jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yuuki Ishiwatari | T. Yokoyama | Tomoya Kojima | Taisuke Banno | Noriyoshi Arai
Source: arXiv (Cornell University) (preprint, 2025-12-16)
Field: Materials Science — Materials Chemistry

Composition-agnostic prediction of self-assembly in multicomponent amphiphile mixtures from molecular structure Predicting self-assembly in multi-component amphiphilic systems is challenging due to the complexity of intercomponent interactions and the combinatorial growth of possible formulations. In this study, we develop a unified machine-learning framework that directly predicts self-assembly behavior from the molecular structures of constituent components, independent of the number or identity of those components. We extend the critical packing parameter (CPP) to multi-component systems and generate a large dataset of self-assembled morphologies using dissipative particle dynamics (DPD) simulations. By systematically evaluating twelve combinations of feature extraction methods and model architectures, we find that models incorporating a fully connected graph convolutional network (GCN) layer achieve superior performance, with the GCN-GCN architecture accurately capturing both intramolecular relationships and intercomponent interactions. Notably, this model exhibits strong extrapolative capability: it accurately predicts CPP values for five-component mixtures even when trained only on systems with fewer components, and it maintains high accuracy for mixtures composed of molecular species that are entirely absent from the training data. These results demonstrate that a composition-agnostic predictive framework can enable efficient virtual screening and provide a foundation for the rational design of complex amphiphilic materials. Dissipative particle dynamics ; Amphiphile ; Intramolecular force ; Dissipative system ; Molecular dynamics ; Graph ; Interpretability ; Feature (linguistics) Block Copolymer Self-Assembly ; Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization
Authors: Felipe Hawthorne (21705966) | Paulo R. E. Raulino (21705969) | Ronaldo Rodrigues Pelá (21705972) | Cristiano F. Woellner (4350652)
Source: Figshare (article, 2025-07-12)
Field: Materials Science — Materials Chemistry

Efficient and AccurateMachine Learning InteratomicPotential for Graphene: Capturing Stress–Strain and VibrationalProperties Machine learning interatomic potentials (MLIPs) offer an efficient and accurate framework for large-scale molecular dynamics (MD) simulations, effectively bridging the gap between classical force fields and <i>ab initio</i> methods. In this work, we present a reactive MLIP for graphene, trained on an extensive data set generated via <i>ab initio</i> molecular dynamics (AIMD) simulations performed using the local density approximation (LDA) exchange–correlation functional and the projector-augmented wave (PAW) method. The model accurately reproduces key mechanical and vibrational properties, including stress–strain behavior, elastic constants, phonon dispersion, and the vibrational density of states. Notably, it captures temperature-dependent fracture mechanisms and the emergence of linear acetylenic carbon chains upon tearing. The phonon analysis also reveals the expected quadratic ZA mode and excellent agreement with experimental and density functional theory (DFT) benchmarks. Our MLIP scales linearly with system size, enabling simulations of large graphene sheets with <i>ab initio</i>-level precision. This work delivers a robust and transferable MLIP, alongside an accessible training workflow that can be extended to other materials. Bridging (networking) ; Phonon ; Density functional theory ; Molecular dynamics ; Quadratic equation ; Interatomic potential ; Work (physics) ; Molecular vibration ; Graphene Machine Learning in Materials Science ; Graphene research and applications ; Boron and Carbon Nanomaterials Research
Authors: Jiankai Wang (12055308) | Lei Rao (4797150) | Lihai Xu (20495536) | Shuhui Tan (12049217) | Xi Chen (35903) | Jun Tang (32983) | Wei Chen (23863) | Haodong Tang (4218067)
Source: Figshare (article, 2025-07-02)
Field: Materials Science — Materials Chemistry

A Search-and-VerificationFramework for EfficientAnnealing Optimization in PbS Quantum Dot Films Precise annealing is vital for achieving high performance in solution-processed PbS quantum-dot (QD) devices, yet conventional methods such as one-variable-at-a-time, full factorial design, and standard machine-learning schemes require large data sets and separate validation steps. Here we report an integrated “search-and-verification” framework that couples ant-colony optimization with hill climbing (ACO-HC) and feeds real-time experimental results back into the algorithm for precise annealing parameter prediction. Starting from a coarse large-spacing factorial design, the ACO-HC method narrows the search space iteratively to 1 °C and 1 min resolution without exhaustive screening. Applied to layer-by-layer PbS QD photoconductors, ACO-HC identifies an annealing condition of 109 °C for 32 min that boosts responsivity by 265% relative to the unannealed baseline and outperforms the best large-spacing factorial point (110 °C, 30 min). Compared with the dense small-spacing full factorial design method, ACO-HC cuts experimental time and material consumption by up to 99%. This universal, scalable strategy provides an efficient route for multiparameter process optimization and holds strong potential to accelerate the development of next-generation solution-processed semiconductor technologies. Annealing (glass) ; Factorial experiment ; Simulated annealing ; Fractional factorial design ; Hill climbing ; Factorial ; Quantum annealing ; Design of experiments Quantum Dots Synthesis And Properties ; Machine Learning in Materials Science ; Nanowire Synthesis and Applications
Authors: Yuuki Ishiwatari | T. Yokoyama | Tomoya Kojima | Taisuke Banno | Noriyoshi Arai
Source: ArXiv.org (article, 2025-12-16)
Field: Materials Science — Materials Chemistry

