jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yan Lin | Jonas A. Finkler | Tao Du | Jilin Hu | Morten M. Smedskjaer
Source: arXiv (Cornell University) (preprint, 2026-03-31)
Field: Materials Science — Materials Chemistry

AMShortcut: An Inference- and Training-Efficient Inverse Design Model for Amorphous Materials Amorphous materials are solids that lack long-range atomic order but possess complex short- and medium-range order. Unlike crystalline materials that can be described by unit cells containing few up to hundreds of atoms, amorphous materials require larger simulation cells with at least hundreds or often thousands of atoms. Inverse design of amorphous materials with probabilistic generative models aims to generate the atomic positions and elements of amorphous materials given a set of desired properties. It has emerged as a promising approach for facilitating the application of amorphous materials in domains such as energy storage and thermal management. In this paper, we introduce AMShortcut, an inference- and training-efficient probabilistic generative model for amorphous materials. AMShortcut enables accurate inference of diverse short- and medium-range structures in amorphous materials with only a few sampling steps, mitigating the need for an excessive number of sampling steps that hinders inference efficiency. AMShortcut can be trained once with all relevant properties and perform inference conditioned on arbitrary combinations of desired properties, mitigating the need for training one model for each combination. Experiments on three amorphous materials datasets with diverse structures and properties demonstrate that AMShortcut achieves its design goals. Amorphous solid ; Inference ; Probabilistic logic ; Set (abstract data type) ; Inverse ; Inverse problem Machine Learning in Materials Science ; Generative Adversarial Networks and Image Synthesis ; Metallic Glasses and Amorphous Alloys
Authors: Riduwan Prasetya | Femiana Gapsari | Andoko Andoko | Khairul Anam | Rudianto Raharjo | Sanjay Mavinkere Rangappa | Suchart Siengchin
Source: Green Technologies and Sustainability (article, 2026-01-12)
Field: Materials Science — Polymers and Plastics

Moisture-induced swelling in natural fiber-reinforced composites: A critical review of chemical treatments, hybrid strategies, and environmental durability This review aims to critically investigate the swelling behavior of natural fiber-reinforced composites (NFRCs), with a focus on identifying key mechanisms of moisture absorption, the influence of fiber–matrix interactions, and the effectiveness of mitigation strategies such as chemical treatments and hybridization. A systematic synthesis of over 120 peer-reviewed articles published between 2015 and 2025 was conducted. These studies were analyzed to extract data on moisture uptake behavior, microstructural evolution, treatment effects, matrix properties, and performance under various environmental conditions. The review identifies cellulose crystallinity, hemicellulose content, and fiber–matrix interfacial bonding as dominant factors influencing swelling. While Fickian diffusion models describe early-stage absorption, long-term exposure often results in non-Fickian behavior due to matrix plasticization and interface degradation. Chemical treatments such as alkali, silane, and acetylation reduce water absorption but may compromise fiber strength if overapplied. Hybridization with hydrophobic fibers and the use of non-polar matrices improve resistance. Moisture-induced performance degradation is strongly influenced by environmental variables such as temperature, humidity cycling, and salt fog. This review consolidates current knowledge on swelling mechanisms in NFRCs and clarifies the trade-offs between moisture resistance and mechanical performance. It also proposes design guidelines and testing protocols for enhancing long-term durability. The findings are applicable to industries seeking sustainable yet durable composite materials, particularly in automotive, construction, marine, and packaging sectors where environmental exposure is a critical concern. Swelling ; Absorption of water ; Natural fiber ; Durability ; Moisture ; Cellulose ; Hemicellulose ; Composite number Natural Fiber Reinforced Composites ; Advanced Cellulose Research Studies ; Hygrothermal properties of building materials
Authors: Takanori Ishii | Kaoru Hisama | Kohei Shinohara
Source: arXiv (Cornell University) (preprint, 2026-01-13)
Field: Materials Science — Materials Chemistry

