jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Aparecida Miranda Corrêa | Jean Michel Moura-Bueno | Carlos Augusto Marconato | Micael da Silva Santos | Carina Marchezan | Douglas Luiz Grando | Adriele Tassinari | William Natale | Danilo Eduardo Rozane | Gustavo Brunetto
Source: Horticulturae (article, 2026-01-19)
Field: Chemistry — Analytical Chemistry

Combining Machine Learning and Vis-NIR Spectroscopy to Estimate Nutrients in Fruit Tree Leaves Traditional chemical analysis of plant tissue is time-consuming, costly, and poses risks due to exposure to toxic gases, highlighting the need for faster, low-cost, and safer alternatives. Vis-NIR spectroscopy, combined with machine learning, offers a promising method for estimating leaf nutrient levels without chemical reagents. This study evaluated the potential of Vis-NIR spectroscopy for nutrient estimation in leaf samples of banana (n = 363), mango (n = 239), and grapevine (n = 336) by applying spectral pre-processing techniques—smoothing (SMO) and first derivative Savitzky–Golay (SGD1d) alongside two machine learning methods: Partial Least Squares Regression (PLSR) and Random Forest (RF). Plant tissue samples were analyzed using sulfuric and nitroperchloric wet digestion and hyperspectral sensors. The prediction models were assessed using concordance correlation coefficient (CCC) and mean squared error (MSE). The highest accuracy (CCC &gt; 0.80 and MSE &lt; 2 g kg−1) was achieved for Ca in banana, P in mango, and N and Ca in grapevine across both machine learning methods and pre-processing techniques. The predictive models calibrated for ‘Grapevine’ exhibited the highest accuracy—characterized by higher CCC values and lower MSE values—when compared with the models developed for ‘Mango’ and ‘Banana’. Models using SMO and SGD1d showed better performance than those using raw spectra (RAW). The high amplitudes and variations in nutrient levels, combined with large standard deviations, negatively affected the predictive performance of the models. Partial least squares regression ; Nutrient ; Mean squared error ; Random forest ; Hyperspectral imaging ; Coefficient of determination ; Linear regression Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Banana Cultivation and Research
Authors: Helder R.O. Rocha | Sara Abou Dargham | Jimmy Romanos | Wesley Costa | Roy Roukos | Jair A.L. Silva | Heinrich Wörtche
Source: Energy Conversion and Management X (article, 2026-01-21)
Field: Chemistry — Inorganic Chemistry

AI-driven optimization approaches of metal–organic frameworks for enhanced methane delivery Methane, the primary component of natural gas, emits less carbon dioxide than other petroleum-based fuels but faces challenges in efficient storage and transportation. Advanced adsorption materials provide a safe and cost-effective solution, with metal–organic frameworks (MOFs) emerging as promising candidates for natural gas storage and delivery in vehicles. This research employed AI-Driven Optimization (AiDO) to identify optimal parameters for enhancing methane uptake while simultaneously improving both gravimetric and volumetric delivery. We developed and validated three machine learning models: eXtreme Gradient Boosting (XGBoost), Kolmogorov–Arnold Network (KAN), and Convolutional Neural Network (CNN), using experimental data. All models demonstrated strong predictive performance, with XGBoost achieving outstanding results, including a Root Mean Squared Error (RMSE) of 0.0103 and a coefficient of determination ( R 2 ) of 0.9722. When integrated into an optimization framework, the XGBoost model identified optimal conditions for methane delivery, predicting a room temperature gravimetric delivery of 724.14 cm 3 /g, and a volumetric delivery of 602.21 cm 3 /cm 3 from 65 to 5 bar. Sensitivity analysis validated the robustness of the AiDO methodology, highlighting its potential to effectively reduce costs and enhance the performance of porous MOFs. • Three AI models developed and validated for predicting methane uptake and delivery in porous MOFs. • XGBoost demonstrated the highest predictive performance (RMSE = 0.0103, R 2 = 0.9722). • AI-Driven Optimization (AiDO) framework identified optimal gravimetric and volumetric methane delivery conditions. • Methodology is robust, scalable, and extendable to other gas storage system. Methane ; Gravimetric analysis ; Natural gas ; Robustness (evolution) ; Artificial neural network ; Natural gas storage ; Support vector machine ; Porosity Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Carbon Dioxide Capture Technologies
Authors: Reese Jones | Adrián Buganza Tepole | Jan N. Fuhg
Source: Journal of Computational Physics (article, 2026-01-14)
Field: Materials Science — Materials Chemistry

