jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Riccardo De Santi | Malte Franke | Ya-Ping Hsieh | Andreas Krause
Source: arXiv (Cornell University) (article, 2026-02-08)
Field: Materials Science — Materials Chemistry

A Unified Density Operator View of Flow Control and Merging Recent progress in large-scale flow and diffusion models raised two fundamental algorithmic challenges: (i) control-based reward adaptation of pre-trained flows, and (ii) integration of multiple models, i.e., flow merging. While current approaches address them separately, we introduce a unifying probability-space framework that subsumes both as limit cases, and enables reward-guided flow merging, allowing principled, task-aware combination of multiple pre-trained flows (e.g., merging priors while maximizing drug-discovery utilities). Our formulation renders possible to express a rich family of operators over generative models densities, including intersection (e.g., to enforce safety), union (e.g., to compose diverse models), interpolation (e.g., for discovery), their reward-guided counterparts, as well as complex logical expressions via generative circuits. Next, we introduce Reward-Guided Flow Merging (RFM), a mirror-descent scheme that reduces reward-guided flow merging to a sequence of standard fine-tuning problems. Then, we provide first-of-their-kind theoretical guarantees for reward-guided and pure flow merging via RFM. Ultimately, we showcase the capabilities of the proposed method on illustrative settings providing visually interpretable insights, and apply our method to high-dimensional de-novo molecular design and low-energy conformer generation. Intersection (aeronautics) ; Flow (mathematics) ; Operator (biology) ; Interpolation (computer graphics) ; Limit (mathematics) ; Sequence (biology) ; Scheme (mathematics) ; Generative grammar Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Timo Reents | Arianna Cantarella | M. Bercx | Pietro Bonfà | Giovanni Pizzi
Source: arXiv (Cornell University) (preprint, 2026-01-05)
Field: Materials Science — Materials Chemistry

Score-based diffusion models for accurate crystal-structure inpainting and reconstruction of hydrogen positions Generative AI models, such as score-based diffusion models, have recently advanced the field of computational materials science by enabling the generation of new materials with desired properties. In addition, these models could also be leveraged to reconstruct crystal structures for which partial information is available. One relevant example is the reliable determination of atomic positions occupied by hydrogen atoms in hydrogen-containing crystalline materials. While crucial to the analysis and prediction of many materials properties, the identification of hydrogen positions can however be difficult and expensive, as it is challenging in X-ray scattering experiments and often requires dedicated neutron scattering measurements. As a consequence, inorganic crystallographic databases frequently report lattice structures where hydrogen atoms have been either omitted or inserted with heuristics or by chemical intuition. Here, we combine diffusion models from the field of materials science with techniques originally developed in computer vision for image inpainting. We present how this knowledge transfer across domains enables a much faster and more accurate completion of host structures, compared to unconditioned diffusion models or previous approaches solely based on DFT. Overall, our approach exceeds a success rate of 97% in terms of finding a structural match or predicting a more stable configuration than the initial reference, when starting both from structures that were already relaxed with DFT, or directly from the experimentally determined host structures. Heuristics ; Diffusion ; Neutron scattering ; Scattering ; Field (mathematics) ; Hydrogen Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Block Copolymer Self-Assembly
Authors: Wenjin Wu | Aleš Leonardis | Linjiang Chen | Jianbo Jiao
Source: arXiv (Cornell University) (preprint, 2026-03-06)
Field: Chemistry — Analytical Chemistry

Latent Diffusion-Based 3D Molecular Recovery from Vibrational Spectra Infrared (IR) spectroscopy, a type of vibrational spectroscopy, is widely used for molecular structure determination and provides critical structural information for chemists. However, existing approaches for recovering molecular structures from IR spectra typically rely on one-dimensional SMILES strings or two-dimensional molecular graphs, which fail to capture the intricate relationship between spectral features and three-dimensional molecular geometry. Recent advances in diffusion models have greatly enhanced the ability to generate molecular structures in 3D space. Yet, no existing model has explored the distribution of 3D molecular geometries corresponding to a single IR spectrum. In this work, we introduce IR-GeoDiff, a latent diffusion model that recovers 3D molecular geometries from IR spectra by integrating spectral information into both node and edge representations of molecular structures. We evaluate IR-GeoDiff from both spectral and structural perspectives, demonstrating its ability to recover the molecular distribution corresponding to a given IR spectrum. Furthermore, an attention-based analysis reveals that the model is able to focus on characteristic functional group regions in IR spectra, qualitatively consistent with common chemical interpretation practices. Infrared spectroscopy ; Infrared ; Spectral line ; Molecular model ; Molecular dynamics ; Molecule ; Molecular vibration Spectroscopy and Chemometric Analyses ; Spectroscopy and Quantum Chemical Studies ; Molecular spectroscopy and chirality
Authors: Aniketh Iyengar | Jiaqi Han | Pengwei Sun | Mingjian Jiang | Jianwen Xie | Stefano Ermon
Source: arXiv (Cornell University) (preprint, 2026-04-05)
Field: Materials Science — Materials Chemistry

