jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: An T. H. Le | Thomas Shvekher | Loan Nguyen | Sergey N. Krylov
Source: ChemBioChem (article, 2025-09-29)
Field: Materials Science — Materials Chemistry

A Conversational Large‐Language‐Model Tutor that Accelerates Machine‐Learning Method Development in Routine Bioanalytical Workflows As machine learning (ML) becomes increasingly relevant in experimental chemistry, many scientists face barriers to adoption due to limited training in ML. While AutoML platforms offer powerful capabilities, they lack the instructional scaffolding needed by users without an ML background. To address this gap, a lightweight, conversational assistant is presented that guides users through ML workflow design using plain-language dialog. Powered by OpenAI's GPT-4o and deployed via a Gradio interface, the assistant operates under a structured system prompt that simulates pedagogical reasoning. It behaves like a domain-specific tutor: helping users define ML goals, assess data structure, select models, evaluate metrics, and generate annotated Python code. A complete documentation of the development process is provided, allowing researchers to adapt the system for other domains. Herein, its utility is demonstrated in two representative case studies: 1) image classification of lateral flow immunoassay test strips for diagnostic readout; and 2) regression-based prediction of liquid chromatography-mass spectrometry retention times from molecular descriptors for small molecules. In both cases, lab members with no ML experience successfully developed working models guided solely by the assistant. By lowering the barrier to ML adoption in data-rich analytical workflows, this system offers a customizable workflow for building domain-specific assistants across experimental science. Workflow ; Python (programming language) ; Documentation ; TUTOR ; Process (computing) ; Test (biology) Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Scientific Computing and Data Management
Authors: Zhan Jiang | Yide Ran | Zhihong Xu | Shaoyi Huang | Weina Meng | Yi Bao
Source: Automation in Construction (article, 2025-11-21)
Field: Materials Science — Materials Chemistry

Automated construction of knowledge graphs for accelerated design and understanding of ultra-high-performance concrete Domain knowledge ; Knowledge graph ; Leverage (statistics) ; Knowledge extraction ; Structuring ; Knowledge-based systems ; Generalizability theory ; Knowledge engineering ; Design knowledge Machine Learning in Materials Science ; Concrete and Cement Materials Research ; Tunneling and Rock Mechanics
Authors: Jiayi Peng | Xinyu Chen | Zheng Tang | Linjie Wang | Zhou Zhan | Shuangquan Liu | Jinjin Liu | Xi Chen | Xiangheng Niu
Source: Biomedical Analysis (article, 2025-12-01)
Field: Materials Science — Materials Chemistry

Machine learning empowered smartphone nanozyme sensing enables precise quantitative prediction of urinary uric acid The increasing incidence of gout necessitates monitoring of uric acid (UA) levels for early risk assessment and clinical management. Although nanomaterials with oxidoreductase-mimicking properties demonstrate great promise in natural enzyme-free UA detection, accurate quantification in authentic samples remains a challenge due to common interference from coexisting reductants and matrix complexity. To overcome this limitation, here we integrate nanozyme catalysis-based smartphone sensing with machine learning to develop regression models that can effectively eliminate matrix interference, thereby achieving the accurate quantification of UA in real urine. A new peroxidase-mimetic nanozyme (Na2V6O16) was prepared to catalyze the H2O2-mediated oxidation of 3,3’,5,5’-tetramethylbenzidine (TMB) to blue oxTMB. When UA existed, it could suppress the chromogenic process due to its reducibility, leading to a smartphone-based assay exhibiting linear response to UA (0.05–0.4 mM) with a detection limit of 0.045 mM. Crucially, to eliminate the interference of redox species (ascorbic acid, L-cysteine, glutathione, etc.) coexisting in samples, machine learning algorithms optimized through parameter tuning were employed to correct background signals from actual specimens, significantly enhancing detection accuracy and reliability. Our work achieves the precise quantitative prediction of UA in complex urine matrices, providing a sensitive, reliable, and non-invasive approach for gout risk stratification and management. Uric acid ; Gout ; Support vector machine ; Detection limit ; Matrix (chemical analysis) ; Interference (communication) ; Process (computing) Advanced Nanomaterials in Catalysis ; Electrochemical sensors and biosensors ; Gout, Hyperuricemia, Uric Acid
Authors: Mingyu Jiang | Zhiling Zhu
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-07-30)
Field: Materials Science — Materials Chemistry