Differentiable neural network representation of multi-well, locally-convex potentials Convexity ; Differentiable function ; Inference ; Artificial neural network ; Representation (politics) ; Feature (linguistics) ; Regular polygon ; Convex optimization Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Quantum many-body systems
Authors: Sarika Joshi | Ankit Dodla | Sarthak Saxena | Twinkle Soni | Sumit Saxena | Shobha Shukla | Bayden R. Wood
Source: Journal of Raman Spectroscopy (article, 2026-04-08)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Bioanalytical TERS and Advanced Data Processing Methods ABSTRACT Tip‐enhanced Raman spectroscopy (TERS) enables vibrational imaging with subnanometer spatial resolution, offering label‐free chemical insight into biological structures at the single‐molecule level. This review outlines recent developments in TERS instrumentation, including tip fabrication strategies, excitation geometries, and configurations compatible with ambient and liquid‐phase environments. Emphasis is placed on biological applications, where TERS has been used to probe proteins, nucleic acids, lipid assemblies, and viral particles. These studies demonstrate how localized plasmonic enhancement supports surface‐selective detection and enables mapping of molecular heterogeneity across cellular interfaces and membrane domains. Spatial resolution metrics, both lateral and vertical, are discussed in relation to tip–sample coupling, field confinement, and operational conditions. We also examine methodological challenges such as signal instability, tip degradation, thermal effects, and reproducibility, alongside emerging strategies for mitigation. In parallel, we highlight the role of advanced data analysis frameworks, including multivariate chemometric methods and machine learning approaches, which are increasingly essential for interpreting hyperspectral datasets and extracting relevant insights. Together, these advances position TERS as a versatile platform for nanoscale bioanalysis, with growing relevance for mechanistic studies, molecular diagnostics, and real‐time tracking of biochemical processes. Hyperspectral imaging ; Raman spectroscopy ; Chemical imaging ; Field (mathematics) ; Bioanalysis ; Plasmon ; Image resolution ; Signal processing Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Near-Field Optical Microscopy
Authors: Malte Grunert | Max Großmann | Erich Runge
Source: Applied Physics Letters (article, 2026-02-02)
Field: Materials Science — Materials Chemistry

Optical spectra prediction using three-body information Obtaining accurate optical spectra is equally necessary for a fundamental physical understanding of semiconductors on the one hand and their technological and economic utilization on the other hand, as well as for many research questions in between, but it remains a challenge both experimentally and theoretically. Machine learning (ML) models present a promising alternative. Recently, the first working ML models became available; however, each one comes with its own significant limitations. In this study, we present OptiMate3B, a ML model that incorporates the advances made separately by the first wave of models. OptiMate3B, a graph attention network utilizing three-body information, is able to predict the frequency-dependent complex dielectric function under the independent particle approximation and the random-phase approximation to quantitative accuracy for a wide range of crystalline semiconductors and insulators within milliseconds. It works particularly well for compounds containing elements that are commonly used for optical and optoelectronic applications. An easy-to-use interface for OptiMate3B significantly lowers the barrier to entry for the broad community. Semiconductor ; Dielectric ; Optical spectra ; Spectral line ; Range (aeronautics) ; Dielectric function ; Graph ; Function (biology) Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions ; X-ray Diffraction in Crystallography
Authors: Vivek Kumar | Abhishek Jangid | Manas Sharma | Manvi Verma | Jampala Pasyanthi | K. Shiv Kumar | Piyush Sindhu Sharma | Emil Chiglintsev | Mikhail I. Panin | Sudeep N. Punnathanam | Alexander I. Chernov | Ananth Govind Rajan | Akshay Singh
Source: Advanced Materials (article, 2026-03-29)
Field: Materials Science — Materials Chemistry

