jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Finn Birchall | Catherine M. Bishop
Source: JOM (article, 2026-04-13)
Field: Materials Science — Materials Chemistry

Static Recrystallization in Incoloy 800H: Insights from High-Throughput Macroscale Deformation Gradient Study Abstract High-throughput graded tensile samples were used to measure the response of Incoloy 800H to annealing after cold work. Macroscale strain gradients between $$\sim 0\%-27\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>∼</mml:mo> <mml:mn>0</mml:mn> <mml:mo>%</mml:mo> <mml:mo>-</mml:mo> <mml:mn>27</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> von Mises strain were tested. For these strains, full recrystallization did not occur for annealing at 1050°C or 1075°C for times up to 2 h, while full recrystallization was observed after 1 h at 1100°C. Strain–time–temperature relationships for initiation and completion of recrystallization were obtained from EBSD and hardness characterization. Special boundary fractions were analyzed for each thermomechanical condition, showing the potential of gradient samples to accelerate grain-boundary engineering studies. Extensive M 23 C 6 precipitation was observed in samples annealed at 1050°C and 1075°C, and appeared to retard recrystallization. Dissolution of M 23 C 6 was observed at 1100°C, contributing to rapid recrystallization. Relationships for recrystallized hardness and grain diameter were also developed. Prior to this study, little data on the response of Incoloy 800H to heat treatment after cold work has been published, despite it being a workhorse alloy in many industries. It is hoped that manufacturers in these industries can use this work as a guide to select optimal thermomechanical conditions for their cold-worked components. Incoloy ; Recrystallization (geology) ; Annealing (glass) ; Electron backscatter diffraction ; Dynamic recrystallization ; Thermomechanical processing ; Ultimate tensile strength ; Alloy ; Grain boundary Microstructure and mechanical properties ; Metallurgy and Material Forming ; High-Velocity Impact and Material Behavior
Authors: Yaping Wang | Yue Yang | Tingyu Xiao | Manyun Xu | Dai Shao-Yu | Xinyu Shen | Xiaohui Bai | Yanrong Chen
Source: Genes (article, 2026-02-06)
Field: Materials Science — Biomaterials

Formation Mechanisms of the Ellipsoid Egg in Silkworm (Bombyx mori): Insights from Transcriptomic Profiling Weighted gene co-expression network analysis (WGCNA) classified gene expression patterns into twelve modules, with two modules showing specificity. Thirteen hub genes were identified, which are functionally linked to translation initiation, protein density regulation, post-translational modification, and protein turnover. These findings provide foundational insights into the molecular mechanisms driving the formation of the elongated egg in silkworms. Transcriptome ; Gene ; Ellipsoid ; Gene expression profiling ; Gene expression ; Translation (biology) ; Proteome Silk-based biomaterials and applications ; Silkworms and Sericulture Research ; Invertebrate Immune Response Mechanisms
Authors: Alberto Carta | Anwesha Panda | Claude Ederer
Source: npj Computational Materials (article, 2026-01-13)
Field: Materials Science — Materials Chemistry

