jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Denis Mušić | Xuelian Xiao | Reza Naser | Keke Chang | Grzegorz Sadowski | Pär A.T. Olsson
Source: Advanced Science (article, 2025-05-29)
Field: Materials Science — Materials Chemistry

Kinetically Limited Growth of Dendritic Tin Oxide Thin Films: a Machine Learning Study beyond the Structure Zone Diagram Even after fifty years since its introduction, the empirical Thornton's structure zone diagram remains a valuable tool for predicting thin film microstructure. This diagram is essential for understanding the correlation between synthesis, composition, structure, and physical properties in emerging applications. In this work, we critically appraise this diagram by examining Sn─O thin films grown at room temperature using reactive magnetron sputtering. Based on transmission electron microscopy, Sn<sub>0.6</sub>O<sub>0.4</sub> thin films form dendrites featuring nanosized Sn and SnO grains, rather than columns, which are not captured by the structure zone diagram. Using density functional theory and machine learning, we constructed a model to explain this unusual microstructure on the atomic scale. Kinetically limited surface diffusion yields SnO islands on Sn(001), which constitute the initial stage of dendrite formation. This study provides the potential to devise models for thin film microstructure evolution, enhancing performance in advanced applications, such as green energy generation and storage. Microstructure ; Thin film ; Materials science ; Transmission electron microscopy ; Diagram ; Sputter deposition ; Nanotechnology ; Tin ; Density functional theory ; Sputtering ; Chemical engineering ; Chemical physics ; Composite material ; Metallurgy ; Computer science ; Chemistry ; Computational chemistry ZnO doping and properties ; Copper-based nanomaterials and applications ; Gas Sensing Nanomaterials and Sensors
Authors: Ikuma Kohata | Kaoru Hisama | Keigo Otsuka | Shigeo Maruyama
Source: Physical Review Materials (article, 2025-07-28)
Field: Materials Science — Materials Chemistry

Machine-learned bond-order potential for exploring the configuration space of carbon Construction of transferable machine-learning interatomic potentials with a minimal number of parameters is important for their general applicability. Here, we present a machine-learning interatomic potential with the functional form of the bond-order potential for comprehensive exploration over the configuration space of carbon. The physics-based design of this potential enables robust and accurate description over a wide range of the potential energy surface with a small number of parameters. We demonstrate the versatility of this potential through validations across various tasks, including phonon dispersion calculations, global structure searches for clusters, phase diagram calculations, and enthalpy-volume mappings of local minima structures. We expect that this potential can contribute to the discovery of novel carbon materials. Materials science ; Space (punctuation) ; Order (exchange) ; Bond order ; Carbon fibers ; Bond ; Chemical physics ; Nanotechnology ; Engineering physics ; Bond length ; Crystallography ; Composite material ; Computer science ; Business ; Engineering ; Physics Machine Learning in Materials Science ; Crystallography and molecular interactions ; X-ray Diffraction in Crystallography
Authors: Xinping Feng | You Xu | Jing Huang
Source: Communications in Computational Chemistry (article, 2025-06-09)
Field: Materials Science — Materials Chemistry

Benchmarking Universal Machine Learning Force Fields with Hydrogen-Bonding Cooperativity Machine learning force fields (MLFFs) offer a promising balance between quantum mechanical (QM) accuracy and molecular mechanics efficiency. While MLFFs have shown strong performance in modeling short-range interactions and reproducing potential energy surfaces, their ability to capture long-range cooperative effects remains underexplored. In this study, we assess the ability of three MLFF models — ANI, MACE-OFF, and Orb — to reproduce cooperative interactions arising from environmental induction and dispersion, which are essential for many biomolecular processes. Using a recently proposed framework, we quantify hydrogen bond (H-bond) cooperativity in N-methylacetamide polymers. Our results show that all MLFFs capture cooperativity to some extent, with MACE-OFF yielding the closest agreement with QM data. These findings highlight the importance of evaluating many-body effects in MLFFs and suggest that H-bond cooperativity can serve as a useful benchmark for improving their physical fidelity. Benchmarking ; Cooperativity ; Hydrogen bond ; Computer science ; Mechanical engineering ; Nanotechnology ; Materials science ; Engineering ; Chemistry ; Physics ; Molecule ; Management ; Economics ; Quantum mechanics Machine Learning in Materials Science
Authors: Yufan Huang | Jinlei Qi | Bingrui An | B.-S. Zhang | Yong Yang | Cheng Cheng | Bingfang He
Source: Frontiers in Bioengineering and Biotechnology (article, 2025-04-24)
Field: Materials Science — Biomaterials

