jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Fei Wang | Xinye Zheng | Kun Li | Yanyan Wei | Yuxin Liu | Ganpeng Hu | Tong Bao | Jingwen Yang
Source: arXiv (Cornell University) (preprint, 2026-03-13)
Field: Materials Science — Materials Chemistry

Multimodal Protein Language Models for Enzyme Kinetic Parameters: From Substrate Recognition to Conformational Adaptation Predicting enzyme kinetic parameters quantifies how efficiently an enzyme catalyzes a specific substrate under defined biochemical conditions. Canonical parameters such as the turnover number ($k_\text{cat}$), Michaelis constant ($K_\text{m}$), and inhibition constant ($K_\text{i}$) depend jointly on the enzyme sequence, the substrate chemistry, and the conformational adaptation of the active site during binding. Many learning pipelines simplify this process to a static compatibility problem between the enzyme and substrate, fusing their representations through shallow operations and regressing a single value. Such formulations overlook the staged nature of catalysis, which involves both substrate recognition and conformational adaptation. In this regard, we reformulate kinetic prediction as a staged multimodal conditional modeling problem and introduce the Enzyme-Reaction Bridging Adapter (ERBA), which injects cross-modal information via fine-tuning into Protein Language Models (PLMs) while preserving their biochemical priors. ERBA performs conditioning in two stages: Molecular Recognition Cross-Attention (MRCA) first injects substrate information into the enzyme representation to capture specificity; Geometry-aware Mixture-of-Experts (G-MoE) then integrates active-site structure and routes samples to pocket-specialized experts to reflect induced fit. To maintain semantic fidelity, Enzyme-Substrate Distribution Alignment (ESDA) enforces distributional consistency within the PLM manifold in a reproducing kernel Hilbert space. Experiments across three kinetic endpoints and multiple PLM backbones, ERBA delivers consistent gains and stronger out-of-distribution performance compared with sequence-only and shallow-fusion baselines, offering a biologically grounded route to scalable kinetic prediction and a foundation for adding cofactors, mutations, and time-resolved structural cues. Scalability ; Substrate (aquarium) ; Representation (politics) ; A priori and a posteriori ; Protein structure ; Enzyme ; Michaelis–Menten kinetics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: J. Ratajski | Błażej Bałasz | Agnieszka Peła | Paweł Krupski | Kamil Bochenek | Łukasz Major
Source: Archives of Civil and Mechanical Engineering (article, 2026-04-08)
Field: Materials Science — Materials Chemistry

Tailoring phase transformation behavior and functional properties of LPBF-fabricated NiTi alloys via post-processing heat treatment Nickel titanium ; Shape-memory alloy ; Differential scanning calorimetry ; Austenite ; Microstructure ; Annealing (glass) ; Diffusionless transformation ; Martensite Shape Memory Alloy Transformations ; High Entropy Alloys Studies ; Musicians’ Health and Performance
Authors: Chao Shang | Gregory D. Fuchs
Source: ArXiv.org (article, 2026-03-14)
Field: Materials Science — Materials Chemistry

NVRNet: Deep Learning Model for Fast Nitrogen Vacancy Characterization under Room Temperature Characterization of the local spin environment of single diamond nitrogen-vacancy centers is a critical task for quantum sensing, quantum networking, and diamond materials optimization. We introduce NVRNet, a physics-informed simulation-to-reality pipeline that maps a fast acquisition, noisy Ramsey photoluminescence (PL) trace to a denoised waveform as well as outputting a direct estimate of hyperfine coupling to ${}^{13}\mathrm{C}$ spins in the environment. The denoiser is a two-stage time-frequency UNet followed by an attention-augmented time-domain UNet, pretrained on Hamiltonian-based simulations with experimentally calibrated noise. The simulation-pretrained, experimentally fine-tuned denoiser reduces the median reconstruction error on held-out few-sweep experimental traces to $0.44$-$0.67\times$ that of the raw experimental noisy traces across the three NV centers. A transformer-based estimator trained on simulation labels then predicts hyperfine parameters, and forward reconstruction from the inferred parameters reproduces the dominant experimental time- and frequency-domain features, with representative normalized FFT reconstruction errors of 0.10-0.19. These results establish NVRNet as a fast, hardware-compatible route to hyperfine inference from minimal Ramsey data. Hyperfine structure ; Spins ; Estimator ; Deep learning ; Diamond ; Characterization (materials science) ; Waveform ; Pipeline (software) Diamond and Carbon-based Materials Research ; Magnetic properties of thin films ; Electronic and Structural Properties of Oxides
Authors: Ran Xu | Xiangyu Miao | Shuo Yang | Shoubin Wang | T. Ma | Alejandro J. Müller | Zhibo Li
Source: Macromolecules (article, 2026-02-13)
Field: Materials Science — Polymers and Plastics

