jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Asmita Jana | Fabrice Roncoroni | Julie C. Fornaciari | Aditi S. Krishnapriyan | D. Prendergast | Adam Weber | Ethan J. Crumlin | Jin Qian
Source: ChemRxiv (article, 2026-01-29)
Field: Materials Science — Materials Chemistry

Benchmarking Machine-Learned Potentials for Water-Splitting Catalysts: Validation on Pt and IrO2 Surfaces Using OC20 and OMat24 Machine Learning Interatomic Potentials (MLIPs) enable first-principles accuracy in modeling complex catalytic interfaces at reduced cost. The Open Catalyst 2020 (OC20) and Open Materials 2024 (OMat24) datasets consist of density functional theory (DFT) data for adsorbates on metals and inorganic bulk materials, respectively. In this work, we benchmarked the Universal Model for Atoms (UMA) trained on the OC20 and OMat24 datasets with DFT calculations for adsorption energies on Pt and IrO2, respectively. We evaluated adsorption energies of common adsorbates such as *H, *O, *CO, and *NO on Pt, and reaction intermediates in the water splitting reaction, such as *O, *OH, and *H, on IrO2. UMA-OC20, trained using non-spin polarized DFT-RPBE energies with no van der Waals (vdW) corrections on metallic surfaces, reproduced the adsorption energies from the same flavor of DFT within 0.1 eV. However, the energy difference between UMA-OC20 and spin-polarized DFT was ∼1.2 eV, with the difference mainly arising from the reference gases. UMA-OMat24, trained using spin-polarized DFT-PBE with vdW corrections, predicted adsorption energies within 0.3 eV of the energies from the same flavor of DFT on IrO2. When both the DFT flavor and geometry were kept fixed between the MLIPs and DFT, UMA-OC20 and UMA-OMat24 predicted adsorption energies within 0.1 and 0.2 eV of their corresponding DFT adsorption energies, respectively. The norm of coordinate differences between UMA-OC20 and DFT was < 0.005 Å while it was < 0.3 Å between UMA-OMat24 and DFT. MLIP requires considerably fewer (∼6000 times less) CPU-hours compared to DFT. Thus, we recommend opting for an MLIP-followed-by-DFT procedure, as it not only reduces the total compute time but also ensures accurate geometry and energies. Density functional theory ; Adsorption ; van der Waals force ; Catalysis ; Metal ; Energy minimization ; Interatomic potential Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Ammonia Synthesis and Nitrogen Reduction
Authors: Y. Muto | Michael R. Zielewski | Motoya Shinozaki | Kosuke Noro | Tomohiro Otsuka
Source: Scientific Reports (preprint, 2026-02-14)
Field: Materials Science — Surfaces, Coatings and Films

Automatic detection of single-electron regime and virtual gate definition in quantum dots using U-Net and clustering To realize practical quantum computers, a large number of quantum bits (qubits) will be required. Semiconductor spin qubits offer advantages such as high scalability and compatibility with existing semiconductor technologies. However, as the number of qubits increases, manual qubit tuning becomes infeasible, motivating automated tuning approaches. In this study, we use U-Net, a neural network method for object detection, to identify charge transition lines in experimental charge stability diagrams. The extracted charge transition lines are analyzed using the Hough transform to determine their positions and angles. Based on this analysis, we obtain the transformation matrix to virtual gates. Furthermore, we identify the single-electron regime by clustering the Hough transform outputs. We also show the single-electron regime within the virtual gate space. These sequential processes are performed automatically. This approach will advance automated control technologies for large-scale quantum devices. Quantum dot ; Cluster analysis ; Net (polyhedron) ; Electron ; Computer science ; Physics ; Algorithm ; Nanotechnology ; Artificial intelligence ; Mathematics ; Quantum mechanics ; Materials science ; Geometry Electron and X-Ray Spectroscopy Techniques ; Integrated Circuits and Semiconductor Failure Analysis ; Advanced Electron Microscopy Techniques and Applications
Authors: Yuzhou Hao | Yujie Liu | Xuejie Li | Turab Lookman | Xiangdong Ding | Jun Sun | Zhibin Gao
Source: arXiv (Cornell University) (article, 2026-02-26)
Field: Materials Science — Materials Chemistry

