtual materials testing challenging. In contrast, low‐parametric stochastic geometry models (e.g., based on Gaussian random fields) enable targeted variation but may struggle to mimic complex morphologies. Combining GANs with advanced stochastic geometry models (e.g., excursion sets of more general random fields) addresses these limitations, allowing model calibration solely from 2D image data. This approach is demonstrated by generating digital twins for the morphology of microstructures in all‐solid‐state battery (ASSB) cathodes. Since the digital twins are parametric, they support systematic exploration of structural scenarios and their macroscopic properties. The proposed method facilitates simulation studies for optimizing 3D morphologies, benefiting not only ASSB cathodes but also other materials with similar structures. Stochastic geometry ; Calibration ; Set (abstract data type) ; Generative grammar ; Gaussian ; Excursion ; Stochastic process ; Stochastic modelling ; Gaussian process Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications
Authors: Orkun Furat | S. V. Weber | Johannes Schubert | René Rekers | M. Łuczak | Erik Glatt | Andreas Wiegmann | Jürgen Janek | Anja Bielefeld | Volker Schmidt
Source: ArXiv.org (preprint, 2025-03-21)
Field: Materials Science — Materials Chemistry

Generative adversarial framework to calibrate excursion set models for the 3D morphology of all-solid-state battery cathodes This paper presents a computational method for generating virtual 3D morphologies of functional materials using low-parametric stochastic geometry models, i.e., digital twins, calibrated with 2D microscopy images. These digital twins allow systematic parameter variations to simulate various morphologies, that can be deployed for virtual materials testing by means of spatially resolved numerical simulations of macroscopic properties. Generative adversarial networks (GANs) have gained popularity for calibrating models to generate realistic 3D morphologies. However, GANs often comprise of numerous uninterpretable parameters make systematic variation of morphologies for virtual materials testing challenging. In contrast, low-parametric stochastic geometry models (e.g., based on Gaussian random fields) enable targeted variation but may struggle to mimic complex morphologies. Combining GANs with advanced stochastic geometry models (e.g., excursion sets of more general random fields) addresses these limitations, allowing model calibration solely from 2D image data. This approach is demonstrated by generating a digital twin of all-solid-state battery (ASSB) cathodes. Since the digital twins are parametric, they support systematic exploration of structural scenarios and their macroscopic properties. The proposed method facilitates simulation studies for optimizing 3D morphologies, benefiting not only ASSB cathodes but also other materials with similar structures. Stochastic geometry ; Calibration ; Set (abstract data type) ; Generative grammar ; Excursion ; Gaussian ; Stochastic process ; Gaussian process ; Stochastic modelling Machine Learning in Materials Science ; Advancements in Battery Materials ; Advanced Electron Microscopy Techniques and Applications
Authors: Garen Avedissian | Eoin Dolan | Beatriz Martín‐García | Marco Gobbi | Fèlix Casanova | Luis E. Hueso
Source: Small (article, 2025-04-27)
Field: Materials Science — Materials Chemistry

Enhanced Spin Relaxation Time in a 2D/1D Van Der Waals Hybrid Heterostructure The key attraction of graphene in spintronics arises from its high electronic mobility and low intrinsic spin-orbit coupling (SOC), which enable long spin relaxation times. However, the weak SOC limits the ability to control spin currents within graphene. A promising strategy for enhancing the spin functionalities of graphene is to introduce proximity effects with other materials. In this context, molecular compounds show great potential for tuning the spin properties of graphene. Here, a novel fabrication methodology is presented that integrates molecular compounds with graphene-based spintronic nanodevices using stencil hexagonal boron nitride (hBN) masks, allowing us to investigate the resulting spin-related effects. First, our non-destructive fabrication technique, confirmed by micro-Raman spectroscopy, preserves the integrity of the molecular compound. Moreover, by combining experimental Hanle precession with a 3D spin diffusion model, it is demonstrated that fullerene (C<sub>60</sub>) molecules enhance the spin relaxation time of the graphene. The established fabrication methodology can be further expanded to integrate graphene with exotic molecular compounds, such as photochromic and spin cross-over molecules, enabling the exploration of proximity-induced spin phenomena and paving the way for spin-based multifunctional nanodevices. Spintronics ; Graphene ; Materials science ; Nanotechnology ; Spin (aerodynamics) ; Raman spectroscopy ; van der Waals force ; Heterojunction ; Spin engineering ; Fabrication ; Chemical physics ; Spin polarization ; Molecule ; Condensed matter physics ; Optoelectronics ; Chemistry ; Physics ; Ferromagnetism Graphene research and applications ; 2D Materials and Applications ; Quantum and electron transport phenomena
Authors: Yuzhi Xu (1645957) | Yongrui Luo (17268400) | Bo Li (112195) | Weikang Jiang (12899964) | Jinyu Zhang (310913) | Jiangbo Wei (11253804) | Hanzhi Bai (8696538) | Zhiqiang Wang (48987) | Jiankai Ge (17551018) | Ruiming Lin (8049983) | Zehan Mi (20383819) | Haozhe Zhang (6584819) | Yifeng Tang (13241270) | Michael S. Jones (10276805) | Xiaotian Li (394967) | John Z.H. Zhang (2617839) | Cheng-Wei Ju (9142674)
Source: Figshare (article, 2025-06-30)
Field: Materials Science — Materials Chemistry