Importance of ligand on-site interactions for the description of Mott-insulators in DFT+DMFT Abstract Calculations combining density functional theory (DFT) and dynamical mean-field theory (DMFT) for transition metal (TM) oxides and similar compounds usually focus on improving the description of the TM d states. Here, we emphasize the importance of also accounting for corrections of the ligand p states. We demonstrate that focusing exclusively on improving the description of the TM d states results in difficulties to obtain the correct insulating behavior for a variety of materials, and requires to use values for the local interaction parameters that are inconsistent with values obtained using, e.g ., the constrained random phase approximation (cRPA). We demonstrate that, to a large part, these inconsistencies arise from using local/semi-local DFT as starting point for computing interaction parameters, and we show that applying a simple empirical correction to the low energy states not included in the correlated subspace results in improved values for the interaction parameters that then allow to obtain the correct insulating behavior. Moreover, we show that applying an approximate but realistic Hartree-Fock-like correction specifically to the O p orbitals, when they are explicitly included in the DMFT subspace, significantly improves the quantitative accuracy of the DFT+DMFT description for prototypical Mott insulators, including LaTiO 3 , LaVO 3 , and the perovskite rare-earth nickelates (RNiO 3 ). Density functional theory ; Subspace topology ; Point (geometry) ; Simple (philosophy) ; Variety (cybernetics) ; Focus (optics) ; Ligand (biochemistry) ; Phase (matter) Electronic and Structural Properties of Oxides ; Magnetic and transport properties of perovskites and related materials ; Inorganic Chemistry and Materials
Authors: Santhosh Kannan | Jainam Savla | Madhuparna Karmakar
Source: arXiv (Cornell University) (article, 2026-03-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Metal-insulator transition and thermal scales in $d$-wave altermagnet We present the first finite-temperature study of a strongly correlated $d$-wave altermagnet across the Mott insulator-metal transition using a non-perturbative numerical approach. We map out the thermal phase diagram and provide quantitative estimates of the transition scales in an interacting altermagnet. We show that altermagnetism-induced geometric frustration stabilizes a finite-temperature correlated magnetic metal and enhances the magnetic transition scale across regimes of interaction. These results establish the finite-temperature landscape of correlated altermagnets and clarify the role of strong electronic interactions in this phase. Phase diagram ; Thermal ; Frustration ; Scale (ratio) ; Thermal fluctuations ; Diagram ; Phase transition Magnetic and transport properties of perovskites and related materials ; Physics of Superconductivity and Magnetism ; Rare-earth and actinide compounds
Authors: Filipa Blasco Tavares Pereira Lopes | Daniela Schlatzer | Tara Sudhadevi | Anantha Harijith | Marzieh Ayati | Mehmet Koyutürk | Mark R. Chance
Source: Journal of Proteome Research (article, 2026-03-26)
Field: Chemistry — Spectroscopy

What Does Next-Generation Mass Spectrometry Offer for Proteomics? A Comprehensive Platform Comparison Next-generation mass spectrometry platforms (Orbitrap Astral, timsTOF Ultra) are reshaping proteomics by enhancing analytical depth and sensitivity. We compared these platforms against Orbitrap Exploris 480 using neonatal mouse lung tissues from a bronchopulmonary dysplasia model (<i>n</i> = 12), employing four acquisition strategies: Exploris 480 DDA/DIA, Astral HR-DIA, and timsTOF Ultra DIA-PASEF. All platforms identified ∼4000 proteins in common, with 98% proteome coverage of data-dependent acquisition (DDA) identifications using data-independent (DIA) methods and 92% concordance between next-generation systems. Orbitrap Astral and timsTOF Ultra quantified >225,000 peptides and 13,000 proteins, representing ∼800% and ∼300% greater depth than Exploris 480 DDA, respectively. Furthermore, new-generation platforms cut recommended sample size by ∼66%. Enhanced proteome depth improved subcellular compartment annotations from 30% (DDA) to 66% (next-generation platforms) and reactome pathway coverage from 58% (DDA) to 90% (next-generation platforms). Differential expression analysis identified up to four times more phenotype-associated proteins in DIA data sets, enriched in mitochondrial, ribosomal, and extracellular components, with up to 44 enriched pathways. Importantly, proteins uniquely detected showed no functional annotation bias. These findings demonstrate that DIA acquisition on multivendor next-generation platforms provides superior proteome coverage and more complete systems biology assessment without introducing bias, enabling enhanced understanding of complex biological systems. Proteome ; Orbitrap ; Proteomics ; Mass spectrometry ; Quantitative proteomics ; Protein expression ; Protein subcellular localization prediction Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Machine Learning in Bioinformatics
Authors: Dominic G. Hoch | Michael W. Belford | Lilian R. Heil | Karl Mechtler | Manuel Matzinger
Source: Scientific Reports (article, 2026-03-21)
Field: Chemistry — Spectroscopy

