jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Miao Yu | Liu-Yu Zong | Wen‐Hao Su
Source: Journal of Food Composition and Analysis (review, 2025-10-09)
Field: Chemistry — Analytical Chemistry

Machine learning in VIS/NIR spectroscopy and hyperspectral imaging for rapid detection of staple foods quality and safety: A review Hyperspectral imaging ; Staple food ; Linear discriminant analysis ; Pattern recognition (psychology) ; Partial least squares regression ; Convolutional neural network ; Support vector machine ; Quality (philosophy) Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Zaidoon M. Shakor | Yaseen M. Tayib | Adnan A. AbdulRazak | Zainab Y. Shnain | Emad N. Al-Shafei
Source: ACS Omega (review, 2025-08-11)
Field: Materials Science — Materials Chemistry

Thermogravimetric Analysis Integrated with Mathematical Methods and Artificial Neural Networks for Optimal Kinetic Modeling of Biomass Pyrolysis: A Review This review emphasized the role of mathematical models and correlations in thermogravimetric analysis to evaluate the thermal stability of various materials, including biomass, polymers, recycled plastics, and solid fuels of carbon and biomass material. Numerous thermogravimetric analysis kinetic models are driven, and they are broadly divided into model-free and model-based categories. Integral models have proven to be more effective for fitting, particularly for materials with wide decomposition temperature ranges in biomass material and mixed recycled plastic waste. The <i>n</i>th order model showed superior predictive accuracy compared with the first-order model, particularly for solid biomass, highlighting the significance of model selection. Traditional thermogravimetric analysis mathematical models are limited in accounting for mass loss as a function of all effective variables. In contrast, artificial neural networks (ANNs) efficiently represent and incorporate these variables, marking a significant advancement in predicting thermogravimetric analysis kinetics. ANNs provide powerful tools for managing complex analysis data, enabling robust predictions and deeper insights into material thermal behavior. Thermogravimetric analysis ; Pyrolysis ; Artificial neural network ; Biomass (ecology) ; Kinetic energy ; Biochemical engineering ; Biological system ; Computer science ; Artificial intelligence ; Engineering ; Chemical engineering ; Ecology ; Biology ; Physics Thermal and Kinetic Analysis ; Thermochemical Biomass Conversion Processes ; Flame retardant materials and properties
Authors: Vivin Vinod | Dongyu Lyu | Marcel Ruth | Peter R. Schreiner | Ulrich Kleinekathöfer | Peter Zaspel
Source: Journal of Computational Chemistry (article, 2025-03-04)
Field: Materials Science — Materials Chemistry

Predicting Molecular Energies of Small Organic Molecules With Multi‐Fidelity Methods Multi-fidelity methods in machine learning (ML) have seen increasing usage for the prediction of quantum chemical properties. These methods, such as <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mi>Δ</mml:mi></mml:mrow> <mml:annotation>$$ \Delta $$</mml:annotation></mml:semantics> </mml:math> -ML and Multifidelity Machine Learning (MFML), have been shown to significantly reduce the computational cost of generating training data. This work implements and analyzes several multi-fidelity methods including <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mi>Δ</mml:mi></mml:mrow> <mml:annotation>$$ \Delta $$</mml:annotation></mml:semantics> </mml:math> -ML and MFML for the prediction of electronic molecular energies at DLPNO-CCSD(T) level, that is, at the level of coupled cluster theory including single and double excitations and perturbative triples corrections. The models for small organic molecules are evaluated not only on the basis of accuracy of prediction, but also on efficiency in terms of the time-cost of generating training data. In addition, the models are evaluated for the prediction of energies for molecules sampled from a public dataset, in particular for atmospherically relevant molecules, isomeric compounds, and highly conjugated complex molecules. Coupled cluster ; Fidelity ; Molecule ; Computer science ; Quantum chemical ; High fidelity ; Training set ; Organic molecules ; Work (physics) ; Cluster (spacecraft) ; Chemistry ; Machine learning ; Artificial intelligence ; Computational chemistry ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Sergei F. Vyboishchikov
Source: Journal of Computational Chemistry (article, 2025-04-19)
Field: Materials Science — Materials Chemistry