LLM-driven discovery for carbon allotropes with bond-network entropy The discovery of novel carbon allotropes with tailored thermal and mechanical properties is critical for advanced thermal management. However, exploring the vast configurational space of carbon using \textit{ab initio} calculations remains computationally prohibitive. Driven by the rich topological landscape of carbon, where the competition between $sp, sp^2,$ and $sp^3$ hybridization states dictates material performance, we establish a closed-loop AI framework to explore this complex configurational space. We introduce a hybridization entropy descriptor to guide the search beyond conventional forms. Here, we establish a closed-loop AI framework that synergizes a Large Language Model (LLM) for structural generation with a Machine Learning Potential (MLP) for accelerated evaluation. Leveraging CrystaLLM to generate candidates and an iteratively refined MLP for high-fidelity validation, we screened thousands of structures to identify several stable allotropes with exotic properties. Specifically, we report ``yne-diamond C$_{12}$'' and ``yne-hex-diamond C$_{8}$'', which exhibit extreme thermal anisotropy and ultralow in-plane shear stiffness arising from their mixed $sp$-$sp^3$ hybridization. Furthermore, we discovered a complex $sp$-$sp^2$-$sp^3$ hybridized C$_{12}$ phase that combines metallic conductivity with an anomalous negative Poisson's ratio. Notably, we identified a superhard phase (C16_3) possessing a calculated Vickers hardness (103.3 GPa) exceeding that of diamond 96 GPa). Microscopic analysis reveals that thermal transport in these materials is governed by the interplay between rigid frameworks and flexible linkers. This work expands the known carbon phase space and demonstrates the efficacy of coupling generative AI with machine learning potentials for the accelerated inverse design of functional materials. Anisotropy ; Inverse ; Carbon fibers ; Thermal conductivity ; Entropy (arrow of time) ; Diamond ; Phase (matter) ; Phase space Machine Learning in Materials Science ; Thermal properties of materials ; Boron and Carbon Nanomaterials Research
Authors: Yogananthan Dhanapal | Deepak Sridhar | Sulekha Khute | Paranthaman Subash | Subramaniam AnandaThangadurai
Source: Intelligent Pharmacy (article, 2026-01-01)
Field: Materials Science — Biomaterials

Artificial intelligence assisted design of nanomedicines for colorectal cancer diagnosis and therapy: Advances, challenges, and future directions Colorectal cancer (CRC) remains one of the leading causes of cancer-related morbidity and mortality worldwide, with an increasing incidence in both high- and low-income countries. Despite advances in colonoscopy, imaging, molecular biomarker testing, and multimodal therapeutic regimens, limitations in early detection, tumor heterogeneity, and resistance to conventional therapy continue to hinder effective management of CRC. Nanomedicine offers transformative opportunities for precision diagnosis and therapy by enabling targeted delivery, controlled drug release, and multimodal imaging. Concurrently, artificial intelligence (AI), including machine learning (ML), deep learning (DL), and emerging transformer-based architectures, has revolutionized the interpretation of biomedical data and guided the rational design of nanocarriers for drug delivery applications. The synergistic integration of artificial intelligence and nanotechnology has accelerated the discovery of CRC-specific biomarkers, improved formulation optimization, enabled predictive pharmacokinetic/pharmacodynamic modeling, and facilitated the design of adaptive clinical trials. Recent breakthroughs include AI-driven histopathology and imaging analysis for early CRC detection, with AI-assisted colonoscopy demonstrating sensitivity improvements of up to 94 to 97% and accuracy gains of 10 to 20% compared to standard methods. However, major challenges remain, including limited access to large, annotated CRC datasets, the interpretability of AI models in clinical decision-making, the reproducibility of complex nanoparticle synthesis, and regulatory gaps in nanomedicine. This review synthesizes the current advances in AI-assisted nanomedicine for CRC diagnosis and therapy, critically evaluates the translational barriers, and outlines future research trajectories. Ultimately, multidisciplinary collaboration across oncology, nanotechnology, the design of adaptive clinical trials, science, and regulatory science is essential to realize the full potential of AI-powered nanomedicine in CRC management Nanomedicine ; Interpretability ; Multidisciplinary approach ; Precision medicine ; Personalized medicine ; Applications of artificial intelligence ; Deep learning ; Nanocarriers Nanoparticle-Based Drug Delivery ; Computational Drug Discovery Methods ; 3D Printing in Biomedical Research
Authors: Roberto Riganti | Luca Dal Negro
Source: ArXiv.org (article, 2026-03-30)
Field: Materials Science — Materials Chemistry

