jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Wuling Zhao (21087305) | Minxia Zhou (21087308) | Jialin Shao (21087311) | Jingzheng Ren (4453594) | Yusha Hu (21087314) | Yulin Han (1643272) | Yi Man (1639591)
Source: Figshare (article, 2025-04-14)
Field: Materials Science — Materials Chemistry

Crystal StructurePrediction Using a Self-AttentionNeural Network and Semantic Segmentation The development of new materials is a time-consuming and resource-intensive process. Deep learning has emerged as a promising approach to accelerate this process. However, accurately predicting crystal structures using deep learning remains a significant challenge due to the complex, high-dimensional nature of atomic interactions and the scarcity of comprehensive training data that captures the full diversity of possible crystal configurations. This work developed a neural network model based on a data set comprising thousands of crystallographic information files from existing crystal structure databases. The model incorporates a self-attention mechanism to enhance prediction accuracy by learning and extracting both local and global features of three-dimensional structures, treating the atoms in each crystal as point sets. This approach enables effective semantic segmentation and accurate unit cell prediction. Experimental results demonstrate that for unit cells containing up to 500 atoms, the model achieves a structure prediction accuracy of 89.78%. Artificial neural network ; Segmentation ; Deep learning ; Set (abstract data type) ; Pattern recognition (psychology) ; Data set ; Network structure ; Crystal structure Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Crystallography and molecular interactions
Authors: Fallah Mehrdad
Source: Open MIND (article, 2025-02-13)
Field: Materials Science — Materials Chemistry

Machine Learning-Assisted Conditional Optimization of Displacement Reaction Pathways for ZnS Nanostructure Engineering Nanostructure ; Displacement (psychology) ; Work (physics) ; Nanoparticle Machine Learning in Materials Science ; Quantum Dots Synthesis And Properties ; Nanowire Synthesis and Applications
Authors: Fallah Mehrdad
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2025-02-13)
Field: Materials Science — Materials Chemistry

Machine Learning-Assisted Conditional Optimization of Displacement Reaction Pathways for ZnS Nanostructure Engineering Nanostructure ; Displacement (psychology) ; Work (physics) ; Nanoparticle Machine Learning in Materials Science ; Quantum Dots Synthesis And Properties ; Nanowire Synthesis and Applications
Authors: Zhenyao Fang (18436723) | Ting-Wei Hsu (9079239) | Qimin Yan (1752490)
Source: Figshare (article, 2025-10-14)
Field: Materials Science — Materials Chemistry

A Machine LearningFramework for Modeling EnsembleProperties of Atomically Disordered Materials Atomic disorder can strongly influence material properties such as charge transport, optical response, and catalytic activity. However, efficiently modeling these disorder effects remains challenging for first-principles methods due to the cost of sampling large configurational spaces and computing complex physical quantities. Recent advances of machine learning techniques, particularly graph neural networks (GNNs), has enabled the efficient and accurate predictions of complex material properties, offering promising tools for studying disordered systems. In this work, we present a general machine-learning-assisted computational framework that integrates equivariant GNNs with Monte Carlo simulations to compute the thermodynamic and ensemble-averaged functional properties of disordered materials. Using the surface-termination-disordered MXene monolayer Ti<sub>3</sub>C<sub>2</sub>T<sub>2–<i>x</i></sub> as a representative system, we find that electrical conductivity exhibits an emergent peak near the order–disorder phase transition temperature due to the interplay between electron scattering and doping. In contrast, optical conductivity remains largely insensitive to local atomic disorder and reflects the global surface chemical composition. These results highlight the role of atomic disorder in affecting material properties and demonstrate the potential of our approach for statistically modeling disorder effects in a wide range of materials such as high-entropy alloys and spin liquids. Monte Carlo method ; Artificial neural network ; Range (aeronautics) ; Ensemble learning ; Multiscale modeling ; Phase transition ; Optical conductivity ; Spin (aerodynamics) Machine Learning in Materials Science ; MXene and MAX Phase Materials ; 2D Materials and Applications
Authors: Sergei Manzhos | Johann Lüder | Golub, Pavlo | Ihara, Manabu
Source: ArXiv.org (preprint, 2025-02-08)
Field: Materials Science — Materials Chemistry