Machine-Learning-Assisted Exploration of High Entropy-Atom Nanozyme for Anti-Tumor Immunotherapy by Enhancing Enzyme Activity and Disrupting Dual Energy Metabolism Abstract Despite its potential in cancer therapy, single-atom nanozyme (SAzyme) faces challenges like low atomic loading and rapid cancer metabolism. Here, a high-entropy atom nanozyme (HEAzyme), PtNiBiSnSb-anti-CD36, is designed for efficient anti-tumor immunotherapy. The PtNiBiSnSb HEAzyme, incorporating five SAzyme, demonstrates enhanced peroxidase (POD)-like activity due to its abundant active sites, showing a 7.2-fold increase in catalytic efficiency compared to the structurally analogous PtBi nanozyme. Further integrating of density functional theory calculations and machine learning analysis reveals that the main reason is due to the addition of Ni, Sn, and Sb, which reduced the reaction energy barrier and changed the adsorption energy of surface hydroxyl groups. Moreover, PtNiBiSnSb-anti-CD36 not only inhibits normal metabolic processes by depleting nicotinamide adenine dinucleotide (NADH) through its enhanced NADH-POD-like activity, but also suppresses lipid metabolism by blocking CD36-mediated uptake. The dual metabolic inhibition facilitates the mild photothermal therapy induced by PtNiBiSnSb. In this process, the anti-tumor immune response is amplified by inducing immunogenic cell death and inhibiting the growth of immunosuppressive cells through blocking fatty acid uptake. The proposal of HEAzyme for cancer therapy provides a novel approach to enhance the catalytic activity of SAzymes and deepens the theoretical understanding of HEAzyme in the POD-like reaction process. Dual (grammatical number) ; Enzyme ; Immunotherapy ; Energy metabolism ; Chemistry ; Entropy (arrow of time) ; Cancer research ; Metabolism ; Biochemistry ; Computational biology ; Biology ; Medicine ; Physics ; Immune system ; Internal medicine ; Immunology ; Philosophy ; Thermodynamics Advanced Nanomaterials in Catalysis ; Nanoplatforms for cancer theranostics ; Nanocluster Synthesis and Applications
Authors: Amélie Descamps | Hannah Hirmz | Ewerton Cristhian Lima de Oliveira | Anton De Spiegeleer | Juliana Auzier Seixas Feio | Kauê Santana da Costa | Bart De Spiegeleer | Evelien Wynendaele
Source: ACS Omega (article, 2025-06-17)
Field: Chemistry — Spectroscopy

Beyond Molecular Weight: Peptide Characteristics Influencing the Sensitivity of Retention to Changes in Organic Solvent in Reversed-Phase Chromatography The <i>S</i> value, representing the sensitivity of peptide retention to changes in organic solvent concentrations in reversed-phase liquid chromatography (RP-LC), is a critical parameter for optimizing gradient elution and enhancing separation selectivity. While the influence of molecular weight on <i>S</i> values is well-documented, the contribution of other peptide-specific characteristics remains insufficiently understood. This study experimentally evaluated a structurally diverse dataset of 18 peptides and applied a multifaceted data approach combining partial least squares, Kendall's tau correlation analysis, and machine learning models (multiple linear regression, decision tree, and lasso) to identify key molecular features affecting the <i>S</i> value. Factors such as hydrophobicity and the number of CH<sub>2</sub>R<sub>2</sub> groups, structural flexibility, isoelectric point, and the number of lysine residues emerged as significant contributors. A predictive model was developed, offering a tool for method optimization in peptide separations. Partial least squares regression ; Chemistry ; Peptide ; Selectivity ; Chromatography ; Flexibility (engineering) ; Reversed-phase chromatography ; Isoelectric point ; Solvent ; Decision tree ; High-performance liquid chromatography ; Mathematics ; Machine learning ; Statistics ; Computer science ; Organic chemistry ; Biochemistry Analytical Chemistry and Chromatography ; Chromatography in Natural Products ; Metabolomics and Mass Spectrometry Studies
Authors: Guowen Gao | Enling Tang | Zhe Zhai | Guolai Yang | Yafei Han
Source: Polymer Testing (article, 2025-11-21)
Field: Materials Science — Materials Chemistry