A Unified Multiscale Auxiliary PINN Framework for Generalized Phonon Transport Nanoscale thermal transport is governed by the phonon Boltzmann transport equation (BTE). However, simulating the sub-continuum dynamics remains computationally prohibitive due to the high dimensionality of the phase space and the intrinsic nonlinearity of the scattering collision operator. Traditional numerical solvers and standard physics-informed neural networks (PINNs) inherently struggle with these integro-differential equations due to deterministic quadrature limitations, artificial thermalization introduced by the relaxation time approximation (RTA), and multiscale spectral bias. This work introduces a multiscale auxiliary physics-informed neural network (MTNet) to solve the generalized equation of phonon radiative transfer (GEPRT). By leveraging an auxiliary formulation, this mesh-free framework recasts the GEPRT into a fully differential system, enabling the analytical evaluation of scattering operators via automatic differentiation and facilitating scalable multi-GPU parallelization. To circumvent optimization stiffness, the architecture employs a decoupled, shallow neural network explicitly constrained by radiative equilibrium. MTNet is validated by simulating steady-state cross-plane transport in a silicon thin film, successfully capturing ballistic-diffusive regimes and characteristic boundary slips across extreme temperature gradients ($ΔT = 100$ K) beyond the standard linearization approach. Furthermore, we show that our framework successfully solves a geometric inverse problem in a slab geometry, retrieving the unknown slab thickness based only on interface temperature constraints in the mesoscopic regime. Ultimately, MTNet establishes a robust, fully differentiable foundation for predicting high-fidelity kinetic transport and extracting material properties in next-generation nanostructures. Boltzmann equation ; Phonon ; Boundary value problem ; Artificial neural network ; Nonlinear system ; Radiative transfer ; Curse of dimensionality ; Solver ; Inverse problem ; Computation Thermal properties of materials ; Thermal Radiation and Cooling Technologies ; Model Reduction and Neural Networks
Authors: Jiayi Chen | Kai Wang | Huaqing Xie | Kerong Ma | Kunlun Li
Source: Energies (article, 2026-03-10)
Field: Materials Science — Materials Chemistry

Performance Prediction of Perovskite-Catalyzed CO2 Decomposition Based on Machine-Learning Method Perovskite oxides show excellent catalytic performance for thermochemical CO2 splitting, with A/B-site cation substitution further enhancing redox activity. While traditional first-principles methods are computationally expensive, machine learning (ML) provides an efficient approach to perovskite optimization. In this paper, machine learning is employed to investigate and predict the performance of perovskite catalysts in CO2 decomposition reactions. Based on 227 perovskite compositions (A1A2)(B1B2)O3 curated from experimental literature, a total of five ML models are used, including Decision Tree, Bagging, Random Forest, Extra Trees, and Gradient Boosting Regression (GBR). The Random Forest model performed best. After hyperparameter optimization, the Random Forest model achieved an R2 of 0.910 and an MAE of 41.528 on an independent test set. SHAP analysis indicated that the thermal reduction temperature (T1) and the B1-site stoichiometric fraction (C_b1) are the most influential features governing the predicted CO yield. A higher CO yield is predicted when C_b1 ranges from 0.6 to 0.8, and T1 exceeds 1300 °C. This behavior can be attributed to the enhanced formation of oxygen vacancies at elevated temperatures and the optimized electronic structure induced by appropriate B-site stoichiometry. Random forest ; Hyperparameter ; Perovskite (structure) ; Boosting (machine learning) ; Stoichiometry ; Gradient boosting ; Decomposition ; Performance prediction ; Thermal decomposition Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; CO2 Reduction Techniques and Catalysts
Authors: Bin Liu | Zhiguo Tian | Alexander A. Barinov | Moran Wang
Source: Applied Physics Letters (article, 2026-03-16)
Field: Materials Science — Materials Chemistry