Composition-agnostic prediction of self-assembly in multicomponent amphiphile mixtures from molecular structure Predicting self-assembly in multi-component amphiphilic systems is challenging due to the complexity of intercomponent interactions and the combinatorial growth of possible formulations. In this study, we develop a unified machine-learning framework that directly predicts self-assembly behavior from the molecular structures of constituent components, independent of the number or identity of those components. We extend the critical packing parameter (CPP) to multi-component systems and generate a large dataset of self-assembled morphologies using dissipative particle dynamics (DPD) simulations. By systematically evaluating twelve combinations of feature extraction methods and model architectures, we find that models incorporating a fully connected graph convolutional network (GCN) layer achieve superior performance, with the GCN-GCN architecture accurately capturing both intramolecular relationships and intercomponent interactions. Notably, this model exhibits strong extrapolative capability: it accurately predicts CPP values for five-component mixtures even when trained only on systems with fewer components, and it maintains high accuracy for mixtures composed of molecular species that are entirely absent from the training data. These results demonstrate that a composition-agnostic predictive framework can enable efficient virtual screening and provide a foundation for the rational design of complex amphiphilic materials. Dissipative particle dynamics ; Amphiphile ; Intramolecular force ; Dissipative system ; Molecular dynamics ; Graph ; Interpretability ; Feature (linguistics) Block Copolymer Self-Assembly ; Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization
Authors: Brian H. Toby
Source: Structural Dynamics (article, 2025-09-01)
Field: Materials Science — Materials Chemistry

Done before needed: The infrastructure that made crystallography so popular for machine learning Crystallography, like all other areas of science, attempts to create a body of knowledge. In our case this concerns the structure of the crystalline and more recently, non-crystalline substances, that are found in biological materials, in the earth and space, and those we create for purposes of improving health, commerce or just curiosity. One area where crystallographers have excelled is at curating and making that information available, developing standardized ways of communicating measurements and results, and providing software tools. This, plus the great importance that chemical/biochemical structure offers for understanding the physics of how a substance performs its actions, has made crystallographic information an early target for applications of machine learning technologies. In a parochial fashion, concentrating more on areas where the speaker has been involved, this presentation will cover where having the right tools before it was clear that they would be needed for “AI” purposes enabled major advances in computation. The unanswered question is: how far will these advances bring us? Presentation (obstetrics) ; Cover (algebra) ; Software ; Data structure Machine Learning in Materials Science
Authors: Amanda Dumi | Shiv Upadhyay | Hassan Harb | Rajeev S. Assary | Dan C. Sorescu | Kenneth D. Jordan | Anouar Benali
Source: The Journal of Physical Chemistry A (article, 2025-11-14)
Field: Materials Science — Materials Chemistry

A Comparison of Electronic Structure Methods for Predicting the Hydrogenation Energies of Candidate Molecules for Hydrogen Storage The development of novel energy materials and fuels is required to expand current available energy sources. Aiming to reach this goal, there is growing interest in using molecular hydrogen as an energy carrier due to its abundance and high energy density. Liquid organic hydrogen carriers (LOHCs) are a promising route to the large-scale storage and transport of hydrogen for use in the energy economy. The search for thermodynamically viable LOHC molecules for real world use has led to a set of constraints on the dehydrogenation enthalpy and the minimum gravimetric hydrogen capacity. These constraints allow one to formulate the search for an ideal LOHC candidate molecule as an optimization problem well suited to the strengths of machine learning and artificial intelligence computational approaches. A critical barrier to a large-scale, high-throughput screening of LOHC candidate molecules is the lack of reliable training data. Computational electronic structure methods including density functional theory, coupled cluster approximations, and diffusion Monte Carlo can be used to provide training data where experimental data are either unreliable or do not exist. In this work, we use these methods to calculate the dehydrogenation energies and enthalpies of candidate LOHC molecules. Dehydrogenation ; Hydrogen storage ; Hydrogen ; Molecule ; Gravimetric analysis ; Density functional theory ; Enthalpy ; Energy carrier Machine Learning in Materials Science ; Crystallography and molecular interactions ; Supramolecular Chemistry and Complexes
Authors: Amir Omranpour | Jörg Behler
Source: ArXiv.org (preprint, 2025-08-30)
Field: Materials Science — Materials Chemistry