Exploring OptimizedOrganic Fluorophore Search throughExperimental Data-Driven Adaptive β‑VAE Designing organic fluorescent molecules with tailored optical properties has been a long-standing challenge. Recently, statistical models have opened new avenues for tackling this problem. Inverse design has attracted considerable attention in organic materials science; however, most existing approaches focus on arbitrary design or theoretical properties. Here, we introduce a strategy that enables the direct optimization of specific experimental properties during the inverse design process. Our method employs an adaptive β-variational autoencoder (adaptive β-VAE) combined with a latent vector-based prediction model. By dynamically tuning the Kullback–Leibler divergence scaling factor (β) and employing a separate training strategy, we enhance both the robustness of the generator and the diversity of the generated molecules. We demonstrate that latent vectors from the adaptive β-VAE serve as powerful inputs for downstream prediction models of experimental properties, such as fluorescence energy and quantum yield. Our optimized search framework for organic fluorescent materialsguided by gradients in latent space and validated by newly synthesized molecules sampled from optimal regions in the high-dimensional spaceshows strong potential for broader applications in the design of diverse organic materials. Robustness (evolution) ; Scaling ; Inverse ; Fluorophore ; Inverse problem ; Autoencoder ; Discriminator Machine Learning in Materials Science ; Luminescence and Fluorescent Materials ; Organic Electronics and Photovoltaics
Authors: Haoyang Yu | Haixin Wang | Bei Zhu | Xue-Xin Wei | Bing-Xue Du | Hui Yu | Jian‐Yu Shi
Source: Big Data Mining and Analytics (article, 2025-09-15)
Field: Materials Science — Materials Chemistry

Compound Cocrystal Prediction via Dual-View Learning Framework Under Adversarial Consistency and Complementarity Constraints It is a vital step to find cocrystal formers of drugs in drug development. Dual-View Learning (DVL) has achieved inspiring progress in predicting cocrystals since compounds can be represented in a dual-source manner (i.e., sequence and 2D structures). Nonetheless, it is still an ongoing issue that the performance of existing DVL-based approaches depend on how appropriate the combination of dual view is. Furthermore, there is a need to elucidate what atoms are crucial to form a cocrystal of two compounds. This work holds an assumption that the orthogonal separation of view representations into view-shared representations and view-specific representations can eliminate the redundancy and irrelevant features among dual view. To address these issues, this work elaborates a novel DVL framework for predicting Compound Cocrystal (DVL-CC). The framework includes molecule encoders of dual view, a dual-view combinator, and a binary predictor. Especially, the dual-view combinator orthogonally disentangles view-shared and view-specific molecule representations from raw view representations by an elaborate Generative Adversarial Network (GAN) based consistency learner and a set of complementary constraints. The comparison with state-of-the-art DVL-based methods demonstrates the superiority of DVL-CC. Also, the comprehensive ablation studies validate and illustrate how its main components contribute to the cocrystal prediction, including individual-view representations, the dual-view combinator, the consistency learner, and the complementary constraints. Furthermore, a case study illustrates the interpretability of DVL-CC by indicating crucial atoms associated with cocrystal conformation patterns between compounds. It is anticipated that this work can boost drug development.The code and data underlying this article are available at https://github.com/savior-22/DVL-CC. Cocrystal ; Interpretability ; Consistency (knowledge bases) ; Complementarity (molecular biology) ; Adversarial system ; Coding (social sciences) ; Binary number Machine Learning in Materials Science ; Crystallography and molecular interactions ; Crystallization and Solubility Studies
Authors: Rees Chang | Anna Pak | Alfredo J. Guerra | Ni Zhan | Nick Richardson | Elif Ertekin | Ryan P. Adams
Source: ArXiv.org (preprint, 2025-05-16)
Field: Materials Science — Materials Chemistry

Space Group Equivariant Crystal Diffusion Accelerating inverse design of crystalline materials with generative models has significant implications for a range of technologies. Unlike other atomic systems, 3D crystals are invariant to discrete groups of isometries called the space groups. Crucially, these space group symmetries are known to heavily influence materials properties. We propose SGEquiDiff, a crystal generative model which naturally handles space group constraints with space group invariant likelihoods. SGEquiD-iff consists of an SE(3)-invariant, telescoping discrete sampler of crystal lattices; permutation-invariant, transformer-based autoregressive sampling of Wyckoff positions, elements, and numbers of symmetrically unique atoms; and space group equivariant diffusion of atomic coordinates. We show that space group equivariant vector fields automatically live in the tangent spaces of the Wyckoff positions. SGEquiDiff achieves state-of-the-art performance on standard benchmark datasets as assessed by quantitative proxy metrics and quantum mechanical calculations. Our code is available at https://github.com/rees-c/sgequidiff. Equivariant map ; Homogeneous space ; Group (periodic table) ; Invariant (physics) ; Unitary group ; Isometry group ; Space (punctuation) Machine Learning in Materials Science ; Quasicrystal Structures and Properties ; 3D Shape Modeling and Analysis
Authors: Johann Ewender | Rafael Auras | Uruchaya Sonchaeng | Frank Welle
Source: Molecules (article, 2025-05-06)
Field: Materials Science — Biomaterials

