jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Nayoung Kim | Honghui Kim | Sihyun Yu | Minkyu Kim | Seongsu Kim | Sungsoo Ahn
Source: ArXiv.org (article, 2026-02-07)
Field: Chemistry — Inorganic Chemistry

AtomMOF: All-Atom Flow Matching for MOF-Adsorbate Structure Prediction Deep generative models have shown promise for modeling metal-organic frameworks (MOFs), but existing approaches (1) rely on coarse-grained representations that assume fixed bond lengths and angles, and (2) neglect the MOF-adsorbate interactions, which are critical for downstream applications. We introduce AtomMOF, a scalable flow-based model built on an all-atom Diffusion Transformer that maps 2D molecular graphs of building blocks and adsorbates directly to equilibrium 3D structures without imposing structural constraints. We further present scaling laws for porous crystal generation, indicating predictable performance gains with increased model capacity, and introduce Feynman-Kac steering guided by machine-learned interatomic potentials to improve geometric validity and sampling stability. On the (MOF-only) BW dataset, AtomMOF increases the match rate by 35.00% and reduces RMSD by 32.64%. On the ODAC25 dataset (MOF-adsorbate), AtomMOF is substantially more sample-efficient than grand canonical Monte Carlo in recovering adsorption configurations and can identify candidates with lower adsorption energies than the reference dataset. Code is available at https://github.com/nayoung10/AtomMOF. Scaling ; Scalability ; Monte Carlo method ; Code (set theory) ; Sampling (signal processing) ; Matching (statistics) ; Importance sampling ; Non-equilibrium thermodynamics ; Void (composites) Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Covalent Organic Framework Applications
Authors: Gyeonghoon Ko | Juho Lee
Source: arXiv (Cornell University) (preprint, 2026-03-24)
Field: Materials Science — Materials Chemistry

Permutation-Symmetrized Diffusion for Unconditional Molecular Generation Permutation invariance is fundamental in molecular point-cloud generation, yet most diffusion models enforce it indirectly via permutation-equivariant networks on an ordered space. We propose to model diffusion directly on the quotient manifold $\tilde{\calX}=\sR^{d\times N}/S_N$, where all atom permutations are identified. We show that the heat kernel on $\tilde{\calX}$ admits an explicit expression as a sum of Euclidean heat kernels over permutations, which clarifies how diffusion on the quotient differs from ordered-particle diffusion. Training requires a permutation-symmetrized score involving an intractable sum over $S_N$; we derive an expectation form over a posterior on permutations and approximate it using MCMC in permutation space. We evaluate on unconditional 3D molecule generation on QM9 under the EQGAT-Diff protocol, using SemlaFlow-style backbone and treating all variables continuously. The results demonstrate that quotient-based permutation symmetrization is practical and yields competitive generation quality with improved efficiency. Permutation (music) ; Symmetrization ; Quotient ; Kernel (algebra) ; Heat kernel ; Diffusion ; Expression (computer science) ; Generalized permutation matrix Machine Learning in Materials Science ; Advanced Fluorescence Microscopy Techniques ; Surface Chemistry and Catalysis
Authors: Nayoung Kim | Honghui Kim | Sihyun Yu | Minkyu Kim | Seongsu Kim | Sungsoo Ahn
Source: Open MIND (preprint, 2026-02-07)
Field: Chemistry — Inorganic Chemistry

AtomMOF: All-Atom Flow Matching for MOF-Adsorbate Structure Prediction Deep generative models have shown promise for modeling metal-organic frameworks (MOFs), but existing approaches (1) rely on coarse-grained representations that assume fixed bond lengths and angles, and (2) neglect the MOF-adsorbate interactions, which are critical for downstream applications. We introduce AtomMOF, a scalable flow-based model built on an all-atom Diffusion Transformer that maps 2D molecular graphs of building blocks and adsorbates directly to equilibrium 3D structures without imposing structural constraints. We further present scaling laws for porous crystal generation, indicating predictable performance gains with increased model capacity, and introduce Feynman-Kac steering guided by machine-learned interatomic potentials to improve geometric validity and sampling stability. On the (MOF-only) BW dataset, AtomMOF increases the match rate by 35.00% and reduces RMSD by 32.64%. On the ODAC25 dataset (MOF-adsorbate), AtomMOF is substantially more sample-efficient than grand canonical Monte Carlo in recovering adsorption configurations and can identify candidates with lower adsorption energies than the reference dataset. Code is available at https://github.com/nayoung10/AtomMOF. Scaling ; Scalability ; Monte Carlo method ; Code (set theory) ; Sampling (signal processing) ; Matching (statistics) ; Importance sampling ; Non-equilibrium thermodynamics ; Void (composites) Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Covalent Organic Framework Applications
Authors: Riccardo De Santi | Kimon Protopapas | Ya-Ping Hsieh | Andreas Krause
Source: arXiv (Cornell University) (article, 2026-02-17)
Field: Materials Science — Materials Chemistry