Atomic Neural Network for Calculation of Solvation Free Energies in Organic Solvents This paper introduces AtomicESE, an artificial neural network for calculating solvation-free energies ΔG°<sub>solv</sub> of molecules in organic solvents. AtomicESE calculates ΔG°<sub>solv</sub> by summing atomic contributions, each evaluated by a dense neural network. This atomic network uses 13 physically relevant input features, comprising six local atomic features, two global charge-related molecular properties, and five solvent-specific properties. For neutral solutes, AtomicESE achieves an average RMSE below 0.6 kcal/mol, demonstrating strong performance across all solvent classes, with particularly high accuracy for aromatic, haloaromatic, alkane, and ketone solvents. AtomicESE also works reliably for ionic solutes. Solvation ; Atomic charge ; Alkane ; Chemistry ; Artificial neural network ; Solvent ; Implicit solvation ; Ketone ; Molecule ; Ionic bonding ; Computational chemistry ; Organic chemistry ; Hydrocarbon ; Computer science ; Machine learning ; Ion Machine Learning in Materials Science ; Free Radicals and Antioxidants ; Computational Drug Discovery Methods
Authors: Shourya Bodla | Prince Jain | Anwesha Khanra | Chhavi Sharma | Anupam Jyoti | Shiv Dutt Purohit | Hemant Singh | Abhijeet Singh | Juhi Saxena
Source: IEEE Open Journal of Engineering in Medicine and Biology (article, 2025-01-01)
Field: Materials Science — Biomaterials

Biomimetic Chitosan-Based Hydrogels for Sustainable Wound Healing With AI/ML Insights Wound healing process is associated with multifaceted complications and is a functional way to advance the therapeutic process. Polymeric biomaterials exhibit structural mimicry with the extracellular matrix of the tissue to be regenerated and they also avoid chronic inflammation and immunological responses. Chitosan, a biopolymer demonstrates exceptional healing properties because of its biocompatibility, biodegradability, antimicrobial nature and affinity for biomolecules. Biomaterials consisting of chitosan along with herbal extracts could be ideal for wound healing. Click chemistry can provide one of the best ways to combine these bio-actives with chitosan. Advancing wound healing strategies with artificial intelligence /machine learning approaches can be employed further to boost the clinical efficacies of bioactive-loaded chitosan composite hydrogels. This review article investigates functionalized wound dressings with special emphasis on chitosan-based hydrogels, their effects on wound healing, and advanced approaches to increase hydrogel benefits by adding bioactive substances to form nanocomposites. Self-healing hydrogels ; Chitosan ; Wound healing ; Biomedical engineering ; Wound dressing ; Chemistry ; Materials science ; Chemical engineering ; Medicine ; Engineering ; Polymer chemistry ; Surgery ; Composite material Electrospun Nanofibers in Biomedical Applications ; Wound Healing and Treatments
Authors: Jiawei Guo | Marcos F. Calegari Andrade | Christopher Hahn | Ambarish Kulkarni | Nitish Govindarajan
Source: ChemRxiv (preprint, 2025-06-02)
Field: Chemistry — Electrochemistry