Diffusion Coefficients and Activation Energies of Diffusion of Organic Molecules in Poly(lactic acid) Films Poly(lactic acid) (PLA) is one of the most important bio-based and industrial compostable materials in food packaging. Its barrier properties towards oxygen and moisture are well documented. However, data on barrier properties of PLA towards organic molecules are scarce in the literature. This study investigated the diffusion of various organic molecules, including <i>n</i>-alkanes, 1-alcohols, 2-ketones, ethers, esters, amines, and aromatics, in two commercial PLA films with thicknesses of 20 µm and 30 µm. The diffusion coefficient (<i>D</i><sub>P</sub>) values were determined from lag time in permeation tests conducted at temperatures ranging from 20 °C to 90 °C. The films were also characterized in terms of crystallinity, rigid and mobile amorphous fractions, and molecular weight. Activation energies (<i>E</i><sub>A</sub>) were calculated based on the temperature dependence of the <i>D</i><sub>P</sub> using the Arrhenius approach. In total, 290 <i>D</i><sub>P</sub> values for 55 individual substances were determined, and 38 <i>E</i><sub>A</sub> values were derived from these data. The <i>E</i><sub>A</sub> correlated well with the molecular volume of the investigated substances. Moreover, the pre-exponential factor <i>D</i><sub>0</sub> showed a correlation with <i>E</i><sub>A</sub>. These correlations enabled the establishment of diffusion modeling parameters for PLA, allowing the prediction of <i>D</i><sub>P</sub> for untested substances. The diffusion behavior of PLA was further compared with the literature data for polyethylene terephthalate and polyethylene naphthalate, providing insights into the relative performance of these materials. Crystallinity ; Diffusion ; Arrhenius equation ; Amorphous solid ; Lactic acid ; Permeation ; Polyethylene ; Polyethylene terephthalate ; Chemical engineering ; Chemistry ; Polymer chemistry ; Organic chemistry ; Activation energy ; Molecule ; Materials science ; Analytical Chemistry (journal) ; Physical chemistry ; Thermodynamics ; Composite material ; Crystallography ; Membrane biodegradable polymer synthesis and properties ; Nanocomposite Films for Food Packaging ; Polymer crystallization and properties
Authors: Marcus Schwarting | Logan Ward | Nathaniel Hudson | Xiaoli Yan | Ben Blaiszik | Santanu Chaudhuri | E. A. Huerta | Ian Foster
Source:  (article, 2025-09-15)
Field: Materials Science — Materials Chemistry

Steering an Active Learning Workflow Towards Novel Materials Discovery via Queue Prioritization Generative AI poses both opportunities and risks for solving inverse design problems in the sciences. Generative tools provide the ability to expand and refine a search space autonomously, but do so at the cost of exploring low-quality regions until sufficiently fine tuned. Here, we propose a queue prioritization algorithm that combines generative modeling and active learning in the context of a distributed workflow for exploring complex design spaces. We find that incorporating an active learning model to prioritize top design candidates can prevent a generative AI workflow from expending resources on nonsensical candidates and halt potential generative model decay. For an existing generative AI workflow for discovering novel molecular structure candidates for carbon capture, our active learning approach significantly increases the number of high-quality candidates identified by the generative model. We find that, out of 1000 novel candidates, our workflow without active learning can generate an average of 281 high-performing candidates, while our proposed prioritization with active learning can generate an average 604 high-performing candidates. Workflow ; Generative grammar ; Active learning (machine learning) ; Context (archaeology) ; Generative Design ; Generative model ; Prioritization Machine Learning in Materials Science ; Machine Learning and Algorithms ; Advanced Database Systems and Queries
Authors: Madhumathy Parthiban | A. Mariappan | T. Arumuganathan | Bhaskaran Shankar
Source: Inorganic Chemistry Communications (article, 2025-12-12)
Field: Chemistry — Inorganic Chemistry

Facile in-situ synthesis of HKUST-1/AHM (POM) composite for effective removal of methylene blue Adsorption ; Methylene blue ; Aqueous solution ; Composite number ; Gibbs free energy ; Exothermic reaction ; Cationic polymerization ; Specific surface area Metal-Organic Frameworks: Synthesis and Applications ; Adsorption and biosorption for pollutant removal ; Supercapacitor Materials and Fabrication