jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Vahidullah Tac | Ellen Kuhl
Source: arXiv (Cornell University) (article, 2026-04-03)
Field: Materials Science — Materials Chemistry

Generative AI for material design: A mechanics perspective from burgers to matter Generative artificial intelligence offers a new paradigm to design matter in high-dimensional spaces. However, its underlying mechanisms remain difficult to interpret and limit adoption in computational mechanics. This gap is striking because its core tools-diffusion, stochastic differential equations, and inverse problems-are fundamental to the mechanics of materials. Here we show that diffusion-based generative AI and computational mechanics are rooted in the same principles. We illustrate this connection using a three-ingredient burger as a minimal benchmark for material design in a low-dimensional space, where both forward and reverse diffusion admit analytical solutions: Markov chains with Bayesian inversion in the discrete case and the Ornstein-Uhlenbeck process with score-based reversal in the continuous case. We extend this framework to a high-dimensional design space with 146 ingredients and 8.9x10^43 possible configurations, where analytical solutions become intractable. We therefore learn the discrete and continuous reverse processes using neural network models that infer inverse dynamics from data. We train the models on only 2,260 recipes and generate one million samples that capture the statistical structure of the data, including ingredient prevalence and quantitative composition. We further generate five new burgers and validate them in a restaurant-based sensory study with 100 participants, where three of the AI-designed burgers outperform the classical Big Mac in overall liking, flavor, and texture. These results establish diffusion-based generative modeling as a physically grounded approach to design in high-dimensional spaces. They position generative AI as a natural extension of computational mechanics, with applications from burgers to matter, and establish a path toward data-driven, physics-informed generative design. Generative grammar ; Generative model ; Artificial neural network ; Markov chain ; Statistical mechanics ; Limit (mathematics) ; Bayesian probability ; Process (computing) ; Generative Design Machine Learning in Materials Science ; Design Education and Practice ; Music Technology and Sound Studies
Authors: Jacob Wekalao | Hussein A. Elsayed | Emad Solouma | Ahmed Mehaney | Amuthakkannan Rajakannu | Pelluce Kabarokole | May Bin‐Jumah
Source: Figshare (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

High-sensitivity multilayer SPR biosensor based on ZnO/Graphene/Ag/MXene architecture for simultaneous detection of leptospirosis, malaria, and glucose imbalance Infectious diseases and metabolic disorders remain major global health challenges, especially in areas with limited diagnostic infrastructure. Conventional diagnostic methods are often costly, complex, and restricted to detecting a single analyte. This study presents a multilayer surface plasmon resonance (SPR) biosensor composed of zinc oxide (ZnO), graphene, silver (Ag), and MXene layers for simultaneous, label-free detection of leptospirosis, malaria, and glucose imbalance. The combined material system improves plasmonic field confinement, increases surface electron density, and strengthens biomolecular interaction sensitivity. The sensor uses a Kretschmann configuration with p-polarized light excitation and is analyzed through the transfer matrix method. Systematic optimization of layer thickness and refractive index variation improves optical performance across several biomarkers. Numerical simulations show that the sensor achieves 1240°/RIU sensitivity with a 1.5° resonance shift for polyuria detection, 2° angular tuning for oliguria, a 2.5° resonance shift for glucose detection, and a 10° shift with 271.43°/RIU sensitivity for malaria biomarkers. Quality factors range from 19.76 to 24.03, and detection limits vary between 0.006 and 0.134 RIU across analytes. Regression-based machine learning analysis verifies the model’s predictive accuracy, with R² values above 0.995 for refractive index variation. Biosensor ; Surface plasmon resonance ; Refractive index ; Nanosensor ; Resonance (particle physics) ; Sensitivity (control systems) ; Plasmon MXene and MAX Phase Materials ; Advanced biosensing and bioanalysis techniques ; Plasmonic and Surface Plasmon Research
Authors: Linlin Sun | Jie Xiong | Qingshuang Ma | Chenghao Pei | Huijun Li | Qiuzhi Gao
Source: Progress in Natural Science Materials International (article, 2026-02-01)
Field: Materials Science — Materials Chemistry

A comprehensive review of machine learning for superalloys: from data-driven prediction to intelligent design Generalizability theory ; Workflow ; Transformative learning ; Data-driven ; Feature (linguistics) ; Flexibility (engineering) Machine Learning in Materials Science ; High Temperature Alloys and Creep ; Advanced Combustion Engine Technologies
Authors: Zhao Wang | Zhenfu Tian
Source: The Journal of Physical Chemistry Letters (article, 2026-03-03)
Field: Materials Science — Materials Chemistry