Verifier-Constrained Flow Expansion for Discovery Beyond the Data Flow and diffusion models are typically pre-trained on limited available data (e.g., molecular samples), covering only a fraction of the valid design space (e.g., the full molecular space). As a consequence, they tend to generate samples from only a narrow portion of the feasible domain. This is a fundamental limitation for scientific discovery applications, where one typically aims to sample valid designs beyond the available data distribution. To this end, we address the challenge of leveraging access to a verifier (e.g., an atomic bonds checker), to adapt a pre-trained flow model so that its induced density expands beyond regions of high data availability, while preserving samples validity. We introduce formal notions of strong and weak verifiers and propose algorithmic frameworks for global and local flow expansion via probability-space optimization. Then, we present Flow Expander (FE), a scalable mirror descent scheme that provably tackles both problems by verifier-constrained entropy maximization over the flow process noised state space. Next, we provide a thorough theoretical analysis of the proposed method, and state convergence guarantees under both idealized and general assumptions. Ultimately, we empirically evaluate our method on both illustrative, yet visually interpretable settings, and on a molecular design task showcasing the ability of FE to expand a pre-trained flow model increasing conformer diversity while preserving validity. Scalability ; Flow (mathematics) ; Entropy (arrow of time) ; Bridging (networking) ; Convergence (economics) ; State (computer science) ; Process (computing) ; Task (project management) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Generative Adversarial Networks and Image Synthesis
Authors: Riccardo De Santi | Kimon Protopapas | Ya-Ping Hsieh | Andreas Krause
Source: Open MIND (preprint, 2026-02-17)
Field: Materials Science — Materials Chemistry

Verifier-Constrained Flow Expansion for Discovery Beyond the Data Flow and diffusion models are typically pre-trained on limited available data (e.g., molecular samples), covering only a fraction of the valid design space (e.g., the full molecular space). As a consequence, they tend to generate samples from only a narrow portion of the feasible domain. This is a fundamental limitation for scientific discovery applications, where one typically aims to sample valid designs beyond the available data distribution. To this end, we address the challenge of leveraging access to a verifier (e.g., an atomic bonds checker), to adapt a pre-trained flow model so that its induced density expands beyond regions of high data availability, while preserving samples validity. We introduce formal notions of strong and weak verifiers and propose algorithmic frameworks for global and local flow expansion via probability-space optimization. Then, we present Flow Expander (FE), a scalable mirror descent scheme that provably tackles both problems by verifier-constrained entropy maximization over the flow process noised state space. Next, we provide a thorough theoretical analysis of the proposed method, and state convergence guarantees under both idealized and general assumptions. Ultimately, we empirically evaluate our method on both illustrative, yet visually interpretable settings, and on a molecular design task showcasing the ability of FE to expand a pre-trained flow model increasing conformer diversity while preserving validity. Scalability ; Flow (mathematics) ; Entropy (arrow of time) ; Bridging (networking) ; Convergence (economics) ; State (computer science) ; Process (computing) ; Task (project management) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Generative Adversarial Networks and Image Synthesis
Authors: Mairi McCauley | Joel Martis | Ondrej L. Krivanek | B Plotkin-Swing | Andreas Mittelberger | Tolga Wagner | Hüseyin Çelik | Grigory Kornilov | Meng Zhao | Matthias Meffert | Luca Giuseppe Piazza | Tracy C. Lovejoy | Guillaume Radtke | C KOCH | Benedikt Haas
Source: Open MIND (preprint, 2026-02-05)
Field: Materials Science — Materials Chemistry