High-yield spidroin mimics for bioinspired fibers via computational design The exceptional mechanical properties, biocompatibility, and biodegradability of spider silk make it a promising biomaterial, yet large-scale production remains hindered by challenges in heterologous expression. Existing prokaryotic systems face bottlenecks due to spidroins' high molecular weight, repetitive sequences, and GC-rich motifs, leading to low yields, premature transcription termination, and insoluble inclusion bodies. Addressing these challenges, the study integrates deep learning and bioengineering to design water-soluble, β-sheet-rich spidroin mimics optimized for efficient prokaryotic expression. By replacing polyalanine motifs in <i>Nephila clavipe</i>s MaSp1 with computationally screened sequences (e.g., ITVQQ from <i>Burkholderia</i> OspA), five functional spidroins were engineered and solubly expressed in <i>E. coli</i>, achieving yields up to 0.99 g/L. Circular dichroism revealed that modified spidroins (e.g., 3rep-ITVQQ) exhibited β-sheet content up to 81.3% under thermal induction, surpassing unmodified MaSp1 (41.5%). Structural analysis via SEM demonstrated dense, uniform networks in 3rep-ITVQQ, correlating with enhanced mechanical potential. And 24rep-ITVQQ nanofibers were successfully prepared by electrostatic spinning. Coarse-grained molecular dynamics simulations validated progressive self-assembly with reduced solvent-accessible surface area over 1,000 ns. This work bridges the gap between sequence design and scalable production by overcoming expression barriers, simplifying purification, and leveraging β-sheet stacking for tunable mechanical properties. The results provide a blueprint for high-performance biomimetic fibers, advancing applications (e.g., surgical sutures, scaffolds) in tissue engineering and functional materials while addressing the limitations of conventional spidroin production systems. Yield (engineering) ; Biochemical engineering ; Materials science ; Engineering ; Composite material Silk-based biomaterials and applications ; Biochemical and Structural Characterization
Authors: G. Alexakis | Marina Pellegrino | Laura Rodríguez-Turienzo | Michail Maniadakis
Source: Recycling (article, 2025-09-22)
Field: Chemistry — Analytical Chemistry

Enhanced Waste Sorting Technology by Integrating Hyperspectral and RGB Imaging Identifying the material composition of objects is crucial for many recycling sector applications. Traditionally, object classification relies either on hyperspectral imaging (HSI), which analyses the chemometric properties of objects to infer material types, or on RGB imaging, which captures an object’s visual appearance and compares it to a reference sample. While both approaches have their strengths, each also suffers from limitations, particularly in challenging scenarios such as robotic municipal waste sorting, where objects are often heavily deformed or contaminated with various forms of dirt, complicating material recognition. This work presents a novel method for material-based object classification that jointly exploits HSI and RGB imaging. The proposed approach aims to mitigate the weaknesses of each technique while amplifying their respective advantages. It involves the real-time alignment of HSI and RGB data streams to ensure reliable result correlation, alongside a machine learning framework that learns to exploit the strengths and compensate for the weaknesses of each modality across different material types. Experimental validation on a municipal waste sorting facility demonstrates that the combined HSI–RGB approach significantly outperforms the individual methods, achieving robust and accurate classification even in highly challenging conditions. Hyperspectral imaging ; Sorting ; RGB color model ; Exploit ; Modality (human–computer interaction) ; Object (grammar) Spectroscopy and Chemometric Analyses ; Remote-Sensing Image Classification ; Industrial Vision Systems and Defect Detection
Authors: Leandro Rodrigues Oviedo | Daniel Moro Druzian | Sthéfany Nunes Loureiro | Alencar Kolinski Machado | Taíse Regina Schuster Montagner | Giovani Pavoski | Denise Crocce Romano Espinosa | Yolice Patricia Moreno Ruiz | Lissandro Dorneles Dalla Nora | William Leonardo da Silva
Source: Journal of Molecular Liquids (article, 2025-09-08)
Field: Materials Science — Materials Chemistry