Wavelike thermal phonons revealed by localization in graphene phononic crystals The high Debye temperature and ultralong phonon mean free paths (MFPs) of graphene phononic crystals (GPnCs) enable wavelike phonon transport to manifest over extended length scales, making them an ideal platform for studying phonon localization. We employ machine-learning molecular dynamics to investigate thermal transport in periodic and aperiodic GPnCs. By combining homogeneous non-equilibrium molecular dynamics and non-equilibrium molecular dynamics, we directly extract the spectrally decomposed phonon MFP applicable to aperiodic structures. Spectral analysis establishes characteristic frequency (ωc≈10 THz) and length (Lc≈100 nm) scales for localization effects, and the nonlinear deviation in the 1/κ–1/L relationship (κ: thermal conductivity) indicates the presence of localized phonons. Lattice dynamics reveals that the increased fraction of low-participation-ratio modes and the absence of high-group-velocity modes in aperiodic graphene phononic crystals (ap-GPnC) uncover the microscopic origin of disorder-induced phonon localization. Elastic wave simulations further provide direct wave-field evidence of pronounced spatial localization of low-frequency phonons in ap-GPnC with increasing propagation distance. Phonon ; Graphene ; Aperiodic graph ; Debye ; Lattice (music) ; Debye model ; Molecular dynamics Thermal properties of materials ; Advanced Thermoelectric Materials and Devices ; Topological Materials and Phenomena
Authors: Justin Airas | Bin Zhang
Source: arXiv (Cornell University) (article, 2026-01-08)
Field: Materials Science — Materials Chemistry

Knowledge Distillation of a Protein Language Model Yields a Foundational Implicit Solvent Model Implicit solvent models (ISMs) promise to deliver the accuracy of explicit solvent simulations at a fraction of the computational cost. However, despite decades of development, their accuracy has remained insufficient for many critical applications, particularly for simulating protein folding and the behavior of intrinsically disordered proteins. Developing a transferable, data-driven ISM that overcomes the limitations of traditional analytical formulas remains a central challenge in computational chemistry. Here we address this challenge by introducing a novel strategy that distills the evolutionary information learned by a protein language model, ESM3, into a computationally efficient graph neural network (GNN). We show that this GNN potential, trained on effective energies from ESM3, is robust enough to drive stable, long-timescale molecular dynamics simulations. When combined with a standard electrostatics term, our hybrid model accurately reproduces protein folding free-energy landscapes and predicts the structural ensembles of intrinsically disordered proteins. This approach yields a single, unified model that is transferable across both folded and disordered protein states, resolving a long-standing limitation of conventional ISMs. By successfully distilling evolutionary knowledge into a physical potential, our work delivers a foundational implicit solvent model poised to accelerate the development of predictive, large-scale simulation tools. Protein folding ; Folding (DSP implementation) ; Solvent models ; Process (computing) ; Computational model ; Artificial neural network ; Protein structure prediction ; Unified Model Machine Learning in Materials Science ; Protein Structure and Dynamics ; Quantum many-body systems
Authors: Hao Liu | M. C. Ekosso | Avery Glagovich | Sarah Maurer | Joshua Schrier
Source: Langmuir (article, 2026-03-04)
Field: Chemistry — Organic Chemistry

Molecular Foundation Models for Predicting Self-Assembly in Aqueous Mixtures Pretrained foundation models offer new representations for molecular structures but have predominantly been applied to single-component property prediction. Here, we develop a concentration-weighted average of the latent representations of graph neural network and transformer-based molecular foundation models to predict the critical micelle concentrations of single surfactant, biphasic separation of binary mixtures, and liposome formation in 7-component amphiphile mixtures. Simple random forest and feed-forward neural network models trained with these representations are comparable to or exceed prior work using bespoke graph neural network methods, physicochemical features, and binary fingerprinting. High-throughput automated experiments validate the ability of our models to extrapolate to novel surfactant mixtures and to guide the iterative exploration of liposome design. Binary number ; Bespoke ; Foundation (evidence) ; Artificial neural network ; Graph ; Aqueous solution ; Amphiphile ; Molecular graph Advanced Polymer Synthesis and Characterization ; Supramolecular Self-Assembly in Materials ; Supramolecular Chemistry and Complexes
Authors: David J. Moss
Source:  (article, 2026-02-13)
Field: Materials Science — Materials Chemistry

