jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yuhui Ding | Thomas Hofmann
Source: ArXiv.org (preprint, 2025-06-11)
Field: Materials Science — Materials Chemistry

Scalable Non-Equivariant 3D Molecule Generation via Rotational Alignment Equivariant diffusion models have achieved impressive performance in 3D molecule generation. These models incorporate Euclidean symmetries of 3D molecules by utilizing an SE(3)-equivariant denoising network. However, specialized equivariant architectures limit the scalability and efficiency of diffusion models. In this paper, we propose an approach that relaxes such equivariance constraints. Specifically, our approach learns a sample-dependent SO(3) transformation for each molecule to construct an aligned latent space. A non-equivariant diffusion model is then trained over the aligned representations. Experimental results demonstrate that our approach performs significantly better than previously reported non-equivariant models. It yields sample quality comparable to state-of-the-art equivariant diffusion models and offers improved training and sampling efficiency. Our code is available at https://github.com/skeletondyh/RADM Equivariant map ; Diffusion ; Scalability ; Euclidean geometry ; Noise (video) ; Transformation (genetics) ; Homogeneous space ; Limit (mathematics) ; Construct (python library) Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Computational Drug Discovery Methods
Authors: Santiago Miret | Marta Skreta | Geemi P. Wellawatte | Stefano Martiniani | N. M. Anoop Krishnan | George Karypis | Kevin Maik Jablonka
Source: Machine Learning Science and Technology (article, 2025-09-30)
Field: Materials Science — Materials Chemistry

Perspective on artificial intelligence for accelerated materials design (AI4Mat) workshops in 2024 Abstract The intersection of artificial intelligence and materials science has become increasingly interconnected, driving ambitious research initiatives across both fields. Since 2022, the AI for accelerated materials design (AI4Mat) workshops have provided a leading venue for showcasing cutting-edge advances in this emerging interdisciplinary domain while fostering critical discussions about the most pressing scientific and technical challenges. In 2024, AI4Mat hosted workshops at BOKU University and NeurIPS 2024, attracting researchers and practitioners from academia, industry, and government institutions worldwide. These workshops explored diverse research areas currently shaping the field, with participants engaging in comprehensive discussions that addressed the intersection’s most significant challenges from scientific, technical, and commercial perspectives. Through this holistic approach, AI4Mat’s 2024 workshops successfully illuminated the multifaceted nature of AI-driven materials research, highlighting both current achievements and future opportunities in this rapidly evolving field. In this article, the AI4Mat-2024 organizing committee presents key insights from our workshops and community discussions, outlining critical challenges in this emerging field while summarizing the latest advances in AI-accelerated materials design. We examine persistent challenges around data creation and reproducibility, alongside the growing commercial interest in developing new markets and optimization materials production processes at scale. The article also highlights significant research breakthroughs showcased at AI4Mat, including the application of large language models to accelerate materials science tasks, the development of sophisticated generative models for materials discovery, and the growing demand for interpretable AI methodologies that provide transparent insights into materials behavior. Government (linguistics) ; Field (mathematics) ; Domain (mathematical analysis) ; Generative grammar ; Perspective (graphical) ; Grand Challenges Machine Learning in Materials Science
Authors: Michael Birsak | John Femiani | Biao Zhang | Peter Wonka
Source: arXiv (Cornell University) (preprint, 2025-01-27)
Field: Chemistry — Organic Chemistry

MatCLIP: Light- and Shape-Insensitive Assignment of PBR Material Models Assigning realistic materials to 3D models remains a significant challenge in computer graphics. We propose MatCLIP, a novel method that extracts shape- and lighting-insensitive descriptors of Physically Based Rendering (PBR) materials to assign plausible textures to 3D objects based on images, such as the output of Latent Diffusion Models (LDMs) or photographs. Matching PBR materials to static images is challenging because the PBR representation captures the dynamic appearance of materials under varying viewing angles, shapes, and lighting conditions. By extending an Alpha-CLIP-based model on material renderings across diverse shapes and lighting, and encoding multiple viewing conditions for PBR materials, our approach generates descriptors that bridge the domains of PBR representations with photographs or renderings, including LDM outputs. This enables consistent material assignments without requiring explicit knowledge of material relationships between different parts of an object. MatCLIP achieves a top-1 classification accuracy of 76.6%, outperforming state-of-the-art methods such as PhotoShape and MatAtlas by over 15 percentage points on publicly available datasets. Our method can be used to construct material assignments for 3D shape datasets such as ShapeNet, 3DCoMPaT++, and Objaverse. All code and data will be released. Computer science Advanced Polymer Synthesis and Characterization ; Silicone and Siloxane Chemistry ; Modular Robots and Swarm Intelligence
Authors: Okhotin, Andrey | Nakhodnov, Maksim | Kazeev, Nikita | Ustyuzhanin, Andrey E | Vetrov, Dmitry
Source: arXiv (Cornell University) (preprint, 2025-11-18)
Field: Materials Science — Materials Chemistry