Active Learning of Anharmonic Phonon Transport Across Carbon Allotropes Machine-learning potentials (MLPs) face significant challenges in sampling anharmonic lattice dynamics due to computational bottlenecks. This work presents an active learning (AL) framework that integrates pretrained potentials with committee models to overcome these limitations. This approach achieves a 59.9% reduction in force errors while maintaining exceptional transferability across diverse carbon allotropes. The optimized potential model reveals distinct anharmonic regimes: in diamond, the longitudinal acoustic phonons exhibit a temperature-induced sign reversal in their lifetime scaling exponent (from <i>n</i> = -1.54 at 200 K to <i>n</i> = 0.17 at 800 K), marking a fundamental transition from Umklapp scattering to fluctuation-induced localization where thermal disorder preferentially suppresses low-frequency phonon propagation; in bilayer graphene, the strongly negative Grüneisen parameters of flexural acoustic (ZA) phonons [<i>γ</i>(<b><i>q</i></b>) = -33.7 to -8.4] reveal anomalous vibrational stiffening under volume expansion, originating from stress release in the interlayer coupling that enhances in-plane restoring forces. These findings establish a general strategy for MLPs development and uncover emergent phonon phenomena, providing new pathways for controlling thermal transport in quantum materials and nanoscale devices. Anharmonicity ; Phonon ; Scaling ; Coupling (piping) ; Lattice (music) ; Thermal conductivity ; Scattering Thermal properties of materials ; Machine Learning in Materials Science ; Boron and Carbon Nanomaterials Research
Authors: Jacob Wekalao | Hussein A. Elsayed | Emad Solouma | Ahmed Mehaney | Amuthakkannan Rajakannu | Pelluce Kabarokole | May Bin‐Jumah
Source: Open MIND (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

High-sensitivity multilayer SPR biosensor based on ZnO/Graphene/Ag/MXene architecture for simultaneous detection of leptospirosis, malaria, and glucose imbalance Infectious diseases and metabolic disorders remain major global health challenges, especially in areas with limited diagnostic infrastructure. Conventional diagnostic methods are often costly, complex, and restricted to detecting a single analyte. This study presents a multilayer surface plasmon resonance (SPR) biosensor composed of zinc oxide (ZnO), graphene, silver (Ag), and MXene layers for simultaneous, label-free detection of leptospirosis, malaria, and glucose imbalance. The combined material system improves plasmonic field confinement, increases surface electron density, and strengthens biomolecular interaction sensitivity. The sensor uses a Kretschmann configuration with p-polarized light excitation and is analyzed through the transfer matrix method. Systematic optimization of layer thickness and refractive index variation improves optical performance across several biomarkers. Numerical simulations show that the sensor achieves 1240°/RIU sensitivity with a 1.5° resonance shift for polyuria detection, 2° angular tuning for oliguria, a 2.5° resonance shift for glucose detection, and a 10° shift with 271.43°/RIU sensitivity for malaria biomarkers. Quality factors range from 19.76 to 24.03, and detection limits vary between 0.006 and 0.134 RIU across analytes. Regression-based machine learning analysis verifies the model’s predictive accuracy, with R² values above 0.995 for refractive index variation. Biosensor ; Surface plasmon resonance ; Refractive index ; Nanosensor ; Resonance (particle physics) ; Sensitivity (control systems) ; Plasmon MXene and MAX Phase Materials ; Advanced biosensing and bioanalysis techniques ; Plasmonic and Surface Plasmon Research
Authors: Ghulam Ullah | Daud Akhtar | S.S.A. Gillani
Source: Journal of Physics and Chemistry of Solids (article, 2026-03-02)
Field: Materials Science — Materials Chemistry

Machine learning-assisted DFT analysis of BaGaO3 under pressure for band gap prediction and photocatalytic water splitting CASTEP ; Band gap ; Phonon ; Hydrostatic pressure ; Ab initio ; Bulk modulus ; Density functional theory ; Direct and indirect band gaps ; Ab initio quantum chemistry methods Machine Learning in Materials Science ; Ga2O3 and related materials ; Advanced Photocatalysis Techniques
Authors: Felipe Hawthorne | Marcelo Lopes Pereira Júnior | Fabiano M. Andrade | Cristiano Francisco Woellner | Raphael M. Tromer
Source: arXiv (Cornell University) (preprint, 2026-04-02)
Field: Materials Science — Materials Chemistry