Machine learning-guided construction of an analytic kinetic energy functional for orbital free density functional theory Machine learning (ML) of kinetic energy functionals (KEF) for orbital-free density functional theory (OF-DFT) holds the promise of addressing an important bottleneck in large-scale ab initio materials modeling where sufficiently accurate analytic KEFs are lacking. However, ML models are not as easily handled as analytic expressions; they need to be provided in the form of algorithms and associated data. Here, we bridge the two approaches and construct an analytic expression for a KEF guided by interpretative machine learning of crystal cell-averaged kinetic energy densities (τ) of several hundred materials. A previously published dataset including multiple phases of 433 unary, binary, and ternary compounds containing Li, Al, Mg, Si, As, Ga, Sb, Na, Sn, P, and In was used for training, including data at the equilibrium geometry as well as strained structures. A hybrid Gaussian process regression - neural network (GPR-NN) method was used to understand the type of functional dependence of τ on the features which contained cell-averaged terms of the 4th order gradient expansion and the product of the electron density and Kohn-Sham effective potential. Based on this analysis, an analytic model is constructed that can reproduce Kohn-Sham DFT energy-volume curves with sufficient accuracy (pronounced minima that are sufficiently close to the minima of the Kohn-Sham DFT-based curves and with sufficiently close curvatures) to enable structure optimizations and elastic response calculations. Density functional theory ; Orbital-free density functional theory ; Kinetic energy ; Energy (signal processing) ; Computer science ; Physics ; Time-dependent density functional theory ; Classical mechanics ; Quantum mechanics Machine Learning in Materials Science
Authors: Wenqiang Gao (8399652) | Ziao Chen (8103659) | Jiaxin Hong (6939563) | YinYue Zhang (21190909) | Zhao Yang (358244) | Minghui Liu (337202) | Xinyu Wang (341591) | Shengcong Shang (14525727) | Zewen You (14846437) | Zhihao Shao (19499524) | Jichen Dong (1747687) | Yunlong Guo (1292445) | Jianyi Chen (1964995) | Yunqi Liu (1423372)
Source: Figshare (article, 2025-04-28)
Field: Materials Science — Materials Chemistry

RapidSynthesis of Single-Crystal Covalent OrganicFramework with Controllable Crystal Habits Covalent organic frameworks (COFs) linked by poorly reversible covalent bonds lack dynamic formation and cleavage, so the synthesis of their single-crystal structures necessitates slow crystallization rates to mitigate defect formation. This, however, inherently restricts opportunities for facet-selective engineering beyond traditional compositional and topological controls. To address this fundamental limitation, we developed an acetal/CH<sub>3</sub>COOH protocol that paradoxically accelerated crystallization while enhancing structural perfection, reducing the synthesis time for 60 μm-sized single-crystal COF-300 to 1 h, while achieving crystal sizes of up to 120 μm within 48 h, and 300 μm after 30 days. Capitalizing on this accelerated synthesis platform, we systematically interrogated crystallization landscapes through multiparameter explorationmodulator chemoselectivity, catalyst dosages, temporal evolution, and reactive temperatureto decode possible growth mechanisms of single-crystal COFs. Based on these, the relationship between reaction conditions and the crystal size, size distribution, shape, and growth dynamics of single-crystal COFs was trained and predicted by a machine learning (ML) model. Crystallization ; Covalent bond ; Crystal (programming language) ; Catalysis ; Crystal growth ; Crystal engineering Covalent Organic Framework Applications ; Advanced Photocatalysis Techniques ; Catalysis for Biomass Conversion
Authors: Kağan Şarlar
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine learning screening of magnetocaloric high-entropy alloys using a field-normalized ΔSref target Magnetic refrigeration ; Noise (video) ; Pattern recognition (psychology) ; Process (computing) Magnetic and transport properties of perovskites and related materials ; Machine Learning in Materials Science
Authors: H. Guler | Rasim Özdemir | Adem Coşkun
Source: Journal of Materials Science (article, 2025-09-18)
Field: Materials Science — Materials Chemistry