MiAD: Mirage Atom Diffusion for De Novo Crystal Generation In recent years, diffusion-based models have demonstrated exceptional performance in searching for simultaneously stable, unique, and novel (S.U.N.) crystalline materials. However, most of these models don't have the ability to change the number of atoms in the crystal during the generation process, which limits the variability of model sampling trajectories. In this paper, we demonstrate the severity of this restriction and introduce a simple yet powerful technique, mirage infusion, which enables diffusion models to change the state of the atoms that make up the crystal from existent to non-existent (mirage) and vice versa. We show that this technique improves model quality by up to $\times2.5$ compared to the same model without this modification. The resulting model, Mirage Atom Diffusion (MiAD), is an equivariant joint diffusion model for de novo crystal generation that is capable of altering the number of atoms during the generation process. MiAD achieves an $8.2\%$ S.U.N. rate on the MP-20 dataset, which substantially exceeds existing state-of-the-art approaches. The source code can be found at \href{https://github.com/andrey-okhotin/miad.git}{\texttt{github.com/andrey-okhotin/miad}}. Crystal (programming language) ; Diffusion ; Atom (system on chip) ; Crystal structure ; Quality (philosophy) ; Code (set theory) Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Generative Adversarial Networks and Image Synthesis
Authors: Lingfeng Pan | Peishan Ding | Xiaotao Zheng
Source: Engineering Failure Analysis (article, 2025-11-27)
Field: Materials Science — Materials Chemistry

Creep rupture and Stress–Corrosion interaction of 316LN stainless steel in Oxygen-Saturated sodium at 700 °C Creep ; Corrosion ; Intergranular corrosion ; Sodium ; Microstructure ; Stress (linguistics) ; Diffusion ; Carbide Nuclear Materials and Properties ; High Temperature Alloys and Creep ; Nuclear reactor physics and engineering
Authors: Zhipeng Xu | Huili Guo | Fulin Shang
Source: Journal of the Brazilian Society of Mechanical Sciences and Engineering (article, 2025-11-25)
Field: Materials Science — Materials Chemistry

Non-Fick mechanical-diffusion with space-dependent diffusivity and time-domain finite element method for transient impact responses analysis Thermal diffusivity ; Finite element method ; Transient (computer programming) ; Dimensionless quantity ; Work (physics) ; Nonlinear system ; Coupling (piping) ; Shock (circulatory) Nonlocal and gradient elasticity in micro/nano structures ; Elasticity and Material Modeling ; Structural Response to Dynamic Loads
Authors: Madeline A. Stricklin | James E. Lee | Ryan Farley | Rachel C. Huber | A. C. Aiken
Source: Data Science in Science (article, 2025-12-30)
Field: Materials Science — Materials Chemistry

Resolving Mixtures of Soot Characterized by SP-AMS Spectra Using a Latent Dirichlet Allocation Model Soot produced by detonation or combustion events exhibits different chemical properties depending on the fuel, device construction, and environmental conditions in which the event occurs. These properties can be useful for defining relevant signatures for probabilistically identifying the different types of events that occurred, based on the soot that is produced from these events. However, it is rare to observe samples of soot from a detonation or combustion that are not contaminated by outside particles. In this paper, we present a method for resolving mixtures of soot to determine the contributions of sources that may be present in samples of recovered soot. We use Latent Dirichlet Allocation to describe the generative process for a sample of recovered soot, and use Variational Bayesian Inference to learn about the parameters associated with the generative model. We demonstrate the utility of this method by considering real samples of mixtures of soot under various frameworks to show that the model is able to identify the different components present in a sample of soot as well as their mixing proportions. Soot ; Combustion ; Detonation ; Sample (material) ; Event (particle physics) ; Mixing (physics) ; Generative model ; Spectral line Machine Learning in Materials Science ; Advanced Combustion Engine Technologies ; Bayesian Methods and Mixture Models
Authors: Maximilian Schebek | Nikolas M. Froböse | Bettina G. Keller | Jutta Rogal
Source: arXiv (Cornell University) (preprint, 2025-12-20)
Field: Materials Science — Materials Chemistry