Align Your Structures: Generating Trajectories with Structure Pretraining for Molecular Dynamics Generating molecular dynamics (MD) trajectories using deep generative models has attracted increasing attention, yet remains inherently challenging due to the limited availability of MD data and the complexities involved in modeling high-dimensional MD distributions. To overcome these challenges, we propose a novel framework that leverages structure pretraining for MD trajectory generation. Specifically, we first train a diffusion-based structure generation model on a large-scale conformer dataset, on top of which we introduce an interpolator module trained on MD trajectory data, designed to enforce temporal consistency among generated structures. Our approach effectively harnesses abundant structural data to mitigate the scarcity of MD trajectory data and effectively decomposes the intricate MD modeling task into two manageable subproblems: structural generation and temporal alignment. We comprehensively evaluate our method on the QM9 and DRUGS small-molecule datasets across unconditional generation, forward simulation, and interpolation tasks, and further extend our framework and analysis to tetrapeptide and protein monomer systems. Experimental results confirm that our approach excels in generating chemically realistic MD trajectories, as evidenced by remarkable improvements of accuracy in geometric, dynamical, and energetic measurements. Trajectory ; Molecular dynamics ; Task (project management) ; Interpolation (computer graphics) ; Consistency (knowledge bases) ; Data modeling ; Generative model Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Aniketh Iyengar | Jiaqi Han | Pengwei Sun | Mingjian Jiang | Jianwen Xie | Stefano Ermon
Source: arXiv (Cornell University) (article, 2026-04-05)
Field: Materials Science — Materials Chemistry

Align Your Structures: Generating Trajectories with Structure Pretraining for Molecular Dynamics Generating molecular dynamics (MD) trajectories using deep generative models has attracted increasing attention, yet remains inherently challenging due to the limited availability of MD data and the complexities involved in modeling high-dimensional MD distributions. To overcome these challenges, we propose a novel framework that leverages structure pretraining for MD trajectory generation. Specifically, we first train a diffusion-based structure generation model on a large-scale conformer dataset, on top of which we introduce an interpolator module trained on MD trajectory data, designed to enforce temporal consistency among generated structures. Our approach effectively harnesses abundant structural data to mitigate the scarcity of MD trajectory data and effectively decomposes the intricate MD modeling task into two manageable subproblems: structural generation and temporal alignment. We comprehensively evaluate our method on the QM9 and DRUGS small-molecule datasets across unconditional generation, forward simulation, and interpolation tasks, and further extend our framework and analysis to tetrapeptide and protein monomer systems. Experimental results confirm that our approach excels in generating chemically realistic MD trajectories, as evidenced by remarkable improvements of accuracy in geometric, dynamical, and energetic measurements. Trajectory ; Molecular dynamics ; Task (project management) ; Interpolation (computer graphics) ; Consistency (knowledge bases) ; Data modeling ; Generative model Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Hassan Imani | Niladri Das | Iain; id_orcid 0000-0002-6755-0299 Styles
Source: Research Portal (Queen's University Belfast) (article, 2026-01-01)
Field: Chemistry — Spectroscopy

A unified transformer architecture for 3D MSI-H&amp;E fusion Mass spectrometry imaging (MSI) enables unlabeled, untargeted mapping of biomolecular distributions in tissue but is limited by low spatial resolution and high data complexity. We address these challenges with a generative model for MSI peak learning that produces compact, chemically meaningful embeddings, and a transformer-based framework for multimodal fusion of low-resolution MSI with high-resolution H&amp;E microscopy across serial sections. The model tokenizes MSI and H&amp;E patches and employs deformable cross-attention to transfer histological structure while preserving MSI chemistry, yielding slice-consistent high-resolution 3D reconstructions. Experiments on 3D DESI Orbitrap MSI/H&amp;E data from human colorectal adenocarcinoma demonstrate anatomically accurate and chemicallyfaithful reconstruction without manual intervention. <br/>Code is available at https://github.com/H-deep/TransUniFERE-3D. Fusion ; High resolution ; Orbitrap ; Sensor fusion ; Transformer ; Generative grammar ; Generative model ; Pattern recognition (psychology) Mass Spectrometry Techniques and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Proteomics Techniques and Applications
Authors: Junyi An | Chao Qu | Yunfei Shi | Zhijian Zhou | Fenglei Cao | Yuan Qi
Source: arXiv (Cornell University) (preprint, 2026-03-10)
Field: Materials Science — Materials Chemistry