Deep learning-based prediction of magnetic properties in electrodeposited Co–Ni alloy thin films Thin film ; Alloy ; Solid mechanics ; Microstructure ; Polymer Machine Learning in Materials Science ; Electrodeposition and Electroless Coatings ; Welding Techniques and Residual Stresses
Authors: Valentín Vassilev-Galindo | Javier LLorca
Source: ChemRxiv (preprint, 2025-07-21)
Field: Materials Science — Materials Chemistry

Explainable artificial intelligence for materialsdiscovery: application to catalysts for the HERand ORR The extraordinary progress of strategies coupling ab initio calculations and machine learning (ML) has opened the door for both fast and accurate chemical/physical properties predictions and for the virtual design of materials. However, these techniques are very often used as a "black box" with the sole objective of obtaining high accuracy with scarce or no special attention on how ML models obtain their predictions. This can be improved by leveraging explainability of ML models, which, at the same time, would increase the chance of ML to offer new insights into the chemistry and physics of materials. Hence, the next generation of ML models in these realms must guarantee explainability by embedding explainable artificial intelligence (XAI) tools into their pipelines. Specifically, ML-assisted materials discovery and design can take great advantage of the use of XAI. Enabling explanations would increase the impact of these approaches by providing not only a set of candidates, but also insights into what makes a given material better than others. With this in mind, on the example of heterogeneous catalysts for the hydrogen production and energy generation, here we propose a novel strategy for materials design based on counterfactuals explanations. We were able to find materials featuring properties close to the design targets that were later validated with density functional theory calculations. Explanations were devised by comparing original samples, counterfactuals, and discovered candidates. Such explanations allowed us to unveil subtle relationships between the most relevant features, other, in principle, less important features, and the target property. This work provides an alternative to already available designing strategies, such as high-throughput screening or generative models, but that, for the first time, incorporates explainability as its main driving mechanism. Catalysis ; Computer science ; Data science ; Artificial intelligence ; Chemistry ; Organic chemistry Machine Learning in Materials Science
Authors: Safoura Vaez (21223576) | Diba Shahbazi (21223579) | Meike Koenig (4415803) | Matthias Franzreb (1443262) | Joerg Lahann (811789)
Source: Figshare (article, 2025-04-30)
Field: Materials Science — Materials Chemistry

Deep LearningBased Surface Classification of FunctionalizedPolymer Coatings Low-technology characterization of material surfaces poses a challenge of significant importance for many scientific fields such as medical implants, biosensors, and regenerative medicine. Simple, fast, and scalable surface analysis methods that can be applied to a wide range of functionalized polymer coatings would thus constitute a major scientific and technological advance. In this work, we studied stain patterns formed by depositing a defined protein solution onto various polymer surfaces. The images of the resulting drying droplet patterns were captured by polarized light microscopy and analyzed by a deep-learning neural network. In this proof-of-concept study, we used chemical vapor deposition polymerization to deposit ten structurally distinct polymer coatings that share an identical polymer backbone, but differ in their functional groups. Despite the relatively minute differences in their chemical structure, the CNN classification of the stain patterns was highly reproducible. Across all different polymers, the overall classification accuracy of the CNN was 96%. When challenging the CNN with images from an unknown polymer coating, i.e., poly[(4-bromo-<i>p</i>-xylylene)-<i>co</i>-(<i>p</i>-xylylene)], these surfaces were classified as halogenated or pseudohalogenated coatings with 95% accuracy. These findings confirm that the scope of surfaces that can be analyzed with this approach goes beyond polymer coatings already known to the CNN through the training procedure and validates the method as a simple, yet versatile surface analysis tool. Polymer ; Polymerization ; Chemical vapor deposition ; Characterization (materials science) ; Deep learning ; Deposition (geology) ; Microscopy ; Surface (topology) Machine Learning in Materials Science ; Nanofabrication and Lithography Techniques ; Polymer Surface Interaction Studies
Authors: Wenqi Guo | Yiyang Du | Mohamed Shehata
Source: ChemRxiv (preprint, 2025-03-19)
Field: Materials Science — Materials Chemistry