Damage characteristics and structural optimization of PELE with gradient reactive filling penetrating steel targets To enhance the radial velocity and damage effectiveness of fragments after PELE projectile penetration, Al/Ep/B reactive material was selected as the core filler. Ballistic gun tests were conducted to study the dynamic penetration of steel targets by PELE projectiles with different core structures. The damage mechanisms of fragments were analyzed through mass distribution statistics and three-dimensional morphological reconstruction, while high-speed imaging was used to quantitatively characterize the axial and radial velocity fields of the fragments. A numerical model corresponding to the experimental conditions was established using Autodyn, incorporating a subroutine to describe the equation of state of the reactive material. The results showed high consistency between the simulated fragment morphology and velocity distribution and the experimental data. To further optimize the reactive core structure, a deep neural network was employed to establish the relationship between material parameters, fragment mass, and radial velocity. Multi-objective optimization was performed using the Non-dominated Sorting Genetic Algorithm (NSGA-II), ultimately identifying the gradient configuration as the optimal solution. A comparison between the optimization results and numerical simulations showed less than 5 % error in key parameters, verifying the reliability of the optimization. • Al/Ep/B material is employed as the filler material for PELE and two filling configurations were designed. • Fragment damage mechanisms were analyzed based on mass distribution statistics and planar morphology reconstruction. • Axial and radial velocity field characteristics of the fragments were quantitatively characterized. • Simulation model of user-defined subroutine was used to characterize the unreacted material, ignition growth model. • Deep neural network was developed to establish the relationship between material parameters, fragment mass and radial velocity. Projectile ; Isotropy ; Subroutine ; Ignition system ; Sorting ; Computer simulation ; Penetration (warfare) High-Velocity Impact and Material Behavior ; Energetic Materials and Combustion ; Military Defense Systems Analysis
Authors: Ugochukwu C. Okonkwo | Christian Emeka Okafor | Shahnawaz Ahmad | Christopher Chukwuemeka Ohagwu | Michael Echeta Aronu | Imhade P. Okokpujie | Christopher Igwe Idumah | Nelson N. Chukwu | Christian Ebele Chukwunyelu | Jiyaul Mustafa
Source: Life Cycle Reliability and Safety Engineering (article, 2025-04-29)
Field: Materials Science — Materials Chemistry

Research advancements in machine learning-assisted design of reinforced composite radiation shields Shields ; Composite number ; Computer science ; Artificial intelligence ; Mechanical engineering ; Materials science ; Engineering ; Composite material ; Electromagnetic shielding Radiation Shielding Materials Analysis ; Graphite, nuclear technology, radiation studies ; Nuclear and radioactivity studies
Authors: John G. Pavek | Nicholas E. Bollis | Jonathan M. Grimes | Michael R. Shortreed | Lloyd M. Smith | Michael T. Marty
Source: Journal of the American Chemical Society (article, 2025-06-10)
Field: Chemistry — Spectroscopy

A Fast Neural Network for Isotopic Charge State Assignment Electrospray ionization (ESI) mass spectrometry is an essential technique for chemical analysis in a range of fields. In ESI, analytes can produce multiple charge states, which must be correctly assigned for identification. Existing approaches to charge state assignment can suffer from limited accuracy or poor speed. Here, we developed a fast neural network to perform isotopic envelope charge assignment. The performance of our algorithm, IsoDec, was demonstrated on top-down proteomics spectra collected on diverse instruments. On these highly complex individual spectra, we found that IsoDec correctly assigns more features compared to existing software tools while simultaneously providing improved speed and accuracy. Importantly, this performance enhancement stems directly from the neural network charge assignment approach and not simply from improved scoring and filtering of isotopic envelopes. Finally, when applied to large top-down proteomics data sets, we discovered that database searching of the IsoDec deconvolution output produces proteoform-spectrum matches with a better combination of coverage and accuracy. Overall, IsoDec provides a compelling demonstration of the potential of lightweight neural networks in mass spectrometry data analysis for diverse applications. Chemistry ; Charge (physics) ; State (computer science) ; Artificial neural network ; Computational chemistry ; Artificial intelligence ; Algorithm Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Isotope Analysis in Ecology
Authors: Jianliang Cao | Chang Liu | Danling Wang | Haichao Li | Lina Xu | Hong‐Ping Xiao | Shaoqi Zhan | He Xiao | Guoyong Fang
Source: Acta Physico-Chimica Sinica (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