Low-resolution FAIMS for increased peptide coverage in low-load and single-cell proteomics Field Asymmetric Ion Mobility Spectrometry (FAIMS) is an indispensable tool in single-cell and low-input proteomics. Here, we show that tuning FAIMS resolution via electrode temperature modulation improves sensitivity. We demonstrate that lowering FAIMS resolution broadens the compensation voltage window, thereby increasing ion transmission. This significantly improves ion counts, quantitative precision and peptide identifications by 25-34%. These results were mirrored when analyzing single cell samples. Ion-mobility spectrometry ; Peptide ; Resolution (logic) ; Ion ; Mass spectrometry ; Proteomics ; Peptide mapping ; Ion channel Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Ion-surface interactions and analysis
Authors: Jinghao Ma | Yabin Liu | Huan Xu | Xu Duan | Zhongshang Dai | Ying Guo | Huan Song | Yun Tan | Jian Chen
Source: iScience (article, 2026-03-16)
Field: Chemistry — Spectroscopy

Plasma proteomic profiling with machine learning identifies immunometabolic perturbations associated with smoking and smoking cessation Cigarette smoking is the leading modifiable risk factor for chronic obstructive pulmonary disease (COPD) and is associated with systemic molecular alterations linking exposure to disease. We profiled the plasma proteome (2,920 proteins) of 38,133 UK Biobank participants and applied machine-learning approaches to characterize smoking-associated alterations and develop risk models. The circulating proteome exhibited dose-dependent changes, with heavy smoking associated with prominent immunometabolic and inflammatory signatures. An 11-protein panel discriminated COPD with an AUC of 0.844, outperforming models based on smoking history alone and distinguishing individuals with higher versus lower susceptibility. Model-based analyses further highlighted a subgroup of heavy smokers-characterized by older age, higher body mass index, and leptin-skewed inflammatory profiles-showing molecular features associated with elevated COPD risk after smoking cessation. Together, these findings characterize systemic proteomic patterns associated with smoking exposure and support plasma-based stratification of COPD risk. Smoking cessation ; Profiling (computer programming) ; Precision medicine ; Proteomics ; MEDLINE ; Systems biology Advanced Proteomics Techniques and Applications ; Advanced Biosensing Techniques and Applications ; vaccines and immunoinformatics approaches
Authors: Hemant Sharma | Dina Sheyfer | Ross Harder | Jonathan Z. Tischler
Source: Journal of Applied Crystallography (article, 2026-03-02)
Field: Materials Science — Materials Chemistry

<i>LaueMatching</i> : an approach for rapid and robust indexing of Laue diffraction patterns Traditional Laue diffraction pattern indexing often struggles with noisy data, weak signals, peak overlap and missing reflections, particularly from complex or deformed microstructures. Here, we introduce <i>LaueMatching</i>, a high-throughput indexing algorithm designed to overcome these limitations. <i>LaueMatching</i> utilizes a fundamentally different approach based on direct pattern correlation: experimentally pre-processed images are compared against a comprehensive pre-computed library of simulated diffraction patterns corresponding to a dense grid of possible orientations. This approach bypasses the need for explicit peak identification and fitting, steps that are often a failure point for traditional methods. The algorithm rapidly and robustly indexes multiple crystallographic orientations and crystal systems simultaneously, even from challenging patterns. <i>LaueMatching</i>'s effectiveness and accuracy have been rigorously tested and validated on diverse experimental (Ni, Al, EuAl<sub>2</sub>O<sub>4</sub>) and simulated diffraction patterns, demonstrating high-fidelity orientation refinement. Code to implement this approach on both CPU and GPU resources can be downloaded from https://github.com/AdvancedPhotonSource/LaueMatching. Search engine indexing ; Diffraction ; Orientation (vector space) ; Point (geometry) ; Code (set theory) ; X-ray crystallography ; Grid X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications
Authors: Isabela Gnasso | Khadija Sarguroh | Dorian A. Gangloff | Sophia E. Economou | Edwin Barnes
Source: Physical review. B./Physical review. B (article, 2026-03-10)
Field: Materials Science — Materials Chemistry