CARBON-2D Topological Descriptor (C2DTD): An Interpretable and Physics-Informed Representation for Two-Dimensional Carbon Networks Two-dimensional (2D) carbon networks, from pristine graphene to defect-rich and amorphous monolayers, exhibit a complex structure-energy landscape governed not only by local bonding but also by medium-range order and network topology. Capturing these multi-scale effects in a compact, interpretable, and data-efficient manner remains a major challenge for machine learning (ML) in low-dimensional materials. In this work, we introduce the CARBON-2D Topological Descriptor (C2DTD), a physically informed structural representation specifically designed for 2D carbon systems. The descriptor integrates local geometric statistics, a compact radial structural signature, and explicit primitive ring topology into a fixed-length, invariant vector that is both computationally efficient and directly interpretable. Benchmarking on diverse datasets of 2D carbon allotropes and defect-engineered graphene sheets demonstrates that C2DTD achieves robust predictive performance in small-data regimes, outperforming generic high-dimensional featurization schemes while preserving physical transparency. Unsupervised manifold analysis reveals a smoother alignment between descriptor space and the DFT energy landscape, and feature-importance and ablation studies confirm that ring topology emerges as a dominant energetic driver, particularly under vacancy-induced reconstruction. Furthermore, controlled simulations with 5-15% random vacancies show that C2DTD naturally captures the progressive transition from hexagon-dominated graphene to topologically disordered networks, enabling both dataset-level and structure-specific interpretation. Owing to its compactness, interpretability, and strong physics-based inductive bias, C2DTD provides a fast and generalizable framework for data-driven modeling, defect analysis, and high-throughput screening of 2D carbon materials. Topology (electrical circuits) ; Representation (politics) ; Manifold (fluid mechanics) ; Graphene ; Invariant (physics) ; Vector space ; Ring (chemistry) ; Space (punctuation) Graphene research and applications ; 2D Materials and Applications ; Topological Materials and Phenomena
Authors: Yaju Zhao | Hang Yuan | Yinsheng Zhang | Minmin Tang | Haiyan Wang
Source: Measurement (article, 2026-01-18)
Field: Chemistry — Spectroscopy

Window function-assisted MALDI-TOF MS data preprocessing combined with variables intersection and machine learning for classification analysis Preprocessor ; Feature selection ; Support vector machine ; Pattern recognition (psychology) ; Data pre-processing ; Classifier (UML) ; Feature extraction ; Pipeline (software) Mass Spectrometry Techniques and Applications ; Spectroscopy and Chemometric Analyses ; Metabolomics and Mass Spectrometry Studies
Authors: Sheng Qian | Xin Zhang | Zun Liang | Hongtao Liang | Yang Yang
Source: Surfaces and Interfaces (article, 2026-01-18)
Field: Materials Science — Materials Chemistry

Machine-learning investigation of layer-resolved collective dynamics in interfacial liquid Fe Work (physics) ; Molecular dynamics ; Crossover ; Relaxation (psychology) ; Scattering ; Symmetry (geometry) ; Acceleration ; Dynamics (music) ; Order (exchange) Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Material Dynamics and Properties
Authors: Utami Dyah Syafitri | I.  Gusti Ngurah Sentana Putra | Ririn Ristovani | Ade  Heri Mulyati | Eti Rohaeti | Anang Kurnia | Mohamad Rafi
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Chemistry — Analytical Chemistry

Modified Cuckoo Search Algorithm for Optimizing Hyperparameters in Bagging and Boosting Models for Identification and Authentication of Strobilanthes crispa from Piper aduncum Boosting (machine learning) ; Gradient boosting ; Pattern recognition (psychology) ; Preprocessor ; Data pre-processing ; Linear discriminant analysis ; AdaBoost Spectroscopy and Chemometric Analyses ; Piperaceae Chemical and Biological Studies ; Advanced Chemical Sensor Technologies
Authors: Marco Cherubini | Abhishek Raghav | Michele Casula
Source: Open MIND (preprint, 2026-01-31)
Field: Chemistry — Physical and Theoretical Chemistry