Symmetry-aware Conditional Generation of Crystal Structures Using Diffusion Models The application of generative models in crystal structure prediction (CSP) has gained significant attention. Conditional generation--particularly the generation of crystal structures with specified stability or other physical properties has been actively researched for material discovery purposes. Meanwhile, the generative models capable of symmetry-aware generation are also under active development, because space group symmetry has a strong relationship with the physical properties of materials. In this study, we demonstrate that the symmetry control in the previous conditional crystal generation model may not be sufficiently effective when space group constraints are applied as a condition. To address this problem, we propose the WyckoffDiff-Adaptor, which embeds conditional generation within a WyckoffDiff architecture that effectively diffuses Wyckoff positions to achieve precise symmetry control. We successfully generated formation energy phase diagrams while specifying stable structures of particular combination of elements, such as Li--O and Ti--O systems, while simultaneously preserving the symmetry of the input conditions. The proposed method with symmetry-aware conditional generation demonstrates promising results as an effective approach to achieving the discovery of novel materials with targeted physical properties. Symmetry (geometry) ; Crystal structure prediction ; Group (periodic table) ; Stability (learning theory) ; Symmetry group ; Crystal structure ; Generative grammar ; Crystal (programming language) Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Block Copolymer Self-Assembly
Authors: Yanyan Yang | Lili Wang | Xiaoya Zhai | Kai Chen | Wenming Wu | Yunkai Zhao | FaLai Chen | Ligang Liu | Xiao-Ming Fu
Source: Smart Materials in Manufacturing (article, 2026-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Guided diffusion for fast inverse design of voxel-based mechanical metamaterials Mechanical metamaterials are synthetic materials that can possess extraordinary physical characteristics, such as abnormal elasticity, stiffness, and stability, by carefully designing their structure. The representation of metamaterials through high-resolution voxels has the potential to reveal delicate local structures with unique mechanical properties. However, this approach results in a substantial computational burden. To this end, this paper proposes a fast inverse-design framework driven by a self-conditioned diffusion model. An initial set of microstructures is first generated using a physics-based optimization algorithm and used to train the diffusion model. The trained model is then employed to synthesize new candidate microstructures, from which high-quality samples are selected and incorporated into the training set for subsequent retraining. By iterating this generate–filter–retrain cycle, we progressively construct a large-scale dataset and obtain a high-performance generative model. Our model is capable of generating a microstructure to approach the specified homogenized tensor matrix in just 0.42 s on an NVIDIA GeForce RTX 3090 GPU. Compared with the state-of-the-art gradient-based topology optimization method, we achieve an average acceleration of 100 times. Furthermore, we demonstrate that the proposed model enables efficient exploration of extreme metamaterial designs, supports multiscale design workflows, and, through a dedicated guidance mechanism, generates families of metamaterials with continuous variations in both geometry and physical properties. This flexible and adaptive generative tool is of great value in structural engineering or other mechanical systems and can stimulate more subsequent research. • This paper presents a fast deep generative inverse-design method for voxel-based mechanical metamaterials. • We generate initial 128 3 microstructures via optimization and expand the dataset using active learning to achieve broad coverage of physical properties. • This rapid inverse design tool facilitates the exploration of extreme metamaterials, metamaterials sequence, and the generation of diverse microstructures for multiscale design. Metamaterial ; Topology optimization ; Generative model ; Representation (politics) ; Set (abstract data type) ; Inverse ; Voxel ; Tensor (intrinsic definition) ; Inverse problem Metamaterials and Metasurfaces Applications ; Acoustic Wave Phenomena Research ; Topology Optimization in Engineering
Authors: Osman Goni Ridwan | Gilles Frapper | Hongfei Xue | Qiang Zhu
Source: arXiv (Cornell University) (preprint, 2026-01-08)
Field: Materials Science — Materials Chemistry