Understanding cation and surface charging effects at electrified interfaces using neural network interatomic potentials Electrified interfaces are at the heart of electrochemical processes related to several technologically important applications, yet they remain poorly understood. This lack of understanding is primarily due to their complex and dynamic nature, that poses significant challenges in developing realistic models. To overcome some of these challenges, we train message-passing neural network interatomic potentials based on the MACE architecture to perform long time-scale molecular dynamics (MD) simulations (O(ns)) of electrified Au interfaces with near-first-principles accuracy. Specifically, we study the impact of surface charging and cation identity (Li+, Na+, K+, Cs+) to obtain detailed insights into various interfacial properties, including the structure and dynamics of (near surface) water molecules, cations, and the double layer capacitance. In general, we find that surface charging has a greater impact on these interfacial properties compared to that of the cation identity. Furthermore, we find that the estimated double layer capacitance from MACE/MD simulations (17-19 muF/cm2) at cathodic potentials is largely insensitive to the cation identity. Our work demonstrates the potential of message passing neural network potentials to obtain a detailed atomistic-level understanding of electrified interfaces. Artificial neural network ; Surface (topology) ; Computer science ; Chemical physics ; Materials science ; Chemistry ; Artificial intelligence ; Mathematics ; Geometry Electrochemical Analysis and Applications ; Gas Sensing Nanomaterials and Sensors ; Analytical Chemistry and Sensors
Authors: Tomoki Murata | Noriko Saito | Eiji Koyama | T. Phuong | Ryusuke Misawa | Satoshi Yokomizo | Tomoya Mato | Yu Takada | S. Hirose | Yukari Katsura
Source: Science and Technology of Advanced Materials Methods (article, 2025-04-04)
Field: Materials Science — Materials Chemistry

Data-driven analysis and visualization of dielectric properties curated from scientific literature Data-driven methods are powerful tools for understanding and discovering materials. However, in certain domains of materials science, the lack of available datasets significantly restricts the research scope. To address this issue, we utilized Starrydata2 web system to compile a comprehensive dataset on dielectric materials, collecting experimental data on over 20,000 samples across a wide compositional space. This dataset enabled the development of machine learning models with high predictive performance and facilitated the identification of important descriptors through recursive feature eliminations. Since the models worked as complete black boxes and hindered intuitive understanding, we employed additional techniques such as dimensionality reduction and clustering to visualize compositional landscape and trends in dielectric properties. By combining the identified important factors with material clustering, we attempted to visualize the effect of crystal lattice on dielectric permittivity within ABO<sub>3</sub> systems, revealing a roughly linear relationship. Our preliminary analyses and visualizations demonstrate the potential of the Starrydata dielectric dataset collected in this study, offering an important foundation for advanced data-driven materials research. This study presents a comprehensive dielectric database curated from the scientific literature with data analyses and visualizations demonstrating its potential, offering an important foundation for advanced data-driven materials research. Visualization ; Data science ; Computer science ; Dielectric ; Scientific visualization ; Data visualization ; Data mining ; Materials science ; Optoelectronics Machine Learning in Materials Science ; Ferroelectric and Piezoelectric Materials ; Semiconductor materials and devices
Authors: Swaroop Chakraborty | Sylvia Britto | Miguel A. Gomez‐Gonzalez | Ana Guilherme Buzanich | Iuliia Mikulska
Source: Cell Reports Physical Science (article, 2025-09-01)
Field: Materials Science — Surfaces, Coatings and Films

Synchrotrons, neutron sources, and XFELs guiding the future of safe and sustainable nanomaterials <h2>Summary</h2> Achieving safe and sustainable nanomaterials remains challenging—not necessarily from limited synthetic innovation but due to gaps in observing structural and chemical transformations under environmental conditions. Here, we make a call for a tri-beam operando characterization strategy, integrating synchrotron, neutron, and X-ray free-electron laser (XFEL) techniques into one synergistic experimental framework. Unlike traditional methods providing disconnected snapshots, tri-beam analysis dynamically tracks nanomaterial evolution from atomic-scale changes to structural collapse under near-real-world/quasi-realistic conditions. This holistic approach reveals previously hidden degradation pathways, transient states, and physicochemical thresholds that reshape definitions of material safety. Enhanced by robotic automation, machine learning, and findable, accessible, interoperable, and reusable (FAIR) data principles, our method directly supports Europe's safe and sustainable by design (SSbD) initiative. We propose embedding tri-beam datasets into regulatory standards, predictive models, and AI-driven screening workflows. Ultimately, tri-beam operando characterization represents a transformative platform for designing resilient, high-performance nanomaterials that meet the environmental and societal demands of the 21st century. Characterization (materials science) ; Transformative learning ; Nanomaterials ; Embedding ; Sustainable development Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications ; Nuclear Physics and Applications
Authors: Anna Katharina Picha | Marcus Wieder | Stefan Boresch
Source: Journal of Chemical Information and Modeling (article, 2025-09-11)
Field: Materials Science — Materials Chemistry