Machine learning potentials for property predictions of two-dimensional group-III nitrides Phonon ; Thermal conductivity ; Density functional theory ; Molecular dynamics ; Lattice (music) ; Thermal ; Scattering ; Nitride ; Energy (signal processing) 2D Materials and Applications ; Thermal properties of materials ; GaN-based semiconductor devices and materials
Authors: Ying Liu | Huazhang Zhang | Konstantin Shapovalov | Ranming Niu | Julie M. Cairney | Xiaozhou Liao | Krystian Roleder | A. Majchrowski | Jordi Arbiol | Philippe Ghosez | Gustau Catalán
Source: ArXiv.org (preprint, 2025-02-09)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Vortices and antivortices in antiferroelectric PbZrO3 Although ferroelectric materials are characterised by their parallel arrangement of electric dipoles, in the right boundary conditions these dipoles can reorganize themselves into vortices, antivortices and other non-trivial topological structures. By contrast, little is known about how (or whether) antiferroelectrics, which are materials showing an antiparallel arrangement of electric dipoles, can exhibit vortices or antivortices. In this study, using advanced aberration-corrected scanning transmission electron microscopy, we uncover the existence of atomic-scale (anti)vorticity in ferroelastic domain walls of the archetypal antiferroelectric phase of PbZrO3. The finding is supported, and its underlying physics is explained, using both second-principles simulations based on a deep-learning interatomic potential, and continuum field modelling. This discovery expands the field of chiral topologies into antiferroelectrics. Antiferroelectricity ; Vortex ; Physics ; Meteorology ; Quantum mechanics Multiferroics and related materials ; Ferroelectric and Piezoelectric Materials ; Solid-state spectroscopy and crystallography
Authors: Dongxiao Chen | Philippe Sautet
Source: Angewandte Chemie International Edition (article, 2025-11-04)
Field: Materials Science — Materials Chemistry

Atomistic Landscape of Pt Nanoparticles via Machine Learning: How Size Effect and Hydrogen Adsorption Govern Structural Ensembles and Catalytic Activity Understanding the atomistic structure and fluxionality of Pt nanoparticles under reactive conditions is essential for rational design of effective catalysts, yet their structural complexity presents a great challenge. In this work, we combine grand canonical global optimization methods and machine learning potentials to explore the atomistic landscape of nanometer-sized (1∼2 nm) Pt nanoparticles under a pressure of hydrogen, resulting in a comprehensive library of Pt<sub>x</sub>H<sub>y</sub> nanoparticles with over one million low-energy metastable structures. We found that hydrogen adsorption drives a size-dependent transformation from an amorphous to a crystalline structure, leading to sharp phase transitions for smaller nanoparticles and smooth transformations for larger ones. This behavior is governed by a competition between distinct core configurations, as well as the formation of rigid and fluxional local domains, where stability is dictated by specific surface motifs at low H coverage and by the crystalline core at high H coverage. By applying this structural library for reactivity modeling of methane dehydrogenation and ethylene hydrogenation, we show a marked discrepancy between the abundance of a surface site and its catalytic contribution, indicating that the active sites are rare, structurally distinct motifs, not the most common sites. Dehydrogenation ; Nanoparticle ; Catalysis ; Metastability ; Amorphous solid ; Adsorption ; Molecular dynamics ; Phase (matter) Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalysis and Oxidation Reactions
Authors: Nam‐Kee Kang | Young-Sun Joo | Hyosung An | Hyerim Hwang
Source: Nanoscale Horizons (review, 2025-01-01)
Field: Materials Science — Materials Chemistry