Platform and Framework for Time-Resolved Nanoscale Thermal Transport Measurements in STEM Understanding heat transport at the nanometer scale is critical for semiconductor devices, quantum materials, and thermal management of nanostructures, yet direct local measurements of thermal conductivity and heat capacity remain scarce. We developed a laser-excitation system integrated into a scanning transmission electron microscope (STEM) for nanoscale thermal transport measurements using ultra-high-resolution electron energy-loss spectroscopy (EELS). A fiber-coupled laser is introduced via a modified aperture mechanism, enabling flexible holder geometries and large tilt angles without optical elements in the polepiece gap. Synchronization of pulsed laser excitation with an externally gated direct electron detector provides temporal resolution about 50 ns at &lt;10 meV energy resolution. Local temperatures are determined via the principle of detailed balance, and thermal transport parameters are extracted by fitting a forward-time central-space heat diffusion model including radiative losses. For amorphous carbon films, we obtain a thermal conductivity of 1.24 $\frac{W}{m\cdot K}$ and a heat capacity of 821 $\frac{J}{kg\cdot K}$, consistent with literature. This framework enables time-resolved nanoscale measurements of thermal transport in materials and devices. Thermal conductivity ; Laser ; Scanning thermal microscopy ; Thermal conduction ; Thermal ; Nanoscopic scale ; Thermal diffusivity ; Heat transfer ; Nanomanufacturing ; Bolometer Thermal properties of materials ; Advanced Electron Microscopy Techniques and Applications ; Thermal Radiation and Cooling Technologies
Authors: Alexey Kabalnov
Source: Open MIND (preprint, 2026-02-27)
Field: Chemistry — Organic Chemistry

Solubilization kinetics of oils by ionic and nonionic micelles: theoretical model Experimental data on solubilization kinetics found in literature were analyzed by using the model proposed earlier (1). The rates of oil molecular exchange between the micellar core and the surrounding aqueous solution were determined. It was concluded that the solubilization of hydrocarbon molecules by nonionic surfactants of ethylene oxide type is essentially barrier-free, that is, is diffusion controlled. It is quite different for ionic surfactants, where the rate is one-two orders of magnitude slower, indicating the existence of a potential barrier for hydrocarbons to get inside the micelles. A Fickean diffusion model of solubilization has been proposed to explain these trends. For ionic micelles, the hydrocarbons are predicted to be excluded from the micellar double layer region because of their low dielectric constant. The Poisson-Boltzmann model was used to model this effect; the diffusion retardation factors were compared with the experiment and a fair agreement was seen. For nonionic groups, such as oligoethylene oxide, on the other hand, no such barrier was predicted to exist. The analysis of this paper is performed only for the case of the 'slow' solubilization; it does not cover the case of the rapid, 'catastrophic' solubilization observed in the other group of experiments; the distinction between the slow and fast mechanisms is discussed and a possible explanation is suggested. Solubilization ; Diffusion ; Ionic bonding ; Kinetics ; Ethylene oxide ; Micellar solutions ; Hydrocarbon ; Micelle ; Aqueous solution Surfactants and Colloidal Systems ; Enhanced Oil Recovery Techniques ; Lipid Membrane Structure and Behavior
Authors: Alexey Kabalnov
Source: arXiv (Cornell University) (article, 2026-02-27)
Field: Chemistry — Organic Chemistry

