jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Chi-Huan Tung | Sidney Yip | Guan-Rong Huang | Lionel Porcar | Yuya Shinohara | Bobby G. Sumpter | Lijie Ding | Changwoo Do | Wei‐Ren Chen
Source: ArXiv.org (preprint, 2025-02-27)
Field: Materials Science — Materials Chemistry

Unlocking Hidden Information in Sparse Small-Angle Neutron Scattering Measurement Small-angle neutron scattering (SANS) is a powerful technique for probing the nanoscale structure of materials. However, the fundamental limitations of neutron flux pose significant challenges for rapid, high-fidelity data acquisition required in many experiments. To circumvent this difficulty, we introduce a Bayesian statistical framework based on Gaussian process regression (GPR) to infer high-quality SANS intensity profiles from measurements with suboptimal signal-to-noise ratios (SNR). Unlike machine learning approaches that depend on extensive training datasets, the proposed one-shot method leverages the intrinsic mathematical properties of the scattering function, smoothness and continuity, offering a generalizable solution beyond the constraints of data-intensive techniques. By examining existing SANS experimental data, we demonstrate that this approach can reduce measurement time by between one and two orders of magnitude while maintaining accuracy and adaptability across different SANS instruments. By improving both efficiency and reliability, this method extends the capabilities of SANS, enabling broader applications in time-sensitive and low-flux experimental conditions. Smoothness ; Gaussian process ; Neutron scattering ; Kriging ; Scattering ; Process (computing) ; Bayesian probability ; Gaussian ; Neutron Machine Learning in Materials Science ; Nuclear Physics and Applications ; X-ray Diffraction in Crystallography
Authors: Yaoyao Fan | Zheli Wang | Xueying Yao | Wenqian Huang | Qingyan Wang | Xi Tian | Liping Chen | Long Yuan
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Efficient Wheat Variety Identification Using Raman Hyperspectral Imaging in Combination with Deep Learning Hyperspectral imaging ; Identification (biology) ; Artificial intelligence ; Deep learning ; Variety (cybernetics) ; Raman spectroscopy ; Computer science ; Pattern recognition (psychology) ; Biology ; Botany ; Optics ; Physics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Smart Agriculture and AI
Authors: Carlos Murguiondo (22653516) | Mario García de Lacoba (10113153) | Valentina Acosta-Borreros (22693964) | Jorge Barriuso (167599) | Alicia Prieto (420410)
Source: Figshare (article, 2025-11-26)
Field: Materials Science — Biomaterials

Computational Studyof a Versatile Lipase for theDegradation of Polylactic Acid Biocatalysis is an emerging and sustainable approach to depolymerize highly hydrophobic plastic polyesters such as poly­(lactic acid) (PLA), a bioplastic widely used in packaging and disposable items. Some enzymes, including lipases, cutinases, and proteases, have been described to hydrolyze PLA, but the activity strongly depends on stereochemistry and crystallinity. In this study, we explored the activity of the versatile lipase fromOphiostoma piceae (OPE) and three engineered variants (N81A, N94A, and N81/94A) on polylactic acid (PLA), comparing the experimental data with predictions from two computational methodologies, Thermal Titration Molecular Dynamics (a classical method) and the machine learning-guided XLPFE scoring function. Experimentally, mutant N81A showed the highest PLA hydrolytic activity, followed by WT OPE, with N94A and N81/94A being substantially less effective. This combined approach served to validate the reliability of these computational strategies for predicting enzyme interactions and highlights the importance of using long enough model substrates to guide future enzyme optimization. Polylactic acid ; Lipase ; Hydrolysis ; Bioplastic ; Degradation (telecommunications) ; Hydrolytic degradation ; Polyester ; Enzyme biodegradable polymer synthesis and properties ; Microplastics and Plastic Pollution ; Enzyme Catalysis and Immobilization
Authors: Görlich, Franz | Zavadlav, Julija
Source: ArXiv.org (article, 2025-12-08)
Field: Materials Science — Materials Chemistry