Design and optimization of 2D-material based integrated optical polarizers using AI On-chip integration of highly anisotropic two-dimensional (2D) materials offers new opportunities for realizing high-performance polarization-selective devices. Obtaining optimized designs for such devices requires extensively sweeping large parameter spaces, which in conventional approaches relies on massive mode simulations that demand considerable computational resources. Here, we address this limitation by developing a machine learning (ML) model based on fully connected neural networks (FCNNs). Trained by using mode simulation results for low-resolution structural parameters, the FCNN model can accurately predict polarizer figures of merits (FOMs) for high-resolution parameters and rapidly map the global variation trend across the entire parameter space. We test the performance of the FCNN model using two types of polarizers with 2D graphene oxide (GO) and molybdenum disulfide (MoS2). Results show that, compared to conventional mode simulation approach, our approach can not only reduce the overall computing time by about 4 orders of magnitude, but also achieve highly accurate FOM predictions with an average deviation of less than 0.04. In addition, the measured FOM values for the fabricated devices show good agreement with the predicted ones, with discrepancies remaining below 0.2. These results validate artificial intelligence (AI) as an effective approach for designing and optimizing 2D-material-based optical polarizers with high efficiency. Polarizer ; Mode (computer interface) ; Artificial neural network ; Anisotropy ; Optical proximity correction 2D Materials and Applications ; Photonic and Optical Devices ; Photorefractive and Nonlinear Optics
Authors: A. Stubbers | E. Solano-Castrejon | B. Swartley | S. Durkee | E. Schwind | A. Ramírez-Acosta | C.R. Weinberger | O.A. Graeve | M.S. García-Vázquez | G.B. Thompson
Source: Materialia (article, 2026-01-14)
Field: Materials Science — Materials Chemistry

Machine learning assisted serial sectioning to enable rapid 3D crack network reconstruction Serial sectioning enables 3D reconstruction of microstructures, providing detailed characterization and insight into processing–structure–property relationships. However, collecting serial section data is time-consuming because many images are required to create reliable reconstructions. In this paper, we investigate the application of machine learning to interpolate intermediate images between reference images, enabling larger cut depths and reducing the total number of collected images. Thus, increasing serial sectioning efficiency and accessibility. Specifically, we describe serial sectioning and a convolutional neural network (CNN) approach with fine-tuning to interpolate crack networks in ζ-Ta 4 C 3- x . The accuracy of CNN-assisted serial sectioning datasets was evaluated using direct image comparison and crack surface area. Two crack types (planar and kinking) were identified to assess the specific capacity of the CNN to recreate complex ζ-Ta 4 C 3- x cracking environments. Results showed that the CNN-generated whole image error was 0.98% with a window size of 129 images and a corresponding cut depth of 3.84 µm. These findings suggest that the serial sectioning time could be reduced by up to 96% with minimal loss in accuracy. By significantly decreasing data collection time, this approach makes serial sectioning more practical for materials characterization and enhances our ability to study material structure and performance at the microscale. Optical sectioning ; Convolutional neural network ; Characterization (materials science) ; Window (computing) ; Image (mathematics) ; Image processing ; Artificial neural network Machine Learning in Materials Science ; Metal and Thin Film Mechanics ; Advanced ceramic materials synthesis
Authors: X. Wang | Q. Kan | H. Liu | Michal Petrů | G. Kang
Source: Composites Part A Applied Science and Manufacturing (article, 2026-03-14)
Field: Materials Science — Materials Chemistry

Physics-informed probabilistic machine learning with knowledge distillation for cross-system prediction of elastic properties in glasses for composite design Probabilistic logic ; Glass fiber ; Stiffness ; Elastic modulus ; Gaussian process ; Artificial neural network ; Fiber ; Composite number ; Linear regression ; Modulus Machine Learning in Materials Science ; Composite Material Mechanics ; Epoxy Resin Curing Processes
Authors: Qiang Ke | Xueling Lei | Kevin Huang
Source: Journal of Materials Chemistry A (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Unraveling Anisotropic Li-Ion Transport in Li 3/8 Sr 7/16 Ta 3/4 Hf 1/4 O 3 via Machine Learning Molecular Dynamics and First-Principles Modeling Solid-state Li-ion electrolytes (SSLIEs) are essential components for the development of nextgeneration rechargeable lithium batteries. Among them, the perovskite-type material Li 3/8 Sr 7/16 Ta 3/4 Hf 1/4 O 3... Anisotropy ; Lithium (medication) ; Molecular dynamics ; Work (physics) ; Feature (linguistics) Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Advancements in Battery Materials