Equivariant Asynchronous Diffusion: An Adaptive Denoising Schedule for Accelerated Molecular Conformation Generation Recent 3D molecular generation methods primarily use asynchronous auto-regressive or synchronous diffusion models. While auto-regressive models build molecules sequentially, they're limited by a short horizon and a discrepancy between training and inference. Conversely, synchronous diffusion models denoise all atoms at once, offering a molecule-level horizon but failing to capture the causal relationships inherent in hierarchical molecular structures. We introduce Equivariant Asynchronous Diffusion (EAD) to overcome these limitations. EAD is a novel diffusion model that combines the strengths of both approaches: it uses an asynchronous denoising schedule to better capture molecular hierarchy while maintaining a molecule-level horizon. Since these relationships are often complex, we propose a dynamic scheduling mechanism to adaptively determine the denoising timestep. Experimental results show that EAD achieves state-of-the-art performance in 3D molecular generation. Asynchronous communication ; Schedule ; Scheduling (production processes) ; Noise reduction ; Equivariant map ; Hierarchy Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Model Reduction and Neural Networks
Authors: Naoki Shimosako | Eiji Miyazaki | Yukino Masuda | Keisuke TOJO | Miki Matsuoka | MASAYA MORIYAMA | Taro KINUMOTO
Source: CEAS Space Journal (article, 2026-01-05)
Field: Materials Science — Materials Chemistry

Outgassing properties of bamboo-derived cellulose nanofiber sheets containing hemicellulose and lignin evaluated by ASTM E1559 for space applications Outgassing ; Hemicellulose ; Cellulose ; Lignin ; Nanofiber ; Spacecraft ; Raw material Silicone and Siloxane Chemistry ; Polymer Nanocomposites and Properties ; Epoxy Resin Curing Processes
Authors: Tri Nguyen | Sherif Abdulkader Tawfik | Truyen Tran | Svetha Venkatesh
Source: arXiv (Cornell University) (preprint, 2026-03-25)
Field: Materials Science — Materials Chemistry

ChargeFlow: Flow-Matching Refinement of Charge-Conditioned Electron Densities Accurate charge densities are central to electronic-structure theory, but computing charge-state-dependent densities with density functional theory remains too expensive for large-scale screening and defect workflows. We present ChargeFlow, a flow-matching refinement model that transforms a charge-conditioned superposition of atomic densities into the corresponding DFT electron density on the native periodic real-space grid using a 3D U-Net velocity field. Trained on 9,502 charged Materials Project-derived calculations and evaluated on an external 1,671-structure benchmark spanning perovskites, charged defects, diamond defects, metal-organic frameworks, and organic crystals, ChargeFlow is not uniformly best on every in-distribution class but is strongest on problems dominated by nonlocal charge redistribution and charge-state extrapolation, improving deformation-density error from 3.62% to 3.21% and charge- response cosine similarity from 0.571 to 0.655 relative to a ResNet baseline. The predicted densities remain chemically useful under downstream analysis, yielding successful Bader partitioning on all 1,671 benchmark structures and high-fidelity electrostatic potentials, which positions flow matching as a practical density-refinement strategy for charged materials. Benchmark (surveying) ; Superposition principle ; Density functional theory ; Electron density ; Charge (physics) ; Grid ; Charge density ; Redistribution (election) ; Electron Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; X-ray Diffraction in Crystallography
Authors: Timo Gahlmann | Tobias Wenger | Philippe Tassin
Source: Machine Learning Science and Technology (article, 2026-02-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine-learning-based inverse design of wide-band metasurfaces with interdependent unit cells Abstract Metasurfaces are thin optical materials consisting of tiny meta-atoms that locally change the phase, amplitude, and polarization of light. Such metasurfaces have been shown to exhibit optical responses beyond what is possible with natural materials, e.g. in thin achromatic lenses, holograms, reflectors, and the generation of structured light. A considerable challenge lies in designing the sometimes thousands of meta-atoms that are needed to locally modify the wavefronts of light. Here we use a conditional generative adversarial network to demonstrate for the first time a way to design metasurfaces where the scattering responses of different meta-atoms are interdependent, i.e. some of the individual scattering parameters are unimportant, and it is the relative values between different meta-atoms that matter. We illustrate our approach with a design of a broadband phase mask for a Zernike wavefront sensor, with complex requirements on the phase relationship between the scattering parameters of an inner disk and the surrounding region. Our inverse design method offers increased design freedom, since it does not require specifying absolute scattering parameters, and allows for metasurfaces with large bandwidths. Wavefront ; Achromatic lens ; Scattering ; Broadband ; Inverse scattering problem ; Polarization (electrochemistry) ; Zernike polynomials ; Inverse Metamaterials and Metasurfaces Applications ; Acoustic Wave Phenomena Research ; Plasmonic and Surface Plasmon Research
Authors: Mingzhang Yang | Yuxi Wang | Jun Deng | Tianping Ying | Qinghua Zhang | Nianjie Liang | Xiaobing Liu | Bai Song | Jian-gang Guo | Xiaolong Chen
Source: Applied Physics Reviews (article, 2026-03-01)
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 (κ) 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-bandgap semiconductors that change into metals under external physical pressure. At room temperature, κ is 0.32 ± 0.02 and 0.53 ± 0.02 W m−1 K−1 in Re6Se8Te7 and Re6Te15, respectively, ranking among the lowest values reported in all-inorganic bulk crystals. It is mainly attributed to the large Grüneisen parameter (1.93) and low average sound speed (&amp;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., a hump of C(T)/T3, verifies the existence of disordered phonon transports. In addition, the temperature dependence of κ can be described by the classic Debye–Callaway model. Notably, above 350 K, the κ values of Re6Se8Te7 and Re6Te15 are remarkably close to the upper limit derived from a glassy-like diffusion model. This finding sets the superatomic compounds as a promising family for searching ultralow-κ and energy management materials. Thermal conductivity ; Anharmonicity ; Phonon ; Thermal ; Diffusion ; Chemical bond ; Chemical composition ; Semiconductor Advanced Thermoelectric Materials and Devices ; Thermal Expansion and Ionic Conductivity ; Inorganic Chemistry and Materials
Authors: Philipp Hoellmer | Stefano Martiniani
Source: Open MIND (preprint, 2026-01-31)
Field: Materials Science — Materials Chemistry