Ferroelectric quantum critical point in superconducting hydrides: The case of H$_3$S H$_3$S sulfur hydride has been widely investigated for its high superconducting critical temperature $T_c$ of 203 K at about $p_c = 155$ GPa. Despite being the precursor of superconducting hydrides, a detailed picture of its phase diagram in an extended temperature and pressure range is still missing. To determine it with inclusion of both thermal and quantum effects, we carry out path integral molecular dynamics combined to a MACE neural network potential trained on BLYP density functional theory configurations. The resulting H$_3$S phase diagram is characterized by the ferroelectric transition between the Im$\bar{3}$m and R3m phases, which originates from a quantum critical point (QCP) located at $p_\mathrm{QCP} \approx 134$ GPa. We show that the experimental $T_c$ peak falls into a paraelectric region of large nuclear quantum fluctuations above the ferroelectric QCP, as measured by local phonon Green's functions resolved in imaginary time, where fluctuating dipole moments are at play. We study the critical behavior of the system in the proximity of the QCP by a finite-size scaling analysis, showing that it belongs to the 4D Ising universality class. We finally discuss its implications for the superconducting state. Quantum critical point ; Superconductivity ; Phase diagram ; Critical point (mathematics) ; Ferroelectricity ; Quantum phase transition ; Critical exponent ; Quantum ; Dipole Advanced Physical and Chemical Molecular Interactions ; High-pressure geophysics and materials ; Solid-state spectroscopy and crystallography
Authors: Yongjian Zhou | Haoran Cui | Zefang Ye | Jung-Fu Lin | Yan Wang | Yaguo Wang
Source: Open MIND (preprint, 2026-02-16)
Field: Materials Science — Materials Chemistry

Stacking-Engineered Thermal Transport and Phonon Filtering in Rhenium Disulfide Cross-plane heat transport is a critical bottleneck for van der Waals (vdW) electronics, yet its microscopic governing principles remain elusive. We demonstrate that stacking order is an effective control knob for cross-plane phonon transport in multilayer Rhenium Disulfide (ReS2). Thickness-dependent thermal conductivity measurements reveal remarkably long cross-plane phonon mean free paths (MFPs) (&gt;= 200-300 nm) and provide a direct experimental observation of the transition from quasi-ballistic transport to a thickness-independent ballistic limit. AA stacking exhibits nearly double the cross-plane thermal conductivity of AB stacking, driven by longer acoustic phonon lifetimes from a more "coherent" interlayer registry. Integrated deep neural-network molecular dynamics reveals that phonon filtering in ReS2 is fundamentally frequency-selective: weak vdW coupling acts as a low-pass filter, whereas stronger coupling broadens the transmission passband. These results establish ReS2 as a model system where stacking order and interlayer coupling can be engineered to tune heat conduction across diffusive, quasi-ballistic, and ballistic regimes, offering a new framework for thermal management in 2D electronics. Stacking ; Phonon ; Thermal conductivity ; Thermal conduction ; Rhenium ; van der Waals force ; Coupling (piping) Thermal properties of materials ; 2D Materials and Applications ; Advanced Thermoelectric Materials and Devices
Authors: Hirokuni Jintoku
Source: ACS Applied Materials & Interfaces (article, 2026-04-09)
Field: Materials Science — Materials Chemistry

Machine-Learning-Based Dispersion Optimizer for Carbon Nanotubes across Dispersant–Solvent–Process Space Appropriately pairing dispersants and solvents and tuning processing conditions yield high-quality, stable carbon nanotube (CNT) dispersions; however, experimental investigation of this combinatorial design space is slow and nonsystematic. We report a machine-learning-based CNT dispersion optimizer that simultaneously considers dispersant, solvent, and the dispersion process to predict CNT dispersibility and crystallinity. The data set comprised 666 dispersions (36 organic dispersants, 22 solvents, and two dispersion methods) with systematic variations in composition and processing parameters. <i>D</i><sub>score</sub> and <i>I</i><sub>G</sub>/<i>I</i><sub>D</sub> quantified the dispersion quality and structural integrity, respectively. Using molecular descriptors, experimental variables, and solvent-dispersant similarity metrics as inputs, an eXtreme Gradient Boosting (XGBoost) model achieved a coefficient of determination (<i>R</i><sup>2</sup>) = 0.57, mean absolute error (MAE) = 0.08 for <i>D</i><sub>score</sub> and <i>R</i><sup>2</sup> = 0.73, MAE = 9.84 for <i>I</i><sub>G</sub>/<i>I</i><sub>D</sub>. These correspond to mean absolute errors below 10% of the target ranges, indicating sufficient performance for screening-grade formulation design. Limited quantitative accuracy was displayed for dispersants outside the training set; solvent-dependent trends were reproduced, and practically useful formulations were identified. Virtual screening within the learned domain yielded improved formulations. SHapley Additive exPlanations and feature-group ablation revealed that <i>D</i><sub>score</sub> was governed primarily by solvent-dispersant compatibility encoded by similarity and distance-like descriptors, while <i>I</i><sub>G</sub>/<i>I</i><sub>D</sub> was dominated by process intensity. These elements constitute a CNT dispersion optimizer that efficiently prescreens formulation and processing conditions and can be extended to other nanomaterial dispersion systems. This prescreening framework reduces empirical trial-and-error and promotes solvent- and process-constrained formulation design by treating dispersions as enabling intermediate materials for the downstream manufacturing of films, fibers, and composites. Carbon nanotube ; Dispersion (optics) ; Space (punctuation) ; Carbon fibers ; Work (physics) Machine Learning in Materials Science ; Carbon Nanotubes in Composites ; Process Optimization and Integration
Authors: Marco Cherubini | Abhishek Raghav | Michele Casula
Source: ArXiv.org (article, 2026-01-31)
Field: Chemistry — Physical and Theoretical Chemistry