A novel nanocomposite based on sodalite and green copper nanoparticles: In vitro safety profile, ecotoxicity and deep learning study for photodegradation of a binary mixture of dyes Photodegradation ; Sodalite ; Crystal violet ; Nanocomposite ; Copper ; Methylene blue ; Nanomaterials ; Nanoparticle Nanoparticles: synthesis and applications
Authors: Chengmin Deng | Mingjie Zheng | Pinghan Chen | Xiaolong Li | Jie Xiong | A. Shahboub | Lin Luo
Source: Journal of Nuclear Materials (article, 2025-06-21)
Field: Materials Science — Metals and Alloys

An interpretable machine learning model for oxidation corrosion prediction of ferritic/martensitic steels in LBE Corrosion ; Martensite ; Metallurgy ; Materials science ; Microstructure Hydrogen embrittlement and corrosion behaviors in metals ; Non-Destructive Testing Techniques ; Corrosion Behavior and Inhibition
Authors: Pravin D. Patil | Niharika Gargate | Manishkumar S. Tiwari | Shamraja S. Nadar
Source: International Journal of Biological Macromolecules (review, 2025-09-04)
Field: Chemistry — Inorganic Chemistry

Compartmentalization of multi-enzymes within designable metal-organic frameworks (MOF): A review of systematic approaches Compartmentalization (fire protection) ; Metal-organic framework ; Nanotechnology ; Chemistry ; Biochemical engineering ; Enzyme ; Materials science ; Biochemistry ; Engineering ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Enzyme Catalysis and Immobilization ; Advanced Nanomaterials in Catalysis
Authors: A. K. Nair | Carlos Da Silva | Cristina H. Amon
Source: The Journal of Physical Chemistry C (article, 2025-07-24)
Field: Materials Science — Materials Chemistry

Predicting Interfacial Thermal Conductance and Thermal Conductivity across Multilayer TiS<sub>2</sub>/MoS<sub>2</sub> van der Waals Heterostructures Using Moment Tensor Potentials Thermal conductivity calculations using classical molecular dynamics (MD) simulations are significantly influenced by the selection of accurate interatomic potentials. However, these interatomic potentials are not readily available for several novel two-dimensional (2D) materials and their heterostructures. Here, we propose a machine learning interatomic potential (MLIP) and D3-dispersion correction approach to determine the out-of-plane thermal conductivity and interfacial thermal conductance of multilayer TiS2/MoS2 heterostructures. We employ an MLIP-based moment tensor potential (MTP) to characterize the intralayer interactions within layers, while the interlayer interactions are characterized using the D3-dispersion correction. This approach demonstrates greater accuracy compared with Lennard-Jones-based potentials for interlayer interactions. Furthermore, this work uses a simplified supercell of the TiS2/MoS2 heterostructure and cost-effective ab initio molecular dynamics (AIMD) simulations with a duration of less than 1 ps. Additionally, this work investigates the effects of tuning the ratio of TiS2 to MoS2 layers on the interfacial thermal conductance and the overall out-of-plane thermal conductivity of the multilayer heterostructure. This MLIP-based methodology provides a reliable approach for estimating the thermal properties of multilayer heterostructures. van der Waals force ; Thermal conductivity ; Materials science ; Heterojunction ; Thermal ; Condensed matter physics ; Conductance ; Tensor (intrinsic definition) ; Moment (physics) ; Thermodynamics ; Physics ; Molecule ; Composite material ; Quantum mechanics Thermal properties of materials ; Machine Learning in Materials Science ; 2D Materials and Applications
Authors: S. Guo | Jinchang Li | Wei Jiang | Jun Yang | Yingying Du | Tao Luo | Ayesha Anwar | Limei Qi
Source: Optics & Laser Technology (article, 2025-09-25)
Field: Chemistry — Spectroscopy