3D2SMILES: Translating Physical Molecular Models into Digital DeepSMILES Notations Using Deep Learning Physical molecular models are widely used in educational settings for teaching organic and other branches of chemistry, offering an intuitive understanding of molecular structures. Conversely, while less intuitive, virtual models provide additional functionalities, such as retrieving molecular names and other properties. Currently, to the best of our knowledge, there is a gap between 3D molecular models and their digital counterparts. This paper introduces a computer vision model designed to bridge this gap by converting images of physical molecular models into their digital DeepSMILES representations. This conversion facilitates further information retrieval, enhancing educational utility. We developed synthetic and real datasets to train our model and evaluated its performance across various dataset combinations. Additionally, we attempted to improve the model’s accuracy by multi-image input and beam search. We achieved 62.0% top1 accuracy and 80.3% top-3 accuracy with beam search and multi-image input on our validation set. We also explored the model’s characteristics, such as explainability by saliency maps, and examined its calibration. We also discussed the model’s limitations and directions for future research. Notation ; Computer science ; Deep learning ; Artificial intelligence ; Natural language processing ; Mathematics ; Arithmetic Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Electron and X-Ray Spectroscopy Techniques
Authors: Md Mahbubul Islam
Source: ECS Meeting Abstracts (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

<i>(Invited)</i> Computational Insights into Cathode Electrocatalyst Design for Metal-CO <sub>2</sub> Batteries Carbon dioxide (CO₂) is a major greenhouse gas contributing significantly to global warming and environmental imbalance. Among emerging energy technologies aimed at addressing this challenge, metal–CO₂ batteries have garnered considerable attention due to their unique ability to harness green electricity through CO2 consumption. In particular, magnesium–CO₂ (Mg–CO₂) batteries hold great promise owing to magnesium's high volumetric capacity, natural abundance, and low cost. Despite their potential, the practical realization of Mg–CO₂ batteries is hindered by several critical challenges, including sluggish CO₂ reduction reaction kinetics, poor reversibility during charge–discharge cycles, and high charge overpotentials. Addressing these obstacles necessitates the rational design of efficient cathode electrocatalysts, guided by fundamental insights into reaction mechanisms and interfacial processes. We employ first-principles density functional theory (DFT) simulations to design and evaluate cathode electrocatalysts aimed at overcoming the sluggish kinetics of CO₂ reduction by unraveling the underlying interfacial mechanisms. Our investigation spans a broad spectrum of catalytic materials, including metal oxides, carbides, and two-dimensional layered MXenes, with a particular focus on how their electronic structures influence catalytic activity. We also explore the potential of atomically dispersed metal atoms—commonly referred to as single-atom catalysts (SACs)—in modulating CO₂ reduction chemistry. Through detailed analysis of reaction free energy profiles, we study the entire CO₂ reduction pathway and quantify charge and discharge overpotentials. To complement our atomistic insights, we integrate machine learning (ML) techniques to uncover the structure–activity relationships and inform the rational design of next-generation electrocatalysts for high-performance metal–CO₂ batteries. Rational design ; Electrocatalyst ; Cathode ; Reduction (mathematics) ; Design elements and principles ; Constraint (computer-aided design) ; Realization (probability) Machine Learning in Materials Science ; Ammonia Synthesis and Nitrogen Reduction ; MXene and MAX Phase Materials
Authors: Yuhang Lu (17355972) | Yibo Sun (6114593) | Cong Hou (3434765) | Zimo Li (7504784) | Jun Ni (68173)
Source: Figshare (article, 2025-06-23)
Field: Materials Science — Materials Chemistry

Explainable AttentionCNN for Predicting Propertiesof Heusler Alloys Machine learning implemented with multiple architectures exhibits a superior predictive capability. The spatial attention mechanism efficiently extracts position-related information and inherently aligns with the physical rules governing such properties. We applied the spatial attention mechanism to investigate material properties and developed a deep learning model termed Attention Convolutional Neural Networks (CNN). This model demonstrated remarkable performance for Heusler alloys, achieving <i>R</i><sup>2</sup> values of 0.9988 for ground state energy (<i>E</i><sub>0</sub>) and 0.9690 for the energy above the convex hull (<i>E</i><sub>hull</sub>), alongside a 27.9% reduction in mean square error compared to standard CNN. Attention CNN also offers significant interpretability. The embeddings for magnetic moment (<i>m</i><sub>s</sub>) elucidate atomic characteristics, reflecting the electron configuration and positions within the periodic table. Analysis of the elemental masked attention matrix reveals the contributions from individual elements and the relationships between elements. The single-element contribution analysis concurs with findings from embeddings, while the analysis of two-element contributions is consistent with the Slater–Pauling rule. Moreover, using Attention CNN, we discover a few compounds (LiClMnPt, TiCoMnIr, TiCoFeIr, and TiRhMnIr) with high saturated magnetic moment ranking in potentially metastable Heulser alloys. Metastability ; Moment (physics) ; Convolutional neural network ; Ranking (information retrieval) ; Deep learning ; Magnetic moment ; Convex hull ; Energy (signal processing) Machine Learning in Materials Science ; Heusler alloys: electronic and magnetic properties ; Thermal Expansion and Ionic Conductivity
Authors: Anna Mäkelä | Jussi Virtanen | Sami Koskinen
Source: International Journal of Materials Science (article, 2025-07-01)
Field: Materials Science — Materials Chemistry