Estimating Solvation Free Energies with Boltzmann Generators Accurate calculations of solvation free energies remain a central challenge in molecular simulations, often requiring extensive sampling and numerous alchemical intermediates to ensure sufficient overlap between phase-space distributions of a solute in the gas phase and in solution. Here, we introduce a computational framework based on normalizing flows that directly maps solvent configurations between solutes of different sizes, and compare the accuracy and efficiency to conventional free energy estimates. For a Lennard-Jones solvent, we demonstrate that this approach yields acceptable accuracy in estimating free energy differences for challenging transformations, such as solute growth or increased solute-solute separation, which typically demand multiple intermediate simulation steps along the transformation. Analysis of radial distribution functions indicates that the flow generates physically meaningful solvent rearrangements, substantially enhancing configurational overlap between states in configuration space. These results suggest flow-based models as a promising alternative to traditional free energy estimation methods. Solvation ; Sampling (signal processing) ; Energy (signal processing) ; Flow (mathematics) ; Solvent models ; Phase (matter) ; Process (computing) ; Distribution (mathematics) Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Chaitanya Bhave | Mathew Swisher | Jacob Hirschhorn | Ryan Sweet | Pierre-Clément A. Simon | Larry K. Aagesen
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

A Mechanistic, Multiscale Model for Predicting Pd Penetration in Triso Fuels Using Bison Penetration (warfare) ; Nuclear engineering ; Environmental science ; Materials science ; Engineering ; Operations research Nuclear Materials and Properties ; Fusion materials and technologies ; Nuclear reactor physics and engineering
Authors: Pin Chen | Qing Mo | Zexin Xu | Xianwei Zhang | Yutong Lu
Source: CCF Transactions on High Performance Computing (article, 2025-04-01)
Field: Materials Science — Materials Chemistry

Star-gen: an HPC-AI framework for constructing large-scale computational materials database Computer science ; Star (game theory) ; Database ; Scale (ratio) ; Parallel computing ; Physics ; Geography ; Astrophysics ; Cartography Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Catalysis and Oxidation Reactions
Authors: Can Polat | Hasan Kurban | Erchin Serpedin | Mustafa Kurban
Source: ArXiv.org (preprint, 2025-06-16)
Field: Materials Science — Materials Chemistry

Stress-Testing Multimodal Foundation Models for Crystallographic Reasoning Evaluating foundation models for crystallographic reasoning requires benchmarks that isolate generalization behavior while enforcing physical constraints. This work introduces a multiscale multicrystal dataset with two physically grounded evaluation protocols to stress-test multimodal generative models. The Spatial-Exclusion benchmark withholds all supercells of a given radius from a diverse dataset, enabling controlled assessments of spatial interpolation and extrapolation. The Compositional-Exclusion benchmark omits all samples of a specific chemical composition, probing generalization across stoichiometries. Nine vision--language foundation models are prompted with crystallographic images and textual context to generate structural annotations. Responses are evaluated via (i) relative errors in lattice parameters and density, (ii) a physics-consistency index penalizing volumetric violations, and (iii) a hallucination score capturing geometric outliers and invalid space-group predictions. These benchmarks establish a reproducible, physically informed framework for assessing generalization, consistency, and reliability in large-scale multimodal models. Dataset and code are available at https://github.com/KurbanIntelligenceLab/StressTestingMMFMinCR. Generalization ; Benchmark (surveying) ; Foundation (evidence) ; Outlier ; Generative grammar ; Context (archaeology) Machine Learning in Materials Science
Authors: Emily
Pachêco Squizatto (22102149) | Iare S. Ribeiro (22102152) | Marcos V. S. Pereira (22102155) | Fábio Junior
M. Novaes (9427975) | Márcio J. da Silva (22102158) | Luciano G. Moura (2236081) | Gilberto R. da Silva Junior (22102161) | Renê C. da Silva (22102164) | Jemmyson R. de Jesus (22102167)
Source: Figshare (article, 2025-08-21)
Field: Chemistry — Inorganic Chemistry