Mapping Still Matters: Coarse-Graining with Machine Learning Potentials Coarse-grained (CG) modeling enables molecular simulations to reach time and length scales inaccessible to fully atomistic methods. For classical CG models, the choice of mapping, that is, how atoms are grouped into CG sites, is a major determinant of accuracy and transferability. At the same time, the emergence of machine learning potentials (MLPs) offers new opportunities to build CG models that can in principle learn the true potential of the mean force for any mapping. In this work, we systematically investigate how the choice of mapping influences the representations learned by equivariant MLPs by studying liquid hexane, amino acids, and polyalanine. We find that when the length scales of bonded and nonbonded interactions overlap, unphysical bond permutations can occur. We also demonstrate that correctly encoding species and maintaining stereochemistry are crucial, as neglecting either introduces unphysical symmetries. Our findings provide practical guidance for selecting CG mappings compatible with modern architectures and guide the development of transferable CG models. Encoding (memory) ; Feature (linguistics) ; Development (topology) ; Equivariant map Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Material Dynamics and Properties
Authors: Görlich, Franz | Zavadlav, Julija
Source: arXiv (Cornell University) (preprint, 2025-12-08)
Field: Materials Science — Materials Chemistry

Mapping Still Matters: Coarse-Graining with Machine Learning Potentials Coarse-grained (CG) modeling enables molecular simulations to reach time and length scales inaccessible to fully atomistic methods. For classical CG models, the choice of mapping, that is, how atoms are grouped into CG sites, is a major determinant of accuracy and transferability. At the same time, the emergence of machine learning potentials (MLPs) offers new opportunities to build CG models that can in principle learn the true potential of the mean force for any mapping. In this work, we systematically investigate how the choice of mapping influences the representations learned by equivariant MLPs by studying liquid hexane, amino acids, and polyalanine. We find that when the length scales of bonded and nonbonded interactions overlap, unphysical bond permutations can occur. We also demonstrate that correctly encoding species and maintaining stereochemistry are crucial, as neglecting either introduces unphysical symmetries. Our findings provide practical guidance for selecting CG mappings compatible with modern architectures and guide the development of transferable CG models. Encoding (memory) ; Feature (linguistics) ; Development (topology) ; Equivariant map Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Material Dynamics and Properties
Authors: Oscar Nordness | Kimia Fereydooni
Source: ECS Meeting Abstracts (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

Predicting the Properties of IL-Solvent Electrolytes Using a Hybrid Support Vector Classification and Gradient Boosting Regression (SVC-GBR) Framework Harnessing the vast design space of ionic liquid (IL) systems requires efficient, high-throughput computer-aided molecular design (CAMD) strategies. We present a hybrid classification-regression machine learning framework for predicting viscosity and conductivity of IL-solvent systems over wide temperature and composition ranges. Gradient boosting regression (GBR) models were trained using COSMO-RS sigma profiles and group contribution (GC) features, with GC features showing superior predictive performance. Additionally, SHapley Additive exPlanations analysis was implemented to provide insight into feature importance. Pre-classifying property datasets into narrower ranges (three for viscosity, two for conductivity) enhanced accuracy, motivating the development of a hybrid support vector classification SVC-GBR framework. This framework achieved AARD(%) values of 19.2% for viscosity and 25.4% for conductivity on IL-solvent systems previously unseen by the model, representing improvements of 34.0% and 21.4% over baseline GBR models. Support vector machine ; Gradient boosting ; Boosting (machine learning) ; Feature vector ; Curse of dimensionality ; Regression ; Pattern recognition (psychology) ; Feature (linguistics) ; Viscosity Machine Learning in Materials Science ; Ionic liquids properties and applications ; Advanced Battery Materials and Technologies
Authors: Anantha Rajmohan Muthusamy | Diwakar Chauhan | Arvind Kumar Jain | Murali Somasundaram | Amit K. Singh
Source: Crystal Growth & Design (article, 2025-04-23)
Field: Chemistry — Spectroscopy