Chirality recognition of amino acid by combining machine learning method and sliding window technique Chiral molecule recognition is crucial in life sciences, pharmaceuticals, disease diagnostics, and environmental protection. Infrared spectrum is a powerful tool for identifying different molecules, which can be used to detect the characteristic transmission peaks associated with molecule’s functional groups. However, the complexity of infrared spectrum limits the chirality recognition of amino acid through the traditional analytical techniques. In this work, we propose a method to distinguish three pairs of L– and D–amino acids based on their infrared spectrum by combining machine learning method and sliding window. Three machine learning (ML) algorithms: error-correcting output codes-support vector machine (ECOC-SVM), principal component analysis-random forest (PCA-RF), and partial least squares-discrimination analysis (PLS-DA) are used to recognize three pairs of chiral amino acids including alanine (Ala), cysteine (Cys), and glutamine (Gln), respectively. Results revealed that the sensitivity of chiral recognition varies significantly with spectral region, window size, and step length. Experimental findings highlight the precise chiral recognition of amino acids by ML-assisted infrared spectrum, advancing its applications in analytical chemistry, biomedicine, and pharmaceutical sciences. Support vector machine ; Amino acid ; Chirality (physics) ; Principal component analysis ; Sliding window protocol ; Infrared ; Pattern recognition (psychology) ; Pseudo amino acid composition Molecular spectroscopy and chirality ; Metabolomics and Mass Spectrometry Studies ; Analytical Chemistry and Chromatography
Authors: R Sarankumar | Hashim Elshafie | Azath Mubarakali | Natarajan Ashokkumar
Source: Journal of The Electrochemical Society (article, 2025-05-01)
Field: Materials Science — Materials Chemistry

Hybrid Graphene-MXene-Gold Metasurface Biosensor with Machine Learning Integration for High-Sensitivity Brain Tumor Detection This research introduces a biosensor designed for brain tumor detection, integrating a multilayered architecture composed of T-shaped resonators made from gold and MXene deposited on a graphene substrate. This hybrid nanostructure uniquely combines with advanced machine learning algorithms, establishing a new paradigm in non-invasive brain tumor diagnostics. The sensor achieves an unprecedented sensitivity of 1538 GHz RIU −1 and attains predictive classification accuracy up to 100%, surpassing existing THz biosensing platforms. Electromagnetic simulations conducted in COMSOL Multiphysics investigate the influence of key parameters such as graphene chemical potential, angle of incidence, and resonator dimensions-on sensor performance. The metasurface exhibits distinct resonance frequency shifts within the 0.412–0.433 THz range, enabling clear spectral differentiation among various brain tumor types. A strong linear correlation (R 2 = 95.837%) between refractive index variations and resonance frequency shifts confirms the sensor’s precise refractive index sensing capability. Further enhancement of diagnostic accuracy is realized through a stacking ensemble machine learning model, which integrates multiple classifiers to optimize prediction of sensor responses, achieving up to 100% classification accuracy. This synergistic integration of advanced materials and artificial intelligence provides a robust framework for early, non-invasive, and highly accurate brain tumor detection, addressing critical limitations inherent in conventional neuroimaging techniques. Graphene ; Biosensor ; Sensitivity (control systems) ; Nanotechnology ; Materials science ; Optoelectronics ; Electronic engineering ; Engineering MXene and MAX Phase Materials ; Advanced Memory and Neural Computing ; Advanced biosensing and bioanalysis techniques
Authors: Amir Omranpour | Jörg Behler
Source: ChemCatChem (article, 2025-11-29)
Field: Materials Science — Materials Chemistry