Crystal Generation using the Fully Differentiable Pipeline and Latent Space Optimization We present a materials generation framework that couples a symmetry-conditioned variational autoencoder (CVAE) with a differentiable SO(3) power spectrum objective to steer candidates toward a specified local environment under the crystallographic constraints. In particular, we implement a fully differentiable pipeline to enable batch-wise optimization on both direct and latent crystallographic representations. Using the GPU acceleration, this implementation achieves about fivefold speed compared to our previous CPU workflow, while yielding comparable outcomes. In addition, we introduce the optimization strategy that alternatively performs optimization on the direct and latent crystal representations. This dual-level relaxation approach can effectively escape local minima defined by different objective gradients, thus increasing the success rate of generating complex structures satisfying the target local environments. This framework can be extended to systems consisting of multi-components and multi-environments, providing a scalable route to generate material structures with the target local environment. Maxima and minima ; Differentiable function ; Pipeline (software) ; Scalability ; Autoencoder ; Relaxation (psychology) ; Optimization problem Machine Learning in Materials Science ; Calcium Carbonate Crystallization and Inhibition ; Topology Optimization in Engineering
Authors: Chuqin Geng | Li Zhang | Mark Zhang | Haolin Ye | Ziyu Zhao | Xujie Si
Source: Open MIND (preprint, 2026-02-18)
Field: Materials Science — Materials Chemistry

Neural Proposals, Symbolic Guarantees: Neuro-Symbolic Graph Generation with Hard Constraints We challenge black-box purely deep neural approaches for molecules and graph generation, which are limited in controllability and lack formal guarantees. We introduce Neuro-Symbolic Graph Generative Modeling (NSGGM), a neurosymbolic framework that reapproaches molecule generation as a scaffold and interaction learning task with symbolic assembly. An autoregressive neural model proposes scaffolds and refines interaction signals, and a CPU-efficient SMT solver constructs full graphs while enforcing chemical validity, structural rules, and user-specific constraints, yielding molecules that are correct by construction and interpretable control that pure neural methods cannot provide. NSGGM delivers strong performance on both unconstrained generation and constrained generation tasks, demonstrating that neuro-symbolic modeling can match state-of-the-art generative performance while offering explicit controllability and guarantees. To evaluate more nuanced controllability, we also introduce a Logical-Constraint Molecular Benchmark, designed to test strict hard-rule satisfaction in workflows that require explicit, interpretable specifications together with verifiable compliance. Controllability ; Satisfiability modulo theories ; Workflow ; Graph ; Solver ; Generative grammar ; Task (project management) ; Verifiable secret sharing Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Chuqin Geng | Li Zhang | Mark Zhang | Haolin Ye | Ziyu Zhao | Xujie Si
Source: arXiv (Cornell University) (article, 2026-02-18)
Field: Materials Science — Materials Chemistry

Neural Proposals, Symbolic Guarantees: Neuro-Symbolic Graph Generation with Hard Constraints We challenge black-box purely deep neural approaches for molecules and graph generation, which are limited in controllability and lack formal guarantees. We introduce Neuro-Symbolic Graph Generative Modeling (NSGGM), a neurosymbolic framework that reapproaches molecule generation as a scaffold and interaction learning task with symbolic assembly. An autoregressive neural model proposes scaffolds and refines interaction signals, and a CPU-efficient SMT solver constructs full graphs while enforcing chemical validity, structural rules, and user-specific constraints, yielding molecules that are correct by construction and interpretable control that pure neural methods cannot provide. NSGGM delivers strong performance on both unconstrained generation and constrained generation tasks, demonstrating that neuro-symbolic modeling can match state-of-the-art generative performance while offering explicit controllability and guarantees. To evaluate more nuanced controllability, we also introduce a Logical-Constraint Molecular Benchmark, designed to test strict hard-rule satisfaction in workflows that require explicit, interpretable specifications together with verifiable compliance. Controllability ; Satisfiability modulo theories ; Workflow ; Graph ; Solver ; Generative grammar ; Task (project management) ; Verifiable secret sharing Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Divya Mathew | Sudheep NM | Benny Thomas
Source: BioNanoScience (article, 2026-03-17)
Field: Materials Science — Biomaterials

Electrospun PVA Nanofibrous Mats Encapsulating Clove Oil for Sustained Antioxidant and Antimicrobial Delivery: Design, Characterization, and Release Kinetics Evaluation Electrospinning ; Thermogravimetric analysis ; Biocompatibility ; Polyvinyl alcohol ; Antioxidant ; Antimicrobial ; Kinetics Electrospun Nanofibers in Biomedical Applications ; Nanocomposite Films for Food Packaging ; Surface Modification and Superhydrophobicity
Authors: Dilipkumar N. Asthagiri | Dmitry V. Matyushov
Source: ArXiv.org (article, 2026-01-15)
Field: Materials Science — Materials Chemistry