A Niobium CoordinationPolymer as an Efficient Sorbentfor Caffeine Detection in Surface Water Herein, it is reported the synthesis of a niobium-based metal–organic framework (MOF), [Nb(Bez(COO)<sub>2</sub>)]<sub><i>n</i></sub>, for the extraction of caffeine from surface waters. The material was synthesized and characterized by Fourier-transform infrared spectroscopy (FTIR), Raman spectroscopy, scanning electron microscopy (SEM), X-ray diffraction (XRD), and Brunauer–Emmett–Teller (BET) analysis, which confirmed the coordination between the ligand (1,4-benzenodicarboxylic, (Bez(COO)<sub>2</sub>)) and niobium (Nb) with a morphology composed of hexagonal rods, high crystallinity, and a surface area of 94.7 m<sup>2</sup> g<sup>–1</sup>. The extraction process was optimized using a response surface methodology, evaluating three factors: (i) mass of the MOF (100–500 mg), (ii) solution pH (5.0–9.0), and (iii) temperature (25–45 °C). The optimal conditions for caffeine extraction were determined as 10 mg of material, pH 9.0, and temperature of 25 °C. Adsorption studies showed that the Freundlich isotherm model provided the best fit (<i>R</i><sup>2</sup> = 0.9498), suggesting adsorption on a heterogeneous surface. Kinetic studies showed that the intraparticle diffusion model better described the adsorption process (<i>R</i><sup>2</sup> = 0.9554), highlighting physisorption by intraparticle diffusion as the predominant mechanism. Thermodynamic parameters revealed spontaneous and exothermic adsorption, with Δ<i>G</i> values between −5.052 and −4.668 kJ mol<sup>–1</sup>. The developed analytical method showed a linear range from 1.0 to 20 μg mL<sup>–1</sup>, with good linearity (<i>R</i><sup>2</sup> = 0.9978), a limit of detection and quantification of 0.54 μg mL<sup>–1</sup>, and 1.78 μg mL<sup>–1</sup>, respectively. Accuracy was confirmed by recovery of 95.6 ± 1.1% at 4.5 μg mL<sup>–1</sup>. Moreover, the [Nb(Bez(COO)<sub>2</sub>)]<sub><i>n</i></sub> material demonstrated high reusability, maintaining its extraction efficiency after five consecutive adsorption–desorption cycles. These results confirm the robustness, efficiency, and sustainability of [Nb(Bez(COO)<sub>2</sub>)]<sub><i>n</i></sub> for environmental monitoring and remediation applications. Physisorption ; Adsorption ; Extraction (chemistry) ; Freundlich equation ; Diffusion ; Niobium ; Detection limit ; Analytical Chemistry (journal) Metal-Organic Frameworks: Synthesis and Applications ; Adsorption and biosorption for pollutant removal ; Covalent Organic Framework Applications
Authors: Joonam Kim | Masakazu Ike | Kenichi Tokuda
Source: Nanomaterials (article, 2025-11-27)
Field: Materials Science — Materials Chemistry

Controlling the Orientation of MoS2 Films on Mo Metal Thin Film Through Sulfur Flux Regulation: A Novel Reaction-Diffusion Model This study presents a novel strategy for controlling the orientation of MoS<sub>2</sub> films on thick metallic substrates through precise regulation of the sulfur flux alone. In contrast to previous approaches that rely on substrate modifications or complex parameter tuning, orientation control is achieved here solely by adjusting the sulfur concentration during the sulfurization of 400 nm RF-sputtered Mo films. The metallic Mo substrate also allows potential film transfer via selective etching-analogous to the graphene/Cu system-providing a viable route for device integration on arbitrary substrates. Analyses (XRD, Raman, and TEM) reveal that low sulfur flux (30-50 sccm) favors horizontal growth, whereas high flux (>300 sccm) induces vertical orientation. To rationalize this behavior, a reaction-diffusion model based on the Thiele modulus was developed, quantitatively linking sulfur flux to film orientation and identifying critical thresholds (~50 and ~300 sccm) governing the horizontal-to-vertical transition. This unified approach enables the realization of distinct MoS<sub>2</sub> orientations using identical materials and processes, analogous to the orientation control in graphene growth on copper. The ability to grow orientation-controlled MoS<sub>2</sub> on non-noble metal substrates opens new opportunities for integrating electronic (horizontal) and catalytic (vertical) functionalities, thereby advancing scalable manufacturing of TMDC-based technologies. Sulfur ; Substrate (aquarium) ; Flux (metallurgy) ; Orientation (vector space) ; Metal ; Thin film ; Realization (probability) 2D Materials and Applications ; Copper Interconnects and Reliability ; Catalysis and Hydrodesulfurization Studies
Authors: Jingmei Lu | Chun-Han Wu | Zhaoyu Ma | Mamdouh Omran | Ju Tang | Fan Zhang | Guo Chen
Source: Chemical Engineering and Processing - Process Intensification (article, 2025-09-01)
Field: Chemistry — Organic Chemistry