Colloidal systems as experimental platforms for physics-informed machine learning Colloidal systems offer a unique experimental window for investigating condensed matter phenomena, uniquely enabling simultaneous access to microscopic particle dynamics and emergent macroscopic responses. Their particle-scale size, thermal motion, and tuneable interactions allow for real-time, real-space, and single-particle-resolved imaging. These features make it possible to directly connect local structural changes, dynamic rearrangements, and mechanical deformation with system-level behaviours. Such capabilities remain largely inaccessible in atomic or molecular systems. This review presents colloidal modelling as a predictive framework that addresses persistent challenges in materials research, including phase classification, dynamic arrest, and defect-mediated mechanics. We describe methodologies for extracting structural, dynamical, and mechanical descriptors from experimental imaging data, show how these features capture governing variables of material behaviour, and illustrate their application in machine learning approaches for phase identification, dynamics prediction, and inverse design. Rather than treating colloidal data as limited to model systems, we emphasize its value as a training ground for developing interpretable and physics-informed models. By linking microscopic mechanisms with macroscopic observables in a single experimental system, colloids generate structured and generalizable datasets. Their integration with data-driven methods offer a promising pathway toward predictive and transferable materials design strategies. Observable ; Particle (ecology) ; Phase (matter) ; Colloidal particle ; Experimental data ; Thermal fluctuations ; Complex system ; Mechanism (biology) Machine Learning in Materials Science
Authors: Zhi-Chao Zhang | Jun Zhou | Jianfei Wen | Fangnan Liang | Fang Zhang | Qianxiu Liu | Jifan Hu
Source: Journal of Luminescence (article, 2025-07-15)
Field: Materials Science — Materials Chemistry

Machine learning analysis and discovery of novel blue-cyan-emitting Na18Ca13Mg5(PO4)18:Eu2+ phosphor Cyan ; Phosphor ; Blue light ; Materials science ; Chemistry ; Optoelectronics ; Physics ; Optics Luminescence Properties of Advanced Materials ; Solid-state spectroscopy and crystallography ; Gas Sensing Nanomaterials and Sensors
Authors: Abdur Baig | Adeel Akram | Ming‐Kuem Lin
Source: International Journal of Molecular Sciences (review, 2025-08-30)
Field: Chemistry — Organic Chemistry

Agarwood in the Modern Era: Integrating Biotechnology and Pharmacology for Sustainable Use Agarwood, valued for its resin, has long been used in perfumery, incense, and traditional medicine. Its resin is primarily derived from species of <i>Aquilaria</i> and is produced through a still-unknown process in response to biotic or abiotic stress. Concerns regarding agarwood's sustainability and conservation have emerged because of the substantial loss of natural resources due to overharvesting and illegal trade. To address these concerns, artificial techniques are being used to produce agarwood. The mechanism underlying agarwood production must be elucidated to enhance yield. The authentication of agarwood species is challenging because of morphological similarities between pure and hybrid <i>Aquilaria</i> species. Techniques such as DNA barcoding, molecular marker assessment, and metabolomics can ensure accurate identification, facilitating conservation. Artificial intelligence and machine learning can support this process by enabling rapid, automated identification on the basis of genetic and phytochemical data. Advances in resin induction methods (e.g., fungal inoculation) and chemical induction treatments are improving yield and quality. Endophytic fungi and bacteria promote resin production at minimal harm to the tree. Agarwood's pharmacological potential-antimicrobial, anti-inflammatory, and anticancer effects-has driven research into bioactive compounds such as sesquiterpenes and flavonoids for the development of novel drugs. This systematic review synthesized current evidence on species authentication, induction techniques, and pharmacological properties. The findings may guide future research aimed at ensuring sustainable use and enhancing the medicinal value of agarwood. Agarwood ; Biotechnology ; Traditional medicine ; Pharmacology ; Biology ; Engineering ethics ; Medicine ; Engineering ; Alternative medicine Wood and Agarwood Research ; Identification and Quantification in Food ; Microbial Metabolism and Applications
Authors: Yong Qiu | L.Y Wang | Chang Liu | Xu Zhang | Yun Tian | Zhen Zhou
Source: Materials Today (article, 2025-10-29)
Field: Chemistry — Inorganic Chemistry

The evolution of MOF discovery for CO2 capture: From high-throughput screening to AI design and automated laboratories Flexibility (engineering) ; Field (mathematics) ; Paradigm shift ; Bridge (graph theory) ; Key (lock) Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies ; Carbon dioxide utilization in catalysis