Insights into the Structure and Dynamics of Water at Co3O4${\rm Co}_3{\rm O}_4$(001) Using a High‐Dimensional Neural Network Potential Abstract is an important catalyst for the oxidation of organic molecules in the liquid phase. Still, understanding the atomistic details of –water interfaces under operando conditions remains extremely challenging. While ab initio molecular dynamics have become an essential tool for investigating these dynamic interfaces in silico, they are limited to only a few picoseconds and a few hundred atoms. In this work, we overcome these limitations by training a high‐dimensional neural network potential (HDNNP) on density functional theory data, which allows us to significantly extend the accessible time and length scales. Employing this HDNNP, we perform simulations to unravel the structure, dynamics, and reactivity of (001)–water interfaces in detail. Our simulations reveal distinct characteristics of the two possible A and B terminations. The B‐terminated surface stabilizes a compact, quasi‐epitaxial hydration layer with strong templating effects, enhanced hydroxylation, and a well‐organized hydrogen‐bond network. In contrast, the A‐termination forms a more diffuse contact layer with weaker templating, lower hydroxylation, and less ordered interfacial water. Extended simulations further uncover proton transfer pathways, including intermittent protonation of surface hydroxyls, migration of water molecules into the epitaxial layer, and rare hydronium‐like configurations. Picosecond ; Molecular dynamics ; Protonation ; Molecule ; Density functional theory ; Ab initio ; Reactivity (psychology) ; Surface (topology) Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Advanced Chemical Physics Studies
Authors: Minseok Moon | Seungwoo Hwang | Jaesun Kim | Yutack Park | Chang‐Ho Hong | Seungwu Han
Source: ACS Applied Materials & Interfaces (article, 2025-10-01)
Field: Materials Science — Materials Chemistry

Unveiling Defect Motifs in Amorphous GeSe Using Machine Learning Interatomic Potentials Ovonic threshold switching (OTS) selectors are pivotal in nonvolatile memory devices due to their nonlinear electrical characteristics and polarity-dependent threshold voltages. However, the atomic-scale origins of the defect states responsible for these behaviors remain unclear. In this study, we systematically investigate defects in amorphous GeSe using molecular dynamics simulations accelerated by machine learning interatomic potentials (MLIPs). We first benchmark several MLIP architectures, including descriptor-based potentials and graph neural network (GNN)-based potentials. Our results demonstrate that capturing higher-order interactions, at least four-body correlations, and medium-range structural order is essential for accurately representing amorphous GeSe structures. Our analysis indicates that GNN architectures with multiple interaction layers effectively capture higher-order correlations and medium-range order, thereby preventing spurious defects easily introduced by less descriptive MLIPs. Utilizing the optimized GNN model, we identify two distinct defect motifs across 20 independent 960-atom amorphous GeSe structures: aligned Ge chains associated with defect states near the conduction band, and overcoordinated Ge chains linked to defect states near the valence band. Moreover, we establish correlations between electronic defect levels and specific structural features─namely, the average alignment of bond angles in aligned chains and the degree of local Peierls distortion around overcoordinated Ge atoms. These insights provide a theoretical framework for interpreting experimental observations and deepening the understanding of defect-driven OTS phenomena in amorphous GeSe. Amorphous solid ; Valence (chemistry) ; Spurious relationship ; Molecular dynamics ; Artificial neural network ; Distortion (music) ; Interatomic potential Phase-change materials and chalcogenides ; Chalcogenide Semiconductor Thin Films ; Machine Learning in Materials Science
Authors: Anders Ø. Madsen
Source: Acta Crystallographica Section A Foundations and Advances (article, 2025-05-29)
Field: Materials Science — Materials Chemistry

Phase seeding may provide a gateway to structure solution by deep learning The phase-seeding method proposed by Carrozzini et al. [(2025), Acta Cryst. A81, 188-201] introduces a strategy for integrating artificial intelligence (AI) with established ab initio phasing techniques. Rather than presenting an AI-based phasing solution itself, the authors demonstrate how traditional crystallographic methods can be significantly enhanced if provided with a small subset of approximate phase values - a `phase seed' - that could, in principle, be generated by a machine learning model. By discretizing phase values into a few angular bins, the method transforms the continuous phase problem into a classification task, thereby reducing the computational burden on AI training. This hybrid approach shows promise for improving structure solution, particularly for large and complex non-centrosymmetric crystals, and opens a pathway for future AI-assisted crystallographic workflows. Seeding ; Phase (matter) ; Workflow ; Phaser ; Artificial intelligence ; Computer science ; Phase problem ; Task (project management) ; Discretization ; Machine learning ; Algorithm ; Mathematics ; Chemistry ; Physics ; Engineering ; Optics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Enzyme Structure and Function