Drying kinetics and energy efficiency of low-grade zinc oxide powders under microwave heating • Kinetic and efficiency of zinc oxide powders under microwave drying were studied. • Modified Page model can accurately describe the microwave drying process. • Microwave heating substantially reduces the dehydration energy barrier. • Theoretical foundation and actionable guidelines for drying were proposed. Low-grade zinc oxide powder produced by calcination in an industrial rotary kiln mainly contains zinc oxide, zinc hypoxide, and recoverable trace metals. Excessive moisture can cause powder to agglomerate, undergo property changes, or even lead to explosions during recovery. Therefore, microwave drying is required to maintain its physical and chemical properties and eliminate potential safety hazards in subsequent recycling processes. Existing studies mostly focus on single-component materials, without considering the impact of trace metals on moisture distribution and drying efficiency This study focuses on the characteristic of containing trace valuable metals and investigates the effects of sample weight, water content, and microwave power. Under the optimal experimental conditions, the Page, Wang and Singh, Quadratic, and Simplified Fick's diffusion models are used to fit the nonlinear curves of moisture ratio changes. FT-IR spectra show that the peak value of the -OH absorption peak of the dried sample is significantly reduced. Calculations using Fick's second law indicate that the diffusion coefficients exhibit regular changes. The microwave drying activation energy of the powder is measured to be -4.4209 W/g. This study provides targeted references for dehydration treatment of metallurgical waste containing trace valuable metals and promotes application of microwave drying in complex systems. Schematic diagram of the microwave drying mechanism for the low-grade zinc oxide powder. Zinc ; Kinetics ; Microwave ; Microwave heating ; Materials science ; Oxide ; Activation energy ; Metallurgy ; Chemical engineering ; Chemistry ; Physical chemistry ; Physics ; Engineering Microwave-Assisted Synthesis and Applications ; Food Drying and Modeling ; Pigment Synthesis and Properties
Authors: Sharifah Norain Mohd Sharif | Norhayati Hashim | Illyas Md Isa | Maizatul Najwa Jajuli | Norlaili Abu Bakar | Wan Rusmawati Wan Mahamod | Mohamad Syahrizal Ahmad | Mohamad Idris Saidin | Tumirah Khadiran | Suyanta Suyanta
Source: Journal of Porous Materials (article, 2025-11-19)
Field: Materials Science — Materials Chemistry

Surfactant-Assisted intercalation of quintozene into layered zinc hydroxide nitrate for controlled release applications In this research, a fungicide, namely quintozene (QZ) fungicide, was successfully intercalated into zinc hydroxide nitrates (ZHN) with sodium dodecyl sulphate (SDS) surfactant by using a co-precipitate and direct reaction method to form new nanomaterials, ZHN-SDS-QZ. PXRD analysis indicated the successful intercalation with the basal spacing of 33.6 Å. The presence of QZ fungicides in the interlayer space of zinc layered hydroxide is also supported by FTIR and elemental analysis. The thermal analysis confirmed that ZHN-SDS-QZ nanomaterials had good thermal stability compared to the pure QZ. The intercalation of QZ leads to important changes in the morphology, porosity and surface area. Nitrogen adsorption-desorption isotherms reveal that the adsorption isotherm types according to BET analysis for ZHN–SDS are Type IV, while for ZHN-SDS-QZ are Type III. The release into aqueous solution of Na3PO4 and Na2CO3 was governed by pseudo-second order, while for the aqueous solution of NaCl was the Fickian diffusion model. Overall, this nanomaterial is a promising material to develop pest control solutions in the agriculture sector that are effective, yet promote minimal risk to human health and the environment, ensuring food security, safety and ecological sustainability. This study demonstrates that ZHN-SDS-QZ exhibits enhanced stability and controlled release behaviour, making it a promising nanocarrier for sustainable agrochemical delivery. Intercalation (chemistry) ; Aqueous solution ; Thermal stability ; Zinc ; Adsorption ; Fourier transform infrared spectroscopy ; Hydrotalcite ; Zinc nitrate ; Zinc hydroxide ; Thermal decomposition Layered Double Hydroxides Synthesis and Applications ; Polymer-Based Agricultural Enhancements ; Mesoporous Materials and Catalysis