Ferroelectric quantum critical point in superconducting hydrides: The case of H$_3$S H$_3$S sulfur hydride has been widely investigated for its high superconducting critical temperature $T_c$ of 203 K at about $p_c = 155$ GPa. Despite being the precursor of superconducting hydrides, a detailed picture of its phase diagram in an extended temperature and pressure range is still missing. To determine it with inclusion of both thermal and quantum effects, we carry out path integral molecular dynamics combined to a MACE neural network potential trained on BLYP density functional theory configurations. The resulting H$_3$S phase diagram is characterized by the ferroelectric transition between the Im$\bar{3}$m and R3m phases, which originates from a quantum critical point (QCP) located at $p_\mathrm{QCP} \approx 134$ GPa. We show that the experimental $T_c$ peak falls into a paraelectric region of large nuclear quantum fluctuations above the ferroelectric QCP, as measured by local phonon Green's functions resolved in imaginary time, where fluctuating dipole moments are at play. We study the critical behavior of the system in the proximity of the QCP by a finite-size scaling analysis, showing that it belongs to the 4D Ising universality class. We finally discuss its implications for the superconducting state. Quantum critical point ; Superconductivity ; Phase diagram ; Critical point (mathematics) ; Ferroelectricity ; Quantum phase transition ; Critical exponent ; Quantum ; Dipole Advanced Physical and Chemical Molecular Interactions ; High-pressure geophysics and materials ; Solid-state spectroscopy and crystallography
Authors: Chen Hao Xia | Manasa Kaniselvan | Marko Mladenoivić | Mathieu Luisier
Source: arXiv (Cornell University) (preprint, 2026-02-02)
Field: Materials Science — Materials Chemistry

Machine-Learned Hamiltonians for Quantum Transport Simulation of Valence Change Memories The construction of the Hamiltonian matrix \textbf{H} is an essential, yet computationally expensive step in \textit{ab-initio} device simulations based on density-functional theory (DFT). In homogeneous structures, the fact that a unit cell repeats itself along at least one direction can be leveraged to minimize the number of atoms considered and the calculation time. However, such an approach does not lend itself to amorphous or defective materials for which no periodicity exists. In these cases, (much) larger domains containing thousands of atoms might be needed to accurately describe the physics at play, pushing DFT tools to their limit. Here we address this issue by learning and directly predicting the Hamiltonian matrix of large structures through equivariant graph neural networks and so-called augmented partitioning training. We demonstrate the strength of our approach by modeling valence change memory (VCM) cells, achieving a Mean Absolute Error (MAE) of 3.39 to 3.58 meV, as compared to DFT, when predicting the Hamiltonian matrix entries of systems made of $\sim$5,000 atoms. We then replace the DFT-computed Hamiltonian of these VCMs with the predicted one to compute their energy-resolved transmission function with a quantum transport tool. A qualitatively good agreement between both sets of curves is obtained. Our work provides a path forward to overcome the memory and computational limits of DFT, thus enabling the study of large-scale devices beyond current \textit{ab-initio} capabilities Hamiltonian (control theory) ; Quantum ; Valence (chemistry) ; Hamiltonian matrix ; Homogeneous ; Graph ; Topology (electrical circuits) ; Artificial neural network Machine Learning in Materials Science ; Quantum many-body systems ; Advanced Memory and Neural Computing