Insights into the Structure and Dynamics of Water at Co$_3$O$_4$(001) Using a High-Dimensional Neural Network Potential Co$_3$O$_4$ is an important catalyst for the oxidation of organic molecules in the liquid phase. Still, understanding the atomistic details of Co$_3$O$_4$-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 Co$_3$O$_4$(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 ; Molecule ; Protonation ; Ab initio ; Dynamics (music) ; Layer (electronics) ; Surface (topology) Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Advanced Chemical Physics Studies
Authors: Yaohui Du | Yunpeng Zhang | Xiaopeng Zhu | Jiqiao Liu | Xia Hou | Weibiao Chen
Source: Optics Express (article, 2025-11-20)
Field: Chemistry — Spectroscopy

Coherent high-spectral-resolution lidar for atmospheric temperature remote sensing with a convolutional neural network A 1550-nm coherent high-spectral-resolution lidar (CHSRL) for detecting the atmospheric Rayleigh-Brillouin scattering spectrum (RBS) is proposed, and atmospheric temperature is retrieved by employing a convolutional neural network (CNN). The homodyne detection scheme is adopted to reduce the bandwidth requirement. The impact of the system parameters on the measured RBS line shape is analyzed and simulated, and a molecular scattering data correction method is introduced. Based on the system's power spectrum characteristics, a convolutional neural network is used to extract temperature information from the measured RBS, thereby avoiding the initial value sensitivity and the high signal-to-noise ratio requirement of the conventional nonlinear least squares fitting algorithm. The feasibility of the proposed CHSRL integrated with the CNN for atmospheric temperature measurement is validated by continuous experimental observations with a temperature uncertainty as low as 2.2 K. Lidar ; Atmospheric optics ; Atmospheric temperature ; Scattering ; Convolutional neural network ; Temperature measurement ; Bandwidth (computing) ; Artificial neural network Spectroscopy and Laser Applications ; Advanced Optical Sensing Technologies ; Atmospheric aerosols and clouds
Authors: Matteo Zorzetto | Riccardo Torchio | Francesco Lucchini | Paolo Di Barba | Maria Evelina Mognaschi | Michele Forzan | Fabrizio Dughiero
Source:  (preprint, 2025-07-28)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine Learning-based Reduced Order Modeling of Nonlinear and Multipysics Magnetic Devices Induction heating processes involve complex multiphysical interactions between electromagnetic and thermal models, often characterized by nonlinear materials. The finite element method is a common approach to tackling such problems, but its computational complexity may become a burden for optimization loops or real-time monitoring. This work proposes a surrogate modeling approach that combines Proper Orthogonal Decomposition (POD) and Gaussian Process Regression (GPR), suited for nonlinear and temperature-dependent magnetic materials. The approach is applied to reduce the complexity of the electromagnetic model of the testing electromagnetic analysis method (TEAM) problem 36, consisting of a copper coil heating through induction a steel billet. Results show how the non-intrusive machine learning approach can accurately reconstruct the field distribution, offering a viable first step towards fast multiphysical simulations involving nonlinear magnetic materials. Nonlinear system ; Order (exchange) ; Computer science ; Physics ; Economics Magnetic Properties and Applications ; Electric Motor Design and Analysis ; Neural Networks and Applications
Authors: Thiago Lima da Silva | Fernanda de Fátima da Silva Devechio | Marcos Silva Tavares | Jamile Raquel Regazzo | Edson José de Souza Sardinha | Liliane Maria Romualdo Altão | Gabriel Pagin | Adriano Rogério Bruno Tech | Murilo Mesquita Baesso
Source: AgriEngineering (article, 2025-09-23)
Field: Chemistry — Analytical Chemistry

Machine Learning in the Classification of RGB Images of Maize (Zea mays L.) Using Texture Attributes and Different Doses of Nitrogen Nitrogen fertilization is decisive for maize productivity, fertilizer use efficiency, and sustainability, which calls for fast and nondestructive nutritional diagnosis. This study evaluated the classification of maize plant nutritional status from red, green, and blue (RGB) leaf images using texture attributes. A greenhouse experiment was conducted under a completely randomized factorial design with four nitrogen doses, one maize hybrid Pioneer 30F35, and four replicates, at two sampling times corresponding to distinct phenological stages, totaling thirty-two experimental units. Images were processed with the gray-level cooccurrence matrix computed at three distances 1, 3, and 5 pixels and four orientations 0°, 45°, 90°, and 135°, yielding eight texture descriptors that served as inputs to five supervised classifiers: an artificial neural network, a support vector machine, k nearest neighbors, a decision tree, and Naive Bayes. The results indicated that texture descriptors discriminated nitrogen doses with good performance and moderate computational cost, and that homogeneity, dissimilarity, and contrast were the most informative attributes. The artificial neural network showed the most stable performance at both stages, followed by the support vector machine and k nearest neighbors, whereas the decision tree and Naive Bayes were less suitable. Confusion matrices and receiver operating characteristic curves indicated greater separability for omission and excess classes, with D1 standing out, and the patterns were consistent with the chemical analysis. Future work should include field validation, multiple seasons and genotypes, integration with spectral indices and multisensor data, application of model explainability techniques, and assessment of latency and scalability in operational scenarios. Support vector machine ; Decision tree ; Artificial neural network ; RGB color model ; Pattern recognition (psychology) ; Confusion matrix Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI
Authors: Andreas Niederquell | Barbora Vraníková | Martin Kuentz
Source: Institutional Repository (IHS Vienna) (article, 2025-09-15)
Field: Materials Science — Materials Chemistry