Rotational Memory Function of SPC/E water Memory effects are essential for dynamics of condensed materials and are responsible for non-exponential relaxation of correlation functions of dynamic variables through the memory function. Memory functions of dipole rotations for polar liquids have never been calculated. We present here calculations of memory functions for single-dipole rotations and for the overall dipole moment of the sample for SPC/E water. The memory functions for single-particle and collective dipole dynamics turn out to be nearly identical. This result validates theories of dielectric spectroscopy in terms of single-particle time correlation functions and the connection between the collective and single-particle relaxation times through the Kirkwood factor. The dielectric function in this formalism contains no new dynamic information that does not exist in the single-dipole correlation function. A short memory time, $\lesssim 1$ fs, justifies the use of rotational diffusion model to describe dynamics of a single molecular dipole moment in bulk water. Dipole ; Dielectric ; Formalism (music) ; Moment (physics) ; Relaxation (psychology) ; Dynamics (music) ; Polar ; Electric dipole moment ; Molecular dynamics Material Dynamics and Properties ; Molecular spectroscopy and chirality ; Spectroscopy and Quantum Chemical Studies
Authors: R. Suresh | A. Sureshkumar | V. K. Shanmuganathan | P. Michael Joseph Stalin
Source: Welding in the World (article, 2026-03-05)
Field: Materials Science — Materials Chemistry

Deep generative learning for microstructure evolution prediction in multimaterial welded interfaces Welding ; Electron backscatter diffraction ; Microstructure ; Weld pool ; Solid mechanics ; Process (computing) ; Intersection (aeronautics) ; Phase (matter) Machine Learning in Materials Science ; Welding Techniques and Residual Stresses ; Additive Manufacturing Materials and Processes
Authors: Haokai Hong | Wanyu Lin | Ming Yang | Kay Chen Tan
Source: Proceedings of the AAAI Conference on Artificial Intelligence (article, 2026-03-14)
Field: Materials Science — Materials Chemistry

Distributional Priors Guided Diffusion for Generating 3D Molecules in Low Data Regimes Can we train a 3D molecule generator using data from dense regions to generate samples in sparse regions? This challenge can be framed as an out-of-distribution (OOD) generation problem. While prior research on OOD generation predominantly targets property shifts, structural shifts, such as differences in molecular scaffolds or functional groups, represent an equally critical source of distributional shifts. This work introduces the Geometric OOD Diffusion Model (GODD), a novel diffusion-based framework that enables training on data-abundant molecular distributions while generalizing to data-scarce distributions under distributional structural shifts. Central to our approach is a designated equivariant asymmetric autoencoder to capture distributional structural priors. The asymmetric design allows the model to generalize to unseen structural variations by capturing distributional priors representing distinct distributions. The encoded structural-grained priors guide generation toward sparse regions without requiring explicit training on such data. Evaluated across standard benchmarks encompassing OOD structural shifts (e.g., scaffolds, rings), GODD achieves an improvement of 12.6% in success rate, defined based on molecular validity, uniqueness, and novelty. Furthermore, the framework demonstrates promising performance and generalization on canonical fragment-based drug design tasks, highlighting its utility in learning-based molecular discovery. Prior probability ; Generalization ; Generator (circuit theory) ; Property (philosophy) ; Observability ; Autoencoder ; Probability distribution Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Domain Adaptation and Few-Shot Learning
Authors: Dhruvesh Patel | Benjamin Rozonoyer | Gaurav Pandey | Tahira Naseem | Ramon Fernandez Astudillo | Andrew McCallum
Source: Open MIND (preprint, 2026-02-21)
Field: Materials Science — Materials Chemistry