Quantifying electron-nuclear spin entanglement dynamics in central-spin systems using one-tangles Optically-active solid-state systems such as self-assembled quantum dots, rare-earth ions, and color centers in diamond and SiC are promising candidates for quantum network, computing, and sensing applications. Although the nuclei in these systems naturally lead to electron spin decoherence, they can be repurposed, if they are controllable, as long-lived quantum memories. Prior work showed that a metric known as the one-tangling power can be used to quantify the entanglement dynamics of sparse systems of spin-1/2 nuclei coupled to color centers in diamond and SiC. Here, we generalize these findings to a wide range of electron-nuclear central-spin systems, including those with spin &gt; 1/2 nuclei, such as in III-V quantum dots (QDs), rare-earth ions, and some color centers. Focusing on the example of an (In)GaAs QD, we offer a procedure for pinpointing physically realistic parameter regimes that yield maximal entanglement between the central electron and surrounding nuclei. We further harness knowledge of naturally-occurring degeneracies and the tunability of the system to generate maximal entanglement between target subsets of spins when the QD electron is subject to dynamical decoupling. We also leverage the one-tangling power as an exact and immediate method for computing QD electron spin dephasing times with and without the application of spin echo sequences, and use our analysis to identify coherence-sustaining conditions within the system. Quantum entanglement ; Spins ; Dephasing ; Spin (aerodynamics) ; Quantum sensor ; Quantum dot ; Spin engineering ; Quantum ; Quantum system Diamond and Carbon-based Materials Research ; Quantum and electron transport phenomena ; Quantum optics and atomic interactions
Authors: Jiayu Liu | Xun Ma | Xinnuo Zhang | Wenchuan Jing | Zhengtai Liu | Dawei Shen
Source: Nano Convergence (article, 2026-02-11)
Field: Materials Science — Materials Chemistry

Symmetry-driven spin splitting in altermagnets: an angle-resolved photoemission spectroscopy perspective Altermagnetism arises from composite real-space and spin-space symmetries, combining zero net magnetization with pronounced momentum-dependent spin splitting. This review highlights the pivotal role of angle-resolved photoemission spectroscopy (ARPES)—along with its spin-resolved (SARPES) and circular-dichroism (CD-ARPES) variants, in directly visualizing the nonrelativistic band splitting and spin textures of altermagnets. Within the spin-group framework, we distinguish ferromagnetic, antiferromagnetic, and altermagnetic orders and elucidate the symmetry origin of spin polarization. We then systematically review representative systems: the debated $$d$$ -wave prototype RuO2, layered $$d$$ -wave altermagnets KV2Se2O and Rb1−δ V2Te2O, and a series of $$g$$ -wave compounds, including MnTe (domain-tunable) and CrSb (topological), together with the noncoplanar antiferromagnet MnTe2 and other emerging and prospective candidates and platforms. Overall, ARPES has become a key microscope for resolving symmetry-driven spin splitting. Future advances in micro/nano-beam and in-situ spectroscopies, combined with strain and domain engineering, heterostructure design, and the exploration of broader unconventional magnetic states, are expected to drive the joint evolution of altermagnetism and photoemission spectroscopy, paving the way for spintronic and correlated quantum research. Spintronics ; Angle-resolved photoemission spectroscopy ; Spin (aerodynamics) ; Antiferromagnetism ; Heterojunction ; Photoemission spectroscopy ; Spectroscopy ; Perspective (graphical) ; Quantum 2D Materials and Applications ; Topological Materials and Phenomena ; Advanced Condensed Matter Physics
Authors: Haocheng Tang | Xingyu Dang | Junmei Wang
Source: arXiv (Cornell University) (preprint, 2026-04-03)
Field: Materials Science — Materials Chemistry