Tailored Vapor Deposition Unlocks Large‐Grain, Wafer‐Scale Epitaxial Growth of 2D Magnetic CrCl <sub>3</sub> Two-dimensional magnetic materials (2D-MM) are an exciting playground for fundamental research, and for spintronics and quantum sensing. However, their large-grain, wafer-scale synthesis using scalable vapor deposition methods is still an unsolved challenge. Here, a tailored physical vapor transport deposition (PVTD) method is developed, which enables centimeter-scale, epitaxial growth of semiconducting 2D-MM CrCl<sub>3</sub> at 500°C (on mica substrate). A controlled synthesis protocol, enabled via four process innovations, (i) low emissivity secondary heating source, (ii) very-high carrier-gas flow, (iii) dynamic precursor flux control, and (iv) oxygen/moisture removal, suppresses redox etching and drives growth beyond the diffusion limit for wafer-scale growth. Optical, stoichiometric, structural, and magnetic characterization confirm single-crystalline, phase-pure 2D-MM CrCl<sub>3</sub>. Substrate temperature tunes thickness of films from few-layers to tens of nanometers, while flow rate controls nucleation density and coverage. Further, we demonstrate selective-area growth and large-area transfer, validating potential wafer-level device integration. Substrate-dependent growth features are explained using density functional theory and state-of-the-art machine learning interatomic potential-based atomic-scale simulations. This scalable, flexible vapor deposition approach offers a general route for synthesizing several (volatile and reactive) 2D-MM and bridges the scalability gap from conventional wafer-scale materials. The low-temperature growth will enable the creation of hybrid functional heterostructures. Nucleation ; Epitaxy ; Chemical vapor deposition ; Physical vapor deposition ; Deposition (geology) ; Etching (microfabrication) ; Substrate (aquarium) ; Spintronics ; Mica 2D Materials and Applications ; Electronic and Structural Properties of Oxides ; Advanced Photocatalysis Techniques
Authors: Xing Ren | Z. Liu | Peng Zhang
Source: Expert Opinion on Drug Discovery (article, 2026-01-02)
Field: Chemistry — Spectroscopy

Small open reading frame-encoded peptides (SEPs) as hidden treasures: a review The revolutionary advances in high-throughput omics technologies - particularly mass spectrometry and ribosome profiling - combined with computational prediction methods such as machine learning, have enabled the discovery of an increasing number of SEPs. Research on SEP is currently in a phase of rapid development, and this suggests that the field of peptide drugs may gain many promising molecular candidate. Profiling (computer programming) ; Ribosome ; Drug discovery ; Reading (process) ; Ribosome profiling ; Peptide Advanced Proteomics Techniques and Applications ; Chemical Synthesis and Analysis ; Machine Learning in Bioinformatics
Authors: Peng Ren | Wei Feng | Zhaoyang Qin | Tianqiang Cui | Xun Zhang | Nan Luo | Bolun Huang | Qi Wang | Hua Geng | Chengshan Yuan | Xiangfeng Shao | Lang Jiang | Hao-Li Zhang | Zitong Liu
Source: Advanced Materials (article, 2026-03-27)
Field: Chemistry — Organic Chemistry