Solubilization kinetics of oils by ionic and nonionic micelles: theoretical model Experimental data on solubilization kinetics found in literature were analyzed by using the model proposed earlier (1). The rates of oil molecular exchange between the micellar core and the surrounding aqueous solution were determined. It was concluded that the solubilization of hydrocarbon molecules by nonionic surfactants of ethylene oxide type is essentially barrier-free, that is, is diffusion controlled. It is quite different for ionic surfactants, where the rate is one-two orders of magnitude slower, indicating the existence of a potential barrier for hydrocarbons to get inside the micelles. A Fickean diffusion model of solubilization has been proposed to explain these trends. For ionic micelles, the hydrocarbons are predicted to be excluded from the micellar double layer region because of their low dielectric constant. The Poisson-Boltzmann model was used to model this effect; the diffusion retardation factors were compared with the experiment and a fair agreement was seen. For nonionic groups, such as oligoethylene oxide, on the other hand, no such barrier was predicted to exist. The analysis of this paper is performed only for the case of the 'slow' solubilization; it does not cover the case of the rapid, 'catastrophic' solubilization observed in the other group of experiments; the distinction between the slow and fast mechanisms is discussed and a possible explanation is suggested. Solubilization ; Diffusion ; Ionic bonding ; Kinetics ; Ethylene oxide ; Micellar solutions ; Hydrocarbon ; Micelle ; Aqueous solution Surfactants and Colloidal Systems ; Enhanced Oil Recovery Techniques ; Lipid Membrane Structure and Behavior
Authors: Jun Zha | Guoqing Zu | Mingyu Li | Xingang Liu | Pengwei Liu | Xingwang Cheng | Zhiping Xiong
Source: Composites Communications (article, 2026-02-06)
Field: Materials Science — Materials Chemistry

Bio-inspired brick-and-mortar TiNi/Ti2Ni composites: Microstructural evolution mechanisms and conditional generative prediction Dynamic recrystallization ; Brittleness ; Recrystallization (geology) ; Probabilistic logic ; Generative grammar ; Microstructure ; Deformation (meteorology) ; Nonlinear system ; Nanoscopic scale Shape Memory Alloy Transformations ; Metallurgy and Material Forming ; Titanium Alloys Microstructure and Properties
Authors: Enrico Massignani | Natalia Tichshenko | Kevin Velghe | Lennart Martens
Source: bioRxiv (Cold Spring Harbor Laboratory) (article, 2026-01-21)
Field: Chemistry — Spectroscopy

MoDPA: Inferring Modification-dependent Protein Associations from Uniformly Reprocessed Mass Spectrometry Abstract Motivation Post-translational modifications (PTMs) are key regulators of protein function and cellular processes; however, the overall principles of PTM co-regulation and crosstalk remain to be fully understood. A major challenge in large-scale PTM crosstalk studies is the scarcity of data, which hampers the ability to reproducibly detect proteome-wide associations between modification sites. Results We present a new computational framework, named Modification-Dependent Protein Associations (MoDPA). To overcome the extreme sparsity and heterogeneity of PTM calls across experiments, MoDPA utilizes a variational autoencoder (VAE) to embed per-site detection profiles into a low-dimensional latent space that preserves covariation while denoising missing data. A PTM association network is constructed by correlating latent representations across experiments. Benchmarking against pulsed SILAC data shows that MoDPA can correctly capture the correlation between heavy- and light-labelled peptides. We apply MoDPA to a large sample of reprocessed public datasets and identify clusters of modified proteins involved in distinct biological pathways, suggesting that some modifications may preferentially regulate specific biological processes, but not others. In particular, we find clusters enriched in lysine acetylation, lysine methylation, and arginine deamidation (citrullination) sites, which contain proteins involved in protein synthesis, neuron development, and cellular senescence. Availability and Implementation All code is freely available on GitHub ( https://github.com/CompOmics/MoDPAv1.0 ) and Zenodo (10.5281/zenodo.18310674). The resulting MoDPA-derived PTM network is available via the TabloidProteome website at https://iomics.ugent.be/tabloidproteome . Contact enrico.massignani@ugent.be , lennart.martens@ugent.be Crosstalk ; Deamidation ; Lysine ; Autoencoder ; Latent variable ; Proteomics ; Posttranslational modification ; Benchmarking ; Pattern recognition (psychology) Advanced Proteomics Techniques and Applications ; Bioinformatics and Genomic Networks ; Machine Learning in Bioinformatics
Authors: E. Ateia | Yousra Yasser | Amira S. Shafaa
Source: Journal of Sol-Gel Science and Technology (article, 2026-02-05)
Field: Materials Science — Materials Chemistry