Navigating Solid-Form Screening Using In Silico Methods Validated with Experimental Data for a Drug-like Molecule We investigated the polymorphism of Ziritaxestat (ZTS) by combining sophisticated computational prediction models with experimental crystallization techniques. In order to forecast a stable amorphous state, we developed an improved neural network model. We performed conformational energy calculations using a potential energy scan (PES) and COSMO-RS-predicted activity coefficients to identify a low-energy conformer that could be experimentally obtained in a stable anhydrous form. The predictions of solubility trends in various solvents using COSMO-RS were consistent with the experimental solubility. Using the COSMO-RS function, the solvate probability was predicted. Additionally, the COSMOtherm contact probability calculations predicted the solvation site, while the COSMO BP-TZVPD-FINE level theory determined the hydrogen bonding energy for the solvates and hydrates. We further obtained the hydrate and solvate systems through experimentation. We validated these in silico methods, further approving the proof of concept. With our diverse methodology, we were able to create a nonsolvated crystal form, four different hydrate polymorphs, and various solvates. All novel forms of ZTS (Form A, Form B, Form C, Form D, Form E, Form F, and amorphous) were thoroughly characterized by PXRD, DSC, TGA, NMR, and DVS techniques, among others. The close agreement between calculated conformations and experimental data was proven by single-crystal structure analysis. In this work, we validated the computationally designed algorithms with experimental results to efficiently search for anhydrous, solvates, hydrates, solvate-hydrate, and amorphous forms. This integrated synergistic computational–experimental approach resulted in maximum polymorph search and minimized the polymorphic risk. In silico ; Drug ; Molecule ; Chemistry ; Computational biology ; Combinatorial chemistry ; Nanotechnology ; Materials science ; Computer science ; Pharmacology ; Medicine ; Biology ; Biochemistry ; Organic chemistry Analytical Chemistry and Chromatography ; Computational Drug Discovery Methods ; Crystallization and Solubility Studies
Authors: Qi Wu | Wenhui Fan | Chong Qin | Xiaoqiang Jiang
Source: Journal of Physics D Applied Physics (article, 2025-11-12)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Inverse design of reconfigurable terahertz metasurface with wide phase modulation range via a high–precision CNN–transformer framework Abstract Terahertz communication promises Tbit s −1 -level connectivity for 6G but is hindered by severe path loss and atmospheric absorption. While reconfigurable metasurfaces offer a promising solution via dynamic wavefront control, conventional design relying on empirical optimization and constrained geometric templates inherently restricts phase tuning ranges. To address these limitations, we propose an intelligent inverse design framework for liquid crystal metasurface with three synergistic innovations: (1) a non-uniform rational B-splines method is introduced to explore smooth topological geometries of metallic patterns, reducing the solution space from 100 to 8 degrees of freedom and simplifying subsequent optimization; (2) a hybrid convolutional neural network−Transformer model is developed to jointly learn local and global electromagnetic features, achieving high prediction accuracy (phase error: 1.03°, amplitude error: 2.8 × 10 −3 ); (3) the proposed network is integrated with a particle swarm optimization algorithm for inverse design, rapidly identifying the globally optimal design within high-dimensional parameter spaces across 0.25−0.55 THz. Finally, the optimized metasurface achieves 310° phase tuning and the reflection amplitude exceeding 0.9, with the liquid crystal thickness being only 1.4% of the working wavelength, surpassing the performance in prior literature. Based on array-level optimization algorithms, we further demonstrate highly directional beam steering with flexible control over beam quantity, power ratio, and beamwidth. This work presents an innovative design approach for reconfigurable terahertz metasurfaces, paving the way for their practical implementation in next-generation communication systems. Terahertz radiation ; Wavefront ; Phase (matter) ; Particle swarm optimization ; Beam (structure) ; Reflection (computer programming) ; Reconfigurable antenna ; Inverse ; Beam steering ; Power (physics) Metamaterials and Metasurfaces Applications ; Advanced Wireless Communication Technologies ; Advanced Antenna and Metasurface Technologies
Authors: Jayani Kalahe (13038898) | Xiaonan Lu (140379) | Kyle Miller (3467528) | Miroslava Peterson (21760658) | Brian J. Riley (1481176) | John D. Vienna (4437607) | James E. Saal (21760667) | Jincheng Du (2059732)
Source: Figshare (article, 2025-10-27)
Field: Materials Science — Ceramics and Composites