Ranking of apparent drug affinity to mesoporous silica utilizing a chromatographic screening method and a tree-based prediction model Hydrophilic interaction chromatography ; Mesoporous silica ; Ranking (information retrieval) ; Stationary phase ; Hydrogen bond ; Molecular descriptor ; Silica gel ; Phase (matter) Mesoporous Materials and Catalysis ; Analytical Chemistry and Chromatography ; Computational Drug Discovery Methods
Authors: Guoxiang Cui (20987344) | Zikang Guo (11011611) | Xiangyu Ren (9695575) | Yuhang Jiang (2344720) | Xinyu Jin (10940252) | Yunwen Wu (1518415) | Shenghong Ju (115167)
Source: Figshare (article, 2025-04-02)
Field: Materials Science — Materials Chemistry

Active Learning for the Discovery of Binary IntermetallicCompounds as Advanced Interconnects The scaling of advanced integrated circuits has posed significant challenges for traditional Cu interconnects, including increased resistivity and reduced electromigration lifetime. Materials with high cohesive energy and low ρ<sub>0</sub> × λ values are emerging as promising alternatives. In this work, active learning coupling density functional theory (DFT) computation is employed to accelerate the discovery of binary intermetallic compounds for interconnect materials. Following five active learning iterations, 100 compounds are screened out. Among them, the proportion of promising materials reaches an impressive 76%, in sharp contrast to a paltry 4.9% under traditional random screening. Moreover, this research adopts an interpretable machine learning method to provide further physical insights. The Shapley additive explanations (SHAP) analysis revealed that binary intermetallic compounds featuring small cell volumes and similar Mendeleev numbers tend to possess low ρ<sub>0</sub> × λ values. Several promising intermetallic candidates were also identified, including VMo, IrRh<sub>3</sub>, PtRh<sub>3</sub>, NbRu, and CrIr<sub>3</sub>, as potential alternatives to traditional Cu interconnects in future technology nodes. The findings in the study highlight the immense potential of machine learning techniques to accelerate the discovery of novel high-performance interconnect materials. Intermetallic ; Electromigration ; Interconnection ; Binary number ; Electronic circuit ; Coupling (piping) ; Active learning (machine learning) Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Boron and Carbon Nanomaterials Research
Authors: Amanda Dumi (6950420) | Shiv Upadhyay (3726628) | Hassan Harb (1762732) | Rajeev S. Assary (1283331) | Dan C. Sorescu (1281933) | Kenneth D. Jordan (1272075) | Anouar Benali (4498261)
Source: Figshare (article, 2025-11-14)
Field: Materials Science — Materials Chemistry

A Comparison ofElectronic Structure Methods for Predictingthe Hydrogenation Energies of Candidate Molecules for Hydrogen Storage The development of novel energy materials and fuels is required to expand current available energy sources. Aiming to reach this goal, there is growing interest in using molecular hydrogen as an energy carrier due to its abundance and high energy density. Liquid organic hydrogen carriers (LOHCs) are a promising route to the large-scale storage and transport of hydrogen for use in the energy economy. The search for thermodynamically viable LOHC molecules for real world use has led to a set of constraints on the dehydrogenation enthalpy and the minimum gravimetric hydrogen capacity. These constraints allow one to formulate the search for an ideal LOHC candidate molecule as an optimization problem well suited to the strengths of machine learning and artificial intelligence computational approaches. A critical barrier to a large-scale, high-throughput screening of LOHC candidate molecules is the lack of reliable training data. Computational electronic structure methods including density functional theory, coupled cluster approximations, and diffusion Monte Carlo can be used to provide training data where experimental data are either unreliable or do not exist. In this work, we use these methods to calculate the dehydrogenation energies and enthalpies of candidate LOHC molecules. Dehydrogenation ; Hydrogen storage ; Hydrogen ; Molecule ; Gravimetric analysis ; Density functional theory ; Enthalpy ; Energy carrier Machine Learning in Materials Science ; Crystallography and molecular interactions ; Advanced Physical and Chemical Molecular Interactions