Axially Chiral Semiconducting Polymers Enabling NIR Circularly Polarized Light‐Sensing Phototransistors and Neuromorphic Synapses Waveplate-free detection of near-infrared (NIR) circularly polarized light (CPL) has been limited by the absence of materials that simultaneously exhibit strong NIR circular dichroism and efficient charge transport. Here, we present a family of axially chiral conjugated polymers, S- and R-pDPP4TBN-X, designed by grafting S/R-6,6'-dimethoxy-1,1'-binaphthyl (S/R-MeBN) onto a diketopyrrolopyrrole (DPP)-bithiophene backbone. By tuning the MeBN: DPP ratio, the polymers exhibit extended absorption up to ∼1000 nm, enhanced circular dichroism in the 500-870 nm region, and optimized thin-film morphology for charge mobility. Organic phototransistors based on S/R-pDPP4TBN-10 achieve hole mobilities up to 0.13 cm<sup>2</sup> V<sup>-1</sup> s<sup>-1</sup>, responsivities as high as 12.52 A W<sup>-1</sup>, detectivities on the order of 10<sup>11</sup> Jones, and dissymmetry factors (g<sub>Iph</sub>) of 0.30 and -0.34 under 808 nm CPL, enabling unambiguous discrimination of left- and right-handed light. Beyond static sensing, these devices emulate synaptic plasticity under CPL stimulation and, when coupled with an artificial neural network, deliver classification accuracies exceeding 95%. This work establishes a modular design paradigm for compact, NIR-active CPL sensors that integrate high charge transport, pronounced chiroptical response, and neuromorphic function, paving the way for photonic encryption, wearable optoelectronics, and bioinspired computing platforms. Neuromorphic engineering ; Circular dichroism ; Photonics ; Circular polarization ; Polymer ; Axial symmetry ; Absorption (acoustics) Synthesis and Properties of Aromatic Compounds ; Organic Electronics and Photovoltaics ; Photochromic and Fluorescence Chemistry
Authors: E. R. Smith
Source: The Journal of Chemical Physics (article, 2026-02-18)
Field: Materials Science — Materials Chemistry

The quantum method of planes-local pressure definitions for machine learning potentials Stress, or pressure, is a central quantity in engineering and remains vital in molecular modeling. However, the commonly used virial stress tensor is invalid for an inhomogeneous fluid, which is essential in fluid dynamics and non-equilibrium molecular dynamics (NEMD) simulations. This is solved by using the method of planes (MoP), a mechanical form of pressure, simply interpreted as the force divided by area, yet it is derived from the firm foundations of statistical mechanics. We present an extension of MoP stress [B. D. Todd et al., Phys. Rev. E 52, 1627-1638 (1995)] to the MACE potential, a particular form of machine learning (ML) potential allowing the incorporation of quantum mechanical physics into classical simulation. We present the derivation of this local stress for the MACE potential using the theoretical framework set out by Irving and Kirkwood [J. Chem. Phys. 18(6), 817-829 (1950)]. For the test case of an interface between water and zirconium oxide, we show that the MoP measures the correct force balance while the virial form fails. Furthermore, we demonstrate that this planar definition of stress is valid arbitrarily far from equilibrium, showing exact conservation every time step in a control volume bounded by MoP. This links the stress directly to the conservation equations and demonstrates the validity in non-equilibrium molecular dynamics systems. All code to reproduce these validations for any MACE system, together with ASE accelerated code to calculate the MoP, is provided as open source. This work helps build the foundation to extend the ML revolution in materials to NEMD and molecular fluid dynamics modeling. Virial theorem ; Quantum ; Cauchy stress tensor ; Stress (linguistics) ; Bounded function ; Tensor (intrinsic definition) ; Molecular dynamics Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Advanced Physical and Chemical Molecular Interactions
Authors: Bart Klumpers | Emiel J. M. Hensen | Ivo A. W. Filot
Source: AI for Science (article, 2026-03-17)
Field: Materials Science — Materials Chemistry

Neural-network analysis of CO adlayer structures and lateral interactions on transition metal surfaces Under realistic operating conditions, heterogeneously catalyzed reaction events occur on surfaces containing many adsorbates. Lateral interactions between co-adsorbates affect the stability of the adlayer as well as the chemical activity by altering the potential energy surface. Elucidating the structure of the adlayer experimentally is difficult due to the large number of adsorption sites that may be occupied and the complex electronic interaction between the adlayer and the surface. Theoretical modeling can resolve these assignments, but is computationally limited due to the large number of adlayer configurations that have to be evaluated. To overcome this limitation, a neural-network-based method is utilized in this work to study the interaction and aggregation behavior of CO on a plethora of metallic surfaces as commonly encountered in catalytic applications. It is found that the configuration of the CO molecules on the surface is largely controlled by steric interactions, resulting in the formation of three distinct operating regimes (low-, medium- and high-coverage) for all systems. The exact transition points and site occupations for these regimes were found not to follow simple correlations, instead depending on a complex balance between the surface-adsorbate and lateral interactions. Data-driven methodologies make these lateral interaction potentials accessible for use in kinetic studies without compromising accuracy. Transition metal ; Metal ; Phase transition ; Carbon fibers ; Cobalt Machine Learning in Materials Science ; Neural Networks and Applications ; Surface Treatment and Coatings
Authors: Alyssa Diona Rego | Suresh K. | T. Cynthia
Source:  (article, 2026-01-22)
Field: Materials Science — Materials Chemistry