Fabrication, characterization and adsorption of lead ions by doped barium hexaferrite nanoparticles Abstract The citrate combustion technique was employed to synthesize barium hexaferrites represented by the formula BaFe 11.5 Y 0.5 O 19 , where Y denotes Zr, Zn, Ni, and Gd. XRD analysis confirms the hexagonal structure of these materials, which are classified under the space group P63/MMC-(No 194). The smallest crystallite size is observed in BaFe 11.5 Gd 0.5 O 19 , with measurements recorded at 36.968 nm according to the Halder-Wagner method. The band gap is calculated using the Tauc model. The introduction of dopant ions (Zr, Zn, Ni, and Gd) resulted in the establishment of electronic energy levels, which caused distortions in the sample and generated new locations for stabilizing charge carrier species within the samples. The BET plot shows H3 hysteresis with a type II isotherm, indicating that the inter-particle voids contribute to meso-porosity with an average pore diameter of 9.351 nm. The samples are applied in wastewater treatment, specifically serving as purifiers for lead-contaminated water. The impact of contact time is examined for all samples, revealing that BaFe 11.5 Zr 0.5 O 19 and BaFe 11.5 Gd 0.5 O 19 achieved the highest efficiencies of 99.693% and 99.237%, respectively. Moreover, BaFe 11.5 Zr 0.5 O 19 adhered to the intra-particle diffusion model, while BaFe 11.5 Zn 0.5 O 19 , BaFe 11.5 Ni 0.5 O 19 , and BaFe 11.5 Gd 0.5 O 19 correspond to the pseudo-second-order model, suggesting that the adsorption mechanism is primarily influenced by chemical processes. Barium ; Crystallite ; Dopant ; Analytical Chemistry (journal) ; Doping ; Adsorption ; Ion ; Diffusion Magnetic Properties and Synthesis of Ferrites ; Electromagnetic wave absorption materials ; Advancements in Battery Materials
Authors: Xiaochen Du | Juno Nam | Sulin Liu | Rafael Gómez-Bombarelli
Source: arXiv (Cornell University) (preprint, 2026-03-16)
Field: Materials Science — Materials Chemistry

Scaling Autoregressive Models for Lattice Thermodynamics Predicting how materials behave under realistic conditions requires understanding the statistical distribution of atomic configurations on crystal lattices, a problem central to alloy design, catalysis, and the study of phase transitions. Traditional Markov-chain Monte Carlo sampling suffers from slow convergence and critical slowing down near phase transitions, motivating the use of generative models that directly learn the thermodynamic distribution. Existing autoregressive models (ARMs), however, generate configurations in a fixed sequential order and incur high memory and training costs, limiting their applicability to realistic systems. Here, we develop a framework combining any-order ARMs, which generate configurations flexibly by conditioning on any known subset of lattice sites, with marginalization models (MAMs), which approximate the probability of any partial configuration in a single forward pass and substantially reduce memory requirements. This combination enables models trained on smaller lattices to be reused for sampling larger systems, while supporting expressive Transformer architectures with lattice-aware positional encodings at manageable computational cost. We demonstrate that Transformer-based any-order MAMs achieve more accurate free energies than multilayer perceptron-based ARMs on both the two-dimensional Ising model and CuAu alloys, faithfully capturing phase transitions and critical behavior. Overall, our framework scales from $10 \times 10$ to $20 \times 20$ Ising systems and from $2 \times 2 \times 4$ to $4 \times 4 \times 8$ CuAu supercells at reduced computational cost compared to conventional sampling methods. Ising model ; Autoregressive model ; Monte Carlo method ; Scaling ; Lattice (music) ; Importance sampling ; Phase transition ; Sampling (signal processing) Machine Learning in Materials Science ; Quantum many-body systems ; Theoretical and Computational Physics
Authors: Prince Setia | Soheil Shaker | Oluwadoyinsola Haastrup | SAMUEL AFOLABI | Keshav Malhotra | Olanrewaju Ojo
Source: International Journal of Mechanical Sciences (article, 2026-02-28)
Field: Materials Science — Materials Chemistry

Investigating the assumption of time-independent diffusion coefficients calculated from experiments Isothermal process ; Diffusion ; Thermal diffusivity ; Constant (computer programming) ; Impurity ; Fick's laws of diffusion Nuclear Materials and Properties ; High Temperature Alloys and Creep ; Nuclear reactor physics and engineering