Stiff Yet Elastic Thermoplastic Polyurethanes Based on Nanoconfined Stereocomplexation in PLA Interphases Stiffness and elasticity are generally considered mutually exclusive properties in thermoplastic polyurethanes (TPUs), as embodied by the counterbalance between hard and soft content. In this work, we report the design strategy of a family of stereocomplexed poly(lactide) (scPLA)-containing TPUs, where short strands of the two PLA stereoisomers (PLLA and PDLA) are covalently introduced into the chain architecture in order to achieve elastomers with simultaneously high moduli, toughness, elasticity, and biomass incorporation. Through fine-tuning of segment lengths and ratios, we identified an optimal composition in which the resulting TPU exhibits an extraordinary Young’s modulus of ∼100 MPa and a heat resistance of 150 °C, while only marginally compromising toughness and elasticity. X-ray diffraction (XRD) and atomic force microscopy (AFM) confirm that scPLA forms exclusively in the stereocomplexed TPU, giving rise to a unique bicontinuous microphase-separated structure that is heavily loaded with scPLA nanofibrils. These “intrinsic fillers” are not only key to superior mechanical and thermal characteristics in our materials design but also highlight the potential of scPLA as an essential building block toward the ultimate goal of developing fully biobased, high-performance TPU products. Toughness ; Thermoplastic polyurethane ; Thermoplastic ; Thermoplastic elastomer ; Elastomer ; Elasticity (physics) ; Stiffness ; Polymer ; Elastic modulus Polymer composites and self-healing ; biodegradable polymer synthesis and properties ; Carbon dioxide utilization in catalysis
Authors: Ramanathan Ramkumar | R. Anbazhagan | John Bosco Balaguru Rayappan
Source: Journal of Materials Science (article, 2026-03-14)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Metasurface biosensors across microwave and optical regimes-electromagnetic design, fabrication, and biosensing applications Label-free detection of biomarkers has garnered considerable research interest, particularly with the advent of advanced sensing platforms, like metasurfaces. Metasurfaces are two-dimensional metamaterials consisting of an array of metal–metal or metal–dielectric-based structures with fine-tuned shapes and sizes at the nanoscale (∼10−9 m). This paper aims to discuss current knowledge and several recent advancements in metasurface biosensing such as reconfigurable metasurface, quasi-BIC, ML-assisted metasurface design, and microfluidic integration of sample movement for real-time monitoring applications at microwave and optical frequencies. The principle behind the operation of the metasurface biosensor is based on the change in dielectric constant at microwave frequencies and refractive index or surface plasmon resonance at optical frequencies for the biomolecular substance. Based on the parametric change in the corresponding frequencies, the biosensor detects abnormalities in the biological substance. In this review, the fundamental design, functional materials that interact with electromagnetic waves, dynamics in sensing mechanism linking field confinement, modal perturbation, resonance shift phenomenon, fabrication techniques and their potential challenges, roadmap to future advancements, and real-time challenges in biosensing applications for microwave and optical frequencies are discussed. Biosensor ; Microwave ; Microwave applications ; Optical sensing ; Polymer Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Advanced Antenna and Metasurface Technologies
Authors: Eleonora G. Hochreiner | Neshat Moslehi | Anne Floor den Ouden | Remco Tuinier | Bence Fehér | Tessa V. M. Bogaardt | Ilja K. Voets | C. van Nostrum | Tina Vermonden | Bas G. P. van Ravensteijn
Source: Macromolecules (article, 2026-03-05)
Field: Materials Science — Materials Chemistry