Revealingthe Structure–Property Relationshipsof Nd<sub>2</sub>O<sub>3</sub>‑Containing Alkali Iron PhosphateGlasses from Molecular Dynamics Simulations The atomic structure and structure–property relationships of Nd<sub>2</sub>O<sub>3</sub>-containing potassium iron phosphate glasses were investigated by using molecular dynamics (MD) simulations based on nulcear waste glass compositions designed with artificial intellegence (AI) and machine learning (ML) approaches. The results show that Nd<sup>3+</sup> ions act as modifiers, with an average Nd<sup>3+</sup>-O bond distance of 2.35 Å and a coordination number of approximately 6. Nd<sup>3+</sup> ions were found to be preferentially associated with phosphate anionic units such as [PO<sub>4</sub>]<sup>3–</sup> and [P<sub>2</sub>O<sub>7</sub>]<sup>4–</sup>, leading to the formation of nonbridging oxygens, while K<sup>+</sup> ions primarily compensate for the negatively charged [FeO<sub>4</sub><sup>]‑</sup> units. Clustering of Nd<sup>3+</sup> ions was observed in the simulated glasses, with a decrease in homogeneous dispersion as the Nd<sub>2</sub>O<sub>3</sub> concentration increased from 5 to 10 mol %. Additionally, increasing the P<sub>2</sub>O<sub>5</sub> content increases the fraction of isolated Nd<sup>3+</sup> ions. The addition of Nd<sub>2</sub>O<sub>3</sub> notably affected the glass structures and led to significant changes in the mechanical properties and glass transition temperature values. The results demonstrate that physics based modeling method such as MD simulations with reliable interatomic potentials can be a valuable tool for composition optimization based on AI/ML design and for elucidating the complex structures and structure–property relationships of rare-earth-ion-containing phosphate glasses, a materials class with applications in critical fields such as photonics and nuclear waste disposal. Phosphate glass ; Molecular dynamics ; Ion ; Phosphate ; Glass transition ; Alkali metal ; Interatomic potential ; Dispersion (optics) Glass properties and applications ; Luminescence Properties of Advanced Materials ; Recycling and utilization of industrial and municipal waste in materials production
Authors: Bowei Zhang | Xin Xie | Lin Li | Kuo Zhao | Sandeep Kumar Chamoli
Source: Optics and Lasers in Engineering (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

Broadband-tunable multispectral imaging using phase-change-based linear variable filters Refractive index ; Multispectral image ; Transmission (telecommunications) ; Filter (signal processing) ; Optical filter ; Dielectric ; Metamaterial ; Sensitivity (control systems) ; Broadband Phase-change materials and chalcogenides ; Metamaterials and Metasurfaces Applications ; Random lasers and scattering media
Authors: Potao Sun (11089740) | Wenqing Yang (8842142) | Wenxia Sima (11089737) | Tao Yuan (443733) | Ming Yang (109148) | Ninglong Fu (21782150) | Zhaoping Li (10013972) | Xiaoxiao Chen (1673935)
Source: Figshare (article, 2025-07-24)
Field: Materials Science — Polymers and Plastics

Machine-Vision-DrivenMicroarray Passive TemperatureSensor Inspired by Insect Compound Eyes for Wide-Range and High-PrecisionSurface Mapping Real-time, accurate, and passive temperature monitoring is critical for industrial and scientific applications. However, conventional temperature sensors often require external power, rely on complex instrumentation, and may perturb the thermal field, compromising measurement accuracy in passive sensing scenarios. Although thermochromic materials offer visual and passive temperature feedback, their utility is limited by narrow sensitivity ranges and subjective interpretation. To address these challenges, this study introduces a machine-vision-enabled microarray passive temperature sensor (MAPTS) inspired by the cooperative perception mechanism of insect compound eyes. The system comprises arrays of organic thermochromic materials patterned via soft lithography on flexible, thermally conductive substrates, enabling wide-range passive thermal sensing. A deep learning-based ResNet-34 architecture deciphers the color-to-temperature relationship from optical images, facilitating high-precision, noncontact regression-based temperature prediction. Experimental results demonstrate that the MAPTS achieves dynamic thermal responses across 0–70 °C with a rapid prediction time of 50 ms. In a high-density 7 × 7 array configuration, the system exhibits better extrapolation performance (R<sup>2</sup> = 0.9996) and higher prediction accuracy (mean absolute error ≤ ±0.3 °C), compared to conventional thermochromic sensing methods. This work presents a cost-effective, highly accurate, and reliable approach for intelligent temperature monitoring in diverse applications. Thermochromism ; Temperature measurement ; Thermal ; Extrapolation ; Sensitivity (control systems) ; Pyrometer ; Work (physics) Transition Metal Oxide Nanomaterials ; Analytical Chemistry and Sensors ; Gas Sensing Nanomaterials and Sensors
Authors: Tomáš Jíra (20423723) | Jiří Janoš (9199125) | Petr Slavíček (1289919)
Source: Figshare (article, 2025-09-16)
Field: Materials Science — Materials Chemistry