Fine-tuning DeepSeek-OCR-2 for Molecular Structure Recognition Optical Chemical Structure Recognition (OCSR) is critical for converting 2D molecular diagrams from printed literature into machine-readable formats. While Vision-Language Models have shown promise in end-to-end OCR tasks, their direct application to OCSR remains challenging, and direct full-parameter supervised fine-tuning often fails. In this work, we adapt DeepSeek-OCR-2 for molecular optical recognition by formulating the task as image-conditioned SMILES generation. To overcome training instabilities, we propose a two-stage progressive supervised fine-tuning strategy: starting with parameter-efficient LoRA and transitioning to selective full-parameter fine-tuning with split learning rates. We train our model on a large-scale corpus combining synthetic renderings from PubChem and realistic patent images from USPTO-MOL to improve coverage and robustness. Our fine-tuned model, MolSeek-OCR, demonstrates competitive capabilities, achieving exact matching accuracies comparable to the best-performing image-to-sequence model. However, it remains inferior to state-of-the-art image-to-graph modelS. Furthermore, we explore reinforcement-style post-training and data-curation-based refinement, finding that they fail to improve the strict sequence-level fidelity required for exact SMILES matching. PubChem ; Task (project management) ; Matching (statistics) ; Training set ; Fidelity ; Pattern recognition (psychology) ; Key (lock) ; Optical character recognition Machine Learning in Materials Science ; Graph Theory and Algorithms ; Computational Drug Discovery Methods
Authors: Xinyu Yang | Bhawnath Tiwari | Gábor Molnár | Lionel Salmon | Azzedine Bousseksou
Source: Sensors and Actuators A Physical (article, 2026-04-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Fine-tuning the efficiency of spin crossover based soft actuators by chemical design Actuator ; Crossover ; Spin (aerodynamics) ; Spin crossover ; Design of experiments Magnetism in coordination complexes ; Magnetic properties of thin films ; Organic Light-Emitting Diodes Research
Authors: Mahindra Rautela | Alexander Most | Siddharth Mansingh | Aleksandra Pachalieva | Bradley Love | Daniel O Malley | Alexander Scheinker | Kyle Hickmann | Diane Oyen | Nathan Debardeleben | Earl Lawrence | Ayan Biswas
Source: arXiv (Cornell University) (article, 2026-03-04)
Field: Materials Science — Materials Chemistry

Out-of-distribution transfer of PDE foundation models to material dynamics under extreme loading Most PDE foundation models are pretrained and fine-tuned on fluid-centric benchmarks. Their utility under extreme-loading material dynamics remains unclear. We benchmark out-of-distribution transfer on two discontinuity-dominated regimes in which shocks, evolving interfaces, and fracture produce highly non-smooth fields: shock-driven multi-material interface dynamics (perturbed layered interface or PLI) and dynamic fracture/failure evolution (FRAC). We formulate the downstream task as terminal-state prediction, i.e., learning a long-horizon map that predicts the final state directly from the first snapshot without intermediate supervision. Using a unified training and evaluation protocol, we evaluate two open-source pretrained PDE foundation models, POSEIDON and MORPH, and compare fine-tuning from pretrained weights against training from scratch across training-set sizes to quantify sample efficiency under distribution shift. Transfer of learning ; Foundation (evidence) ; Scratch ; Benchmark (surveying) ; Snapshot (computer storage) ; Dynamics (music) ; Mathematical model Machine Learning in Materials Science ; Model Reduction and Neural Networks ; High-Velocity Impact and Material Behavior
Authors: Mahindra Rautela | Alexander Most | Siddharth Mansingh | Aleksandra Pachalieva | Bradley Love | Daniel O Malley | Alexander Scheinker | Kyle Hickmann | Diane Oyen | Nathan Debardeleben | Earl Lawrence | Ayan Biswas
Source: Open MIND (preprint, 2026-03-04)
Field: Materials Science — Materials Chemistry

Out-of-distribution transfer of PDE foundation models to material dynamics under extreme loading Most PDE foundation models are pretrained and fine-tuned on fluid-centric benchmarks. Their utility under extreme-loading material dynamics remains unclear. We benchmark out-of-distribution transfer on two discontinuity-dominated regimes in which shocks, evolving interfaces, and fracture produce highly non-smooth fields: shock-driven multi-material interface dynamics (perturbed layered interface or PLI) and dynamic fracture/failure evolution (FRAC). We formulate the downstream task as terminal-state prediction, i.e., learning a long-horizon map that predicts the final state directly from the first snapshot without intermediate supervision. Using a unified training and evaluation protocol, we evaluate two open-source pretrained PDE foundation models, POSEIDON and MORPH, and compare fine-tuning from pretrained weights against training from scratch across training-set sizes to quantify sample efficiency under distribution shift. Transfer of learning ; Foundation (evidence) ; Scratch ; Benchmark (surveying) ; Snapshot (computer storage) ; Dynamics (music) ; Mathematical model Machine Learning in Materials Science ; Model Reduction and Neural Networks ; High-Velocity Impact and Material Behavior