Polymerization-Induced Self-Assembly versus Nanoprecipitation: A Systematic Comparison of Block Copolymer Micelle Properties Amphiphilic block copolymers (BCPs) can assemble into a wide range of kinetically (meta)stable, nonequilibrium nanostructures, yet how distinct assembly pathways determine micellar morphology and kinetic trapping remains poorly understood. Here, we present a systematic experimental–theoretical comparison of three fundamentally different aqueous assembly strategies, namely, nanoprecipitation via solvent exchange with N,N-dimethylformamide (DMF), solvent evaporation using acetone (ACE), and polymerization-induced self-assembly (PISA) of monomethoxy poly(ethylene glycol)90-block-poly(diacetone acrylamide)x (mPEG90-b-p(DAAm)x) with x = 30, 50, 100, and 200. An extensive physicochemical analysis combining transmission electron microscopy (TEM), dynamic light scattering (DLS), asymmetric flow field-flow fractionation (AF4), and detailed small-angle X-ray scattering (SAXS) profile fitting provides direct evidence for pathway-dependent micellar size, aggregation number, polyethylene glycol (PEG) packing density, and the coexistence of mixed spheroidal and worm-like morphologies. Whereas PISA produces exclusively spheroidal micelles, both nanoprecipitation routes yield persistent morphological phase coexistence across all block ratios. Direct comparison with self-consistent field (SCF) theory predictions of (near-)equilibrium micellar morphologies reveals that kinetic trapping is ubiquitous and most pronounced for PISA-derived particles. Notably, despite stronger kinetic trapping, PISA exhibits higher reproducibility than nanoprecipitation-based assemblies. These findings demonstrate that controlled kinetic trapping and assembly pathways can be rationally exploited to fine-tune micellar nanostructures for advanced applications such as drug delivery. Copolymer ; Micelle ; Block (permutation group theory) ; Polymer ; Chemical solution Block Copolymer Self-Assembly ; Advanced Polymer Synthesis and Characterization ; Synthesis and Properties of Aromatic Compounds
Authors: Poonam Bhadoria | Manoj Kumar Gangwar | Vikas Yadav
Source: Research Square (preprint, 2026-03-30)
Field: Chemistry — Organic Chemistry

Computational Elucidation of Electronic Structure and Noncovalent Interactions in NHC-Supported Palladium-PEPPSI Complexes for Acceptorless Alcohol Dehydrogenation Electronic structure ; Natural bond orbital ; Intermolecular force ; Density functional theory ; Electrophile ; Palladium ; Covalent bond ; Population ; Mulliken population analysis ; Non-covalent interactions N-Heterocyclic Carbenes in Organic and Inorganic Chemistry ; Asymmetric Hydrogenation and Catalysis ; Catalysis for Biomass Conversion
Authors: Kewei Zhu | Cameron Wilson | Bartosz Mazur | Yi Li | Ashleigh M. Chester | Peyman Z. Moghadam
Source: arXiv (Cornell University) (preprint, 2026-04-12)
Field: Materials Science — Materials Chemistry

ReadMOF: Structure-Free Semantic Embeddings from Systematic MOF Nomenclature for Machine Learning Systematic chemical names, such as IUPAC-style nomenclature for metal-organic frameworks (MOFs), contain rich structural and compositional information in a standardized textual format. Here we introduce ReadMOF, which is, to our knowledge, the first nomenclature-free machine learning framework that leverages these names to model structure-property relationships without requiring atomic coordinates or connectivity graphs. By employing pretrained language models, ReadMOF converts systematic MOF names from the Cambridge Structural Database (CSD) into vector embeddings that closely represent traditional structure-based descriptors. These embeddings enable applications in materials informatics, including property prediction, similarity retrieval, and clustering, with performance comparable to geometry-dependent methods. When combined with large language models, ReadMOF also establishes chemically meaningful reasoning ability with textual input only. Our results show that structured chemical language, interpreted through modern natural language processing techniques, can provide a scalable, interpretable, and geometry-independent alternative to conventional molecular representations. This approach opens new opportunities for language-driven discovery in materials science. Nomenclature ; Property (philosophy) ; Natural language ; Similarity (geometry) ; Support vector machine ; Training set ; Semantics (computer science) Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; Computational Drug Discovery Methods
Authors: junting sheng | Yuying Guan | jiale wang | Xu Liu | Zhenghao Xu | Yu Chen | Fuqing Wu | Guoqiang Chen
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Biomaterials

Modulating ilvA Encoding Threonine Deaminase for Balanced Growth and PHB Synthesis by Halomonas Grown in Rich Nitrogen Source Halomonas ; Psychological repression ; Threonine ; Gene ; Biomass (ecology) ; Cell growth ; De novo synthesis ; Catabolite repression biodegradable polymer synthesis and properties ; Microplastics and Plastic Pollution ; Microbial Metabolic Engineering and Bioproduction
Authors: Ashwini Dilip Dhumale | Devendra Parganiha | Shilpi Bhardwaj | Raushan Kumar Jha | Anushri Arun Pawar | Sangit Kumar
Source: Chemistry - An Asian Journal (article, 2026-01-01)
Field: Chemistry — Organic Chemistry