Experimental and computational insights into materials for next-generation technologies The rapid evolution of next-generation technologies ranging from renewable energy systems and quantum devices to high-performance structural applications has placed materials science at the forefront of scientific and industrial innovation. This study integrates experimental investigations with computational modeling to provide a holistic understanding of advanced materials, focusing on their structural, electronic, and functional properties. The primary objective is to bridge the gap between empirical observations and theoretical predictions, enabling the design of materials with tailored functionalities. Experimental techniques, including X-ray diffraction, scanning electron microscopy, and mechanical testing, were employed to characterize microstructures, defect distributions, and mechanical responses under diverse conditions. Complementary computational approaches, such as density functional theory (DFT), molecular dynamics simulations, and machine learning-assisted property predictions, were used to probe electronic structures, dynamic behavior, and performance optimization.The findings reveal significant correlations between atomic-scale configurations and macroscopic behaviors, demonstrating how hybrid approaches accelerate materials discovery. High-entropy alloys exhibited superior strength-to-weight ratios compared to conventional alloys, while two-dimensional materials such as graphene and MoS? showed unprecedented electronic tunability validated by computational predictions. Data-driven algorithms further highlighted the efficiency of predictive models in narrowing the search space for optimal compositions, reducing experimental iterations. The synthesis of insights from both experimental and computational domains underscores the transformative potential of interdisciplinary strategies in addressing technological challenges. This work not only elucidates fundamental mechanisms governing material performance but also sets a precedent for developing innovative frameworks that will guide the deployment of next-generation technologies. Computational model ; Software deployment ; Transformative learning ; Work (physics) ; Graphene ; Bridge (graph theory) ; Characterization (materials science) ; Nanomanufacturing Boron and Carbon Nanomaterials Research ; Ferroelectric and Piezoelectric Materials ; Advanced Thermoelectric Materials and Devices
Authors: Qizhen Du | Feng Wang | Chengkun Wu | Handong Wang | Yongpeng Liu | Zhaoyin Zhou | Kenli Li
Source:  (article, 2025-09-02)
Field: Materials Science — Materials Chemistry

Scaling Deep Learning Molecular Dynamics to 500M Atoms on 4096-Node ARMv8 Clusters Molecular dynamics (MD) simulations are essential tools for investigating large-scale molecular systems, yet achieving high performance and scalability on CPU-based architectures remains challenging. In this study, we present a highly optimized framework based on DeepMD-kit for conducting 500 millionatom MD simulations on an ARMv8 SVE high-performance computing (HPC) system. Key optimizations include leveraging OpenMP for multi-threaded acceleration of DeepMD-kit and utilizing the ARMv8 SVE instruction set to optimize doubleprecision matrix multiplication in PyTorch. These enhancements enable single ARMv8 SVE 64-core processors to achieve 1.3x the training performance of NVIDIA V100 GPU, and two ARMv8 SVE 64-core processors to achieve 1.05x the inference performance of NVIDIA V100 GPU. Leveraging this optimized framework, we achieve large-scale MD simulations across 4,096 computing nodes. Scalability ; Scaling ; Acceleration ; Molecular dynamics ; Set (abstract data type) ; Key (lock) ; Supercomputer ; Deep learning Machine Learning in Materials Science ; Nuclear Physics and Applications ; Electron and X-Ray Spectroscopy Techniques