A Machine-Driven Learning Framework for Sustainable Fashion Proto-Typing Using 3D Modelling and Material Efficiency Prediction This research paper explores the integration of machine learning frameworks with sustainable fashion proto-typing using 3D modelling technology to address prototyping sustainability challenges in garment and textile design. The proposed model analyses the garment's 3D modelled design and features to predict material efficiency, encompassing the amount of fabric required and used, the amount of material wasted, and the overall cost effectiveness and sustainability of the prototype through a sustainability score of the materials used for the prototype. The machine learning framework would suggest smart recommendations for alternative fabrics that are comparatively more eco-friendly, affordable and capable of minimizing fabric wastage, based on the evaluation of the score obtained by the prototype. The model helps designers and brands minimize wastage of physical prototypes while making data-driven material choices by incorporating this predictive process directly into the early stages of fashion prototyping. The advantages of this proposed model include improved resource optimization, the timely incorporation of sustainable practices, and reduced production costs. Apart from material prediction, the proposed model promotes zero-waste prototyping, lowers harmful environmental impact and encourages the adoption of digital-first design workflows, thereby contributing to a more sustainable and resilient fashion industry. In this paper, five multi-objective optimization algorithms that could be suitable for the proposed methodology model are examined. The MOPSO, NSGA-II, MOEA/D, SPEA2 and MOABC/MOACO algorithms are evaluated against four sustainability metrics: average price of the material used, estimated carbon emission related to the material used, water usage for the production of materials used, and landfill waste contribution of the material used in the prototype, to determine the most suitable algorithm for the proposed model's sustainability-based predictions Sustainability ; Process (computing) ; Production (economics) ; Material efficiency ; Resource efficiency ; Resource (disambiguation) ; Rapid prototyping ; Textile Machine Learning in Materials Science ; Sustainable Supply Chain Management ; Sustainable Industrial Ecology
Authors: David M. Vahey | Manting Mu | Shannon A. Bonke | Timo Sommer | Prithvi Vangal | Carl J. Mallia | Max García-Melchor | E Reisner
Source: Open MIND (article, 2026-01-01)
Field: Chemistry — Organic Chemistry

Anti-Friedel–Crafts alkylation via electron donor–acceptor photoinitiation Alkylation ; Phthalimide ; Nucleophile ; Electrophile ; Selectivity ; Yield (engineering) ; Molecule ; Alkyl Radical Photochemical Reactions ; Catalytic C–H Functionalization Methods ; Oxidative Organic Chemistry Reactions
Authors: Jesper Byggmästar
Source: ArXiv.org (article, 2026-03-04)
Field: Materials Science — Materials Chemistry

Machine-learned Interatomic Potential for Ti$_{n+1}$C$_n$ MXenes: Application to Ion Irradiation Simulations A computationally efficient and accurate machine-learned (ML) interatomic potential is developed for Ti$_{n+1}$C$_n$ MXenes. With a diverse set of structures computed with density functional theory, the trained ML potential demonstrates good accuracy and robustness to a wide range of bond distances and environments, making it a useful tool for molecular dynamics simulations of MXenes subjected to mechanical load or irradiation. The ML potential is applied to simulations of light and heavy ion irradiation, gathering insight into the statistics and probabilities of sputtering, reflection, defect creation, and implantation into Ti$_{n+1}$C$_n$ MXene sheets. The results provide guidelines for defect engineering of MXenes through ion irradiation and implantation. Additionally, the ML potential development provides a landmark recipe for enabling machine-learning-driven atomistic simulations of other MXenes. MXenes ; Molecular dynamics ; Interatomic potential ; Ion ; Robustness (evolution) ; Range (aeronautics) MXene and MAX Phase Materials ; Machine Learning in Materials Science ; Advanced Memory and Neural Computing