Monodentate Transient Directing Group Enabled Distal C─H Bond Functionalization in Ferrocenecarboxaldehyde Heteroannular palladium migration on stacked molecules is one of the keys for achieving distal C-H activation by avoiding the template design. However, the possibility of migration is only realized when a relatively stable palladacycle is formed at a distal position with the aid of a transient directing group. Herein, we have revealed that heteroannular palladium migration facilitated by a monodentate transient directing group, along with an additional ligand, can help to fine-tune the desired bite angle for palladium migration. The developed reaction effectively demonstrates 23 examples of distal heteroannular C-H functionalization of ferrocenecarboxaldehydes, a process that has been less extensively studied compared to the homoannular cyclopentadienyl (Cp) ring C-H activation. Furthermore, the control experiments have been performed to understand monodentate transient directing group (TDG)-enabled heteroannular palladium migration, favored by the monodentate TDG and ligand. Palladium ; Denticity ; Surface modification ; Cyclopentadienyl complex ; Transient (computer programming) Catalytic C–H Functionalization Methods ; Catalytic Cross-Coupling Reactions ; Catalytic Alkyne Reactions
Authors: J Wang | Cui Cui Wang | Say Kee Ong
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Chemistry — Inorganic Chemistry

Halogen-Engineered Zinc(II) Complexes Based on a Semicarbazone Schiff Base: Correlating Molecular Structure with Enhanced Thermal Stability and Tunable Luminescence Triclinic crystal system ; Halogen ; Thermal stability ; Hydrogen bond ; Schiff base ; Molecule ; Halogen bond ; Semicarbazone Metal-Organic Frameworks: Synthesis and Applications ; Crystallography and molecular interactions ; Luminescence and Fluorescent Materials
Authors: Zhihang Zhou | Qixiang Wang | Yufeng Li | Wenpeng Xing
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

CMeval: Safety Assessment for Chemical Scenarios of Small Language Models Benchmark (surveying) ; Chemical safety ; Cover (algebra) ; Work (physics) ; Risk assessment ; Poison control Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Artificial Intelligence in Healthcare and Education
Authors: Siqi Chen | Zhiqiang Wang | Yili Shen | Xianqi Deng | Xi Cheng | Cheng-Wei Ju | Jun Yi | Guo Ling | Dieaa Alhmoud | Hui Guan | Zhou Lin
Source: arXiv (Cornell University) (preprint, 2026-04-10)
Field: Materials Science — Materials Chemistry

Transferable FB-GNN-MBE Framework for Potential Energy Surfaces: Data-Adaptive Transfer Learning in Deep Learned Many-Body Expansion Theory Mechanistic understanding and rational design of complex chemical systems depend on fast and accurate predictions of electronic structures beyond individual building blocks. However, if the system exceeds hundreds of atoms, first-principles quantum mechanical (QM) modeling becomes impractical. In this study, we developed FB-GNN-MBE by integrating a fragment-based graph neural network (FB-GNN) into the many-body expansion (MBE) theory and demonstrated its capacity to reproduce first-principles potential energy surfaces (PES) for hierarchically structured systems with manageable accuracy, complexity, and interpretability. Specifically, we divided the entire system into basic building blocks (fragments), evaluated their one-fragment energies using a QM model, and addressed many-fragment interactions using the structure-property relationships trained by FB-GNNs. Our investigation shows that FB-GNN-MBE achieves chemical accuracy in predicting two-body (2B) and three-body (3B) energies across water, phenol, and mixture benchmarks, as well as the one-dimensional dissociation curves of water and phenol dimers. To transfer the success of FB-GNN-MBE across various systems with minimal computational costs and data demands, we developed and validated a teacher-student learning protocol. A heavy-weight FB-GNN trained on a mixed-density water cluster ensemble (teacher) distills its learned knowledge and passes it to a light-weight GNN (student), which is later fine-tuned on a uniform-density (H2O)21 cluster ensemble. This transfer learning strategy resulted in efficient and accurate prediction of 2B and 3B energies for variously sized water clusters without retraining. Our transferable FB-GNN-MBE framework outperformed conventional non-FB-GNN-based models and showed high practicality for large-scale molecular simulations. Complex system ; Artificial neural network ; Dissociation (chemistry) ; Transfer of learning ; Cluster (spacecraft) ; Quantum chemical ; Energy transfer ; Graph Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Crystallography and molecular interactions
Authors: Zipeng Sun | Can Chen | Ye Yuan | Haolun Wu | Jiayao Gu | Christopher Pal | Xue Nan Liu
Source: arXiv (Cornell University) (preprint, 2026-03-18)
Field: Materials Science — Materials Chemistry