Transferable Neural Network Potentials and Condensed Phase Properties Transferable neural network potentials (NNP) are undergoing rapid development. Many practical applications of NNPs focus on single molecules; e.g., using NNPs as a fast replacement for quantum chemical methods for dihedral angle scans in force field development. Similarly, the reference data on which most transferable NNPs have been trained are single molecule properties. As NNPs are beginning to be used to simulate more complex systems, such as solute-solvent simulations, the question arises whether the current generation of transferable NNPs is accurate enough to reproduce condensed phase properties, which in most cases are outside the training domain of the models. Here we present a first analysis of how well two transferable NNPs (ANI-2x, MACE-OFF23(S/M)) perform in reproducing properties such as density, heat of vaporization, heat capacity, and isothermal compressibility of several pure liquids (water, methanol, acetone, benzene, <i>n</i>-hexane at room temperature, and <i>N</i>-methylacetamide at 100 C). In addition, we examine selected radial distribution functions and the self-diffusion constant. We find specific weaknesses for each of the models, and seemingly small flaws lead to poor performance when applied to condensed phase simulations. The varied outcomes observed with the machine learning potentials suggest that, currently, selecting an architecture or model for all-NNP simulations of real-world applications requires careful consideration and testing. Phase (matter) ; Work (physics) ; Artificial neural network ; Focus (optics) ; Domain (mathematical analysis) ; Compressibility ; Dihedral angle Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Thomas C. Nicholas | Daniel F. Thomas du Toit | Louise A. M. Rosset | Davide Μ. Proserpio | Andrew L. Goodwin | Volker L. Deringer
Source: ArXiv.org (preprint, 2025-03-31)
Field: Chemistry — Inorganic Chemistry

The structure and topology of an amorphous metal-organic framework Amorphous metal-organic frameworks are an important emerging materials class that combine the attractive physical properties of the amorphous state with the versatility of metal-organic framework (MOF) chemistry. The structures of amorphous MOFs have largely been inferred by drawing analogies to crystalline polymorphs and inorganic glasses, but ultimately the validity of such structural models has been challenging to establish either experimentally or computationally. Here we use a unified data-driven approach, combining experimental scattering data and active machine learning for interatomic potentials, to determine the structure of an amorphous zeolitic imidazolate framework (a-ZIF) -- the canonical amorphous MOF. Our results reveal clear differences between the structure of a-ZIF and that of other amorphous tetrahedral networks, allowing us to invalidate the long-standing assumption that these inorganic and hybrid glasses are topologically equivalent. To this end, we introduce a systematic notation for the network topology of amorphous solids, building a bridge to the successful use of topology analysis in crystalline MOFs and to materials informatics. Our work provides insights into the structure and topology of the archetypal amorphous MOF and opens up new avenues for modelling and understanding amorphous framework materials more generally. Amorphous solid ; Topology (electrical circuits) ; Network topology ; Zeolitic imidazolate framework ; Polyamorphism ; Tetrahedron Metal-Organic Frameworks: Synthesis and Applications ; Zeolite Catalysis and Synthesis ; X-ray Diffraction in Crystallography
Authors: Zhengneng Zheng | Fan Zheng | Yuning Wu | Shiyou Chen | Lin‐Wang Wang
Source: Computational Materials Today (article, 2025-03-03)
Field: Materials Science — Materials Chemistry