Open Materials Generation with Inference-Time Reinforcement Learning Continuous-time generative models for crystalline materials enable inverse materials design by learning to predict stable crystal structures, but incorporating explicit target properties into the generative process remains challenging. Policy-gradient reinforcement learning (RL) provides a principled mechanism for aligning generative models with downstream objectives but typically requires access to the score, which has prevented its application to flow-based models that learn only velocity fields. We introduce Open Materials Generation with Inference-time Reinforcement Learning (OMatG-IRL), a policy-gradient RL framework that operates directly on the learned velocity fields and eliminates the need for the explicit computation of the score. OMatG-IRL leverages stochastic perturbations of the underlying generation dynamics preserving the baseline performance of the pretrained generative model while enabling exploration and policy-gradient estimation at inference time. Using OMatG-IRL, we present the first application of RL to crystal structure prediction (CSP). Our method enables effective reinforcement of an energy-based objective while preserving diversity through composition conditioning, and it achieves performance competitive with score-based RL approaches. Finally, we show that OMatG-IRL can learn time-dependent velocity-annealing schedules, enabling accurate CSP with order-of-magnitude improvements in sampling efficiency and, correspondingly, reduction in generation time. Reinforcement learning ; Generative grammar ; Generative model ; Inference ; Computation ; Generative Design ; Process (computing) ; Reinforcement Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Generative Adversarial Networks and Image Synthesis
Authors: Philipp Hoellmer | Stefano Martiniani
Source: arXiv (Cornell University) (article, 2026-01-31)
Field: Materials Science — Materials Chemistry

Open Materials Generation with Inference-Time Reinforcement Learning Continuous-time generative models for crystalline materials enable inverse materials design by learning to predict stable crystal structures, but incorporating explicit target properties into the generative process remains challenging. Policy-gradient reinforcement learning (RL) provides a principled mechanism for aligning generative models with downstream objectives but typically requires access to the score, which has prevented its application to flow-based models that learn only velocity fields. We introduce Open Materials Generation with Inference-time Reinforcement Learning (OMatG-IRL), a policy-gradient RL framework that operates directly on the learned velocity fields and eliminates the need for the explicit computation of the score. OMatG-IRL leverages stochastic perturbations of the underlying generation dynamics preserving the baseline performance of the pretrained generative model while enabling exploration and policy-gradient estimation at inference time. Using OMatG-IRL, we present the first application of RL to crystal structure prediction (CSP). Our method enables effective reinforcement of an energy-based objective while preserving diversity through composition conditioning, and it achieves performance competitive with score-based RL approaches. Finally, we show that OMatG-IRL can learn time-dependent velocity-annealing schedules, enabling accurate CSP with order-of-magnitude improvements in sampling efficiency and, correspondingly, reduction in generation time. Reinforcement learning ; Generative grammar ; Generative model ; Inference ; Computation ; Generative Design ; Process (computing) ; Reinforcement Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Generative Adversarial Networks and Image Synthesis