Training Diffusion Language Models for Black-Box Optimization We study offline black-box optimization (BBO), aiming to discover improved designs from an offline dataset of designs and labels, a problem common in robotics, DNA, and materials science with limited labeled samples. While recent work applies autoregressive LLMs to BBO by formatting tasks as natural-language prompts, their left-to-right design generation struggles to capture the strong bidirectional dependencies inherent in design problems. To address this, we propose adapting diffusion LLMs to offline BBO to leverage their bidirectional modeling capabilities. However, a domain gap exists between the natural text pre-training of diffusion LLMs and the heterogeneous signals in BBO (prompts, designs, and labels). To bridge this gap, we construct a unified prompt-response corpus and introduce delimiter tokens to explicitly mark field boundaries for domain adaptation. We further propose a two-stage post-training framework to align the diffusion LLM generation with high-label designs. The first stage performs supervised fine-tuning on the unified dataset via masked-response prediction, and the second stage adopts reinforcement learning with rewards defined by label improvements. Our method achieves state-of-the-art results on Design-Bench small-data settings. Leverage (statistics) ; Domain (mathematical analysis) ; Domain adaptation ; Construct (python library) ; Field (mathematics) ; Bridge (graph theory) Machine Learning in Materials Science ; Advanced Multi-Objective Optimization Algorithms ; Generative Adversarial Networks and Image Synthesis
Authors: Jia Luo | Yanhong Fan | Yanchao Zhao | Jiajie Wu | Yaqi Gu | Haoyu Song | Hua Liang | Shuangke Wu | Zhuang Miao | Lu Qiu | Xing Jiang | Akshay Rao | Zhilong Zhang
Source: The Journal of Physical Chemistry Letters (article, 2026-02-23)
Field: Materials Science — Materials Chemistry

Alloyed Quantum Dots with Tuned Exciton Wave Function Delocalization for Enhanced Energy Transfer Efficient exciton energy transfer from photoexcited quantum dots (QDs) is the key to achieving high performance across diverse applications. Exciton wave function delocalization favors energy transfer, yet fine-tuning the degree of delocalization remains challenging. Here, we demonstrate the modulation of exciton delocalization by engineering the exciton Bohr radius via tellurium doping in lead selenide (PbSe<sub>1-<i>x</i></sub>Te<sub><i>x</i></sub>) alloy QDs. Using a QD-sensitized triplet-triplet annihilation upconversion system as a testing platform, we show that Te doping enhances energy transfer and boosts upconversion efficiency by up to 50-fold, before the poor stability of PbTe dominates at higher Te content. This doping strategy is also compatible with other optimizations, as shortening the surface ligands of PbSe<sub>1-<i>x</i></sub>Te<sub><i>x</i></sub> QDs further enhances energy transfer and upconversion performance. Through femtosecond transient absorption microscopy, we further confirm that optimized Te doping accelerates early stage exciton transfer dynamics in QD thin films, indicating larger exciton delocalization. This strategy provides a versatile material-design route to optimize energy transfer in QD-based material systems. Delocalized electron ; Exciton ; Quantum dot ; Bohr radius ; Photon upconversion ; Biexciton ; Doping ; Femtosecond Quantum Dots Synthesis And Properties ; Semiconductor Quantum Structures and Devices ; Chemical and Physical Properties of Materials
Authors: Said Nawab | Hareef Ahmed Keerio | Xiangfei Li | Yu Chen | Chuanchao Wu | Hu Zhang | Rumeng Han | Huili Wang | Yan LIU
Source: Biotechnology Advances (article, 2026-02-28)
Field: Materials Science — Biomaterials

Engineering Escherichia coli for high-level poly(3-hydroxybutyrate) production: Recent advances and future perspective Synthetic biology ; Metabolic engineering ; Escherichia coli ; Bioplastic ; Genetically engineered ; Protein engineering biodegradable polymer synthesis and properties ; Microbial Metabolic Engineering and Bioproduction ; Biochemical Acid Research Studies
Authors: A.S. Prikhodko | N.I. Borgardt
Source: Micron (article, 2026-02-02)
Field: Materials Science — Materials Chemistry

Accurate interlayer distance measurement in bilayer graphene on SiC by high-resolution electron microscopy data analysis Phase (matter) ; Graphene ; Bilayer ; Transmission electron microscopy ; Delocalized electron ; Length measurement ; Accuracy and precision ; Molecular dynamics Graphene research and applications ; Advanced Physical and Chemical Molecular Interactions ; Surface and Thin Film Phenomena