Critical Assessmentof Curvature-Driven Surface HoppingAlgorithms Trajectory surface-hopping (TSH) methods have become the most used approach in nonadiabatic molecular dynamics. The increasingly popular curvature-driven schemes represent a subset of TSH based on the implicit local diabatization of potential energy surfaces. Their appeal partly stems from compatibility with machine-learning frameworks that often provide only local PES information. Here, we critically assess the limitations of these curvature-based algorithms by examining three challenging scenarios: (i) dynamics involving more than two strongly coupled electronic states; (ii) trivial crossings; and (iii) spurious transitions arising from small discontinuities in multireference potential energy surfaces. Furthermore, we extend the Landau–Zener surface hopping (LZSH) method beyond two-state systems and introduce practical modifications to enhance its robustness. The performance is benchmarked on both low- and higher-dimensional model Hamiltonians, as well as realistic molecular systems treated with <i>ab initio</i> methods. While curvature-driven TSH using the explicit electronic coefficient propagation qualitatively captures the dynamics in most cases, we find no regime where it outperforms LZSH, especially when trivial crossings, multistate crossings, or discontinuities are encountered. Hence, we advocate for using a conceptually simple but solid LZSH method when nonadiabatic couplings are not available. Spurious relationship ; Classification of discontinuities ; Surface hopping ; Potential energy ; Molecular dynamics ; Simple (philosophy) ; Trajectory ; Energy (signal processing) Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Advanced Chemical Physics Studies
Authors: Massimiliano Lupo Pasini | Jong Youl Choi | Pei Zhang | Kshitij Mehta | R. W. Weaver | Ashwin M. Aji | Karl W. Schulz | Jordà Polo | Prasanna Balaprakash
Source: ArXiv.org (preprint, 2025-06-26)
Field: Materials Science — Materials Chemistry

Multi-task parallelism for robust pre-training of graph foundation models on multi-source, multi-fidelity atomistic modeling data Graph foundation models using graph neural networks promise sustainable, efficient atomistic modeling. To tackle challenges of processing multi-source, multi-fidelity data during pre-training, recent studies employ multi-task learning, in which shared message passing layers initially process input atomistic structures regardless of source, then route them to multiple decoding heads that predict data-specific outputs. This approach stabilizes pre-training and enhances a model's transferability to unexplored chemical regions. Preliminary results on approximately four million structures are encouraging, yet questions remain about generalizability to larger, more diverse datasets and scalability on supercomputers. We propose a multi-task parallelism method that distributes each head across computing resources with GPU acceleration. Implemented in the open-source HydraGNN architecture, our method was trained on over 24 million structures from five datasets and tested on the Perlmutter, Aurora, and Frontier supercomputers, demonstrating efficient scaling on all three highly heterogeneous super-computing architectures. Scalability ; Graph ; Parallelism (grammar) ; Generalizability theory ; Transferability ; Scaling ; Process (computing) ; Data parallelism ; Message passing Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Graph Theory and Algorithms
Authors: Yulin Zhao | Yifan Zhao | Yu Hong | Geng Chen | Yingying Zhu
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Nirs-Based Prediction of Biomass Moisture and Hhv with Emphasis on Model Stability Emphasis (telecommunications) ; Stability (learning theory) ; Moisture ; Biomass (ecology) ; Environmental science ; Water content ; Computer science ; Agronomy ; Meteorology ; Engineering ; Physics ; Biology ; Machine learning ; Geotechnical engineering Spectroscopy and Chemometric Analyses ; Thermochemical Biomass Conversion Processes ; Mineral Processing and Grinding