Insertion Based Sequence Generation with Learnable Order Dynamics In many domains generating variable length sequences through insertions provides greater flexibility over autoregressive models. However, the action space of insertion models is much larger than that of autoregressive models (ARMs) making the learning challenging. To address this, we incorporate trainable order dynamics into the target rates for discrete flow matching, and show that with suitable choices of parameterizations, joint training of the target order dynamics and the generator is tractable without the need for numerical simulation. As the generative insertion model, we use a variable length masked diffusion model, which generates by inserting and filling mask tokens. On graph traversal tasks for which a locally optimal insertion order is known, we explore the choices of parameterization empirically and demonstrate the trade-offs between flexibility, training stability and generation quality. On de novo small molecule generation, we find that the learned order dynamics leads to an increase in the number of valid molecules generated and improved quality, when compared to uniform order dynamics. Autoregressive model ; Generator (circuit theory) ; Tree traversal ; Variable (mathematics) ; Stability (learning theory) ; Sequence (biology) ; Flexibility (engineering) ; Generative model ; Dynamics (music) Machine Learning in Materials Science ; Topic Modeling ; Generative Adversarial Networks and Image Synthesis
Authors: Dhruvesh Patel | Benjamin Rozonoyer | Gaurav Pandey | Tahira Naseem | Ramon Fernandez Astudillo | Andrew McCallum
Source: ArXiv.org (article, 2026-02-21)
Field: Materials Science — Materials Chemistry

Insertion Based Sequence Generation with Learnable Order Dynamics In many domains generating variable length sequences through insertions provides greater flexibility over autoregressive models. However, the action space of insertion models is much larger than that of autoregressive models (ARMs) making the learning challenging. To address this, we incorporate trainable order dynamics into the target rates for discrete flow matching, and show that with suitable choices of parameterizations, joint training of the target order dynamics and the generator is tractable without the need for numerical simulation. As the generative insertion model, we use a variable length masked diffusion model, which generates by inserting and filling mask tokens. On graph traversal tasks for which a locally optimal insertion order is known, we explore the choices of parameterization empirically and demonstrate the trade-offs between flexibility, training stability and generation quality. On de novo small molecule generation, we find that the learned order dynamics leads to an increase in the number of valid molecules generated and improved quality, when compared to uniform order dynamics. Autoregressive model ; Generator (circuit theory) ; Tree traversal ; Variable (mathematics) ; Stability (learning theory) ; Sequence (biology) ; Flexibility (engineering) ; Generative model ; Dynamics (music) Machine Learning in Materials Science ; Topic Modeling ; Generative Adversarial Networks and Image Synthesis
Authors: Mingzhang Yang | Yuxi Wang | Jun Deng | Tianping Ying | Qinghua Zhang | Nianjie Liang | Xiaobing Liu | Bai Song | Jian-gang Guo | Xiaolong Chen
Source: arXiv (Cornell University) (preprint, 2026-03-30)
Field: Materials Science — Materials Chemistry

Intrinsically ultralow thermal conductivity in all-inorganic superatomic bulk crystals Superatomic compounds, composed of atomic clusters interwoven by weak chemical bonds exhibit large anharmonicity vibrations, are excellent candidates for ultralow thermal conductivity (\k{appa}) materials. However, growing bulk superatomic single crystals is challenging due to complex chemical composition and chemical bonds, and studies on their intrinsic thermal property are scarce. Here, we grew high-quality superatomic single crystals of Re6Se8Te7 and Re6Te15, both of which are narrow band gap semiconductors that change into metals under external physical pressure. At room-temperature, the \k{appa} are 0.32 W m-1 K-1 and 0.53 W m-1 K-1 in Re6Se8Te7 and Re6Te15, respectively, ranking among the lowest value reported in all-inorganic bulk crystals. It is mainly attributed to the large Grüneisen parameter (1.93) and low average sound speed (&lt; 1482 m/s), which are due to soft Te7 nets weakly embedded among the rigid Re6Se8 (Re6Te8) quasi-cubic clusters. The appearance of boson peak, i. e., hump of C(T)/T3, verifies the existence of disordered phonon transports. Besides, the temperature dependence of \k{appa} can be described by classic Debye-Callaway model. Notably, above 350 K, the \k{appa} values of Re6Se8Te7 and Re6Te15 are remarkably close to the upper limit derived from glassy-like diffusion model. This finding sets the superatomic compounds as a promising family for searching ultralow-\k{appa} and energy management materials. Thermal conductivity ; Anharmonicity ; Phonon ; Thermal ; Semiconductor ; Diffusion ; Chemical bond ; Chemical composition Advanced Thermoelectric Materials and Devices ; Thermal Expansion and Ionic Conductivity ; Inorganic Chemistry and Materials