A Generalized Bond Switching Monte Carlo method for amorphous structure generation Amorphous semiconductors are used in many electronic devices. For Si and other highly covalent bond systems, the amorphous structures can be generated using the bond switching method. However, for other non-covalent amorphous materials, it becomes challenging to generate their atomic structures. In this work, we extend the bond switching method to various complex and ionic systems. This method can be used to transform a general ionic crystalline structure to an amorphous structure (such as HfO 2 ), or generate the amorphous structures without the crystalline counterparts such as carbon black. We also introduce additional terms in the Bond Switching Monte Carlo process to control the porosity of the amorphous system, such as the amorphous In 2 O 3 . In addition, the amorphous structure can be generated to reproduce the experimental pair correlation function. We have investigated the changes in coordination numbers of the Bond Switching Monte Carlo process, and analyzed the usability of this method depending on the ionicity of the system. • We have introduced a new atomic repulsing term to control the uniformity of the amorphous structure, and successfully eliminated the voids in In 2 O 3 , making the structure generated by BSMC more reasonable. • Our method has a faster running speed compared to molecular dynamics (MD) methods like DFT MD and MD with machine learning potential , especially for materials with strong covalent properties. MD usually requires a long simulation time to break covalent bonds , however, this process is quite easy in our BSMC program. In addition, we also provide a series of universal parameters to generate amorphous materials for most systems, while machine learning methods require model retraining for different systems • In our program, the experimental pair correlation function can be used as input to reproduce the desired amorphous structure. Therefore, the structure generated by our program can match the experimental results . • Our algorithm is not only applied to covalent bond systems , but also tested for more ionic systems like HfO 2 , and In 2 O 3 . We have not seen such ionic systems constructed by BSMC in the literature. According to our calculations, the structure generated by our program has a local bonding number similar to MD , a bandgap and dielectric constant consistent with experiments, and does not contain any deep defect levels. • The algorithm can generate amorphous system in specific shaped structures, like slab, bending structure, interface and device structures like GAA FET, thus pave the way for realistic nanosized electronic device simulations. Monte Carlo method ; Statistical physics ; Amorphous solid ; Bond ; Materials science ; Physics ; Mathematics ; Crystallography ; Chemistry ; Statistics ; Economics Phase-change materials and chalcogenides ; Metallic Glasses and Amorphous Alloys ; Glass properties and applications
Authors: Julia A. Stebani | Íñigo Iribarren Aguirre | Gohar A. Siddiqui | Darren Wragg | Alessio Gagliardi | Angela Casini
Source: Journal of Chemical Theory and Computation (article, 2025-11-20)
Field: Chemistry — Organic Chemistry

Computational Workflow to Unravel the Structural Dynamics of Supramolecular Metallacages in Solution The structural dynamics of self-assembled metallacages is important because it determines their function and stability in different applications involving encapsulation and release of a guest molecule. We present here an integrated computational workflow to study the dynamic behavior of selected [Pd<sub>2</sub>L<sub>4</sub>]<sup>4+</sup> metallacages in explicit solvents (water and DMSO) and benchmark them for future <i>in silico</i> investigations of their host-guest chemistry, pivotal to their application as drug delivery systems. Two different pathways for the Molecular Dynamics (MD) simulations of the systems are explored, namely classical force field (FF) and Machine Learning Interatomic Potentials (MLIPs), to assess the conformational changes of two cage systems in solution, enabling evaluation of the performance vs computational cost for both methodologies. The proposed workflow offers a versatile framework to computationally assess the structural dynamics of supramolecular systems in solution, effectively bridging the gap between quantum-level accuracy and the temporal and spatial scales needed for simulations of different functional applications. Workflow ; Bridging (networking) ; Molecular dynamics ; Supramolecular chemistry ; Benchmark (surveying) ; Field (mathematics) ; System dynamics Supramolecular Chemistry and Complexes ; Magnetism in coordination complexes ; Surface Chemistry and Catalysis
Authors: Imtiaz Qavi | George Z. Tan
Source: Journal of Intelligent Manufacturing (article, 2025-09-06)
Field: Materials Science — Biomaterials

Deep learning based computer vision for predicting electrospinning stability via Taylor cone analysis Stability (learning theory) ; Artificial intelligence ; Deep learning ; Electrospinning ; Cone (formal languages) ; Computer science ; Engineering ; Computer vision ; Materials science ; Machine learning ; Algorithm ; Composite material Electrospun Nanofibers in Biomedical Applications ; Advanced Sensor and Energy Harvesting Materials ; Lubricants and Their Additives