jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Doeke R. Hekstra | Harrison K. Wang | Margaret A. Klureza | Jack B. Greisman | Kevin M. Dalton
Source: Science Advances (article, 2025-12-03)
Field: Materials Science — Materials Chemistry

Sensitive detection of structural dynamics using a statistical framework for comparative crystallography Chemical and conformational changes are crucial to protein function and its pharmacological control. X-ray crystallography can reveal these changes in atomic detail, but standard analysis methods, which refine separate datasets, often overlook differences that are subtle or arise in only a subset of molecules. Direct comparison of crystallographic datasets is, in principle, more powerful, but systematic errors ("scales") often mask changes in the crystallographic observables ("structure factors"). Machine learning algorithms that jointly estimate scales and structure factors can address this limitation. Here, we augment this approach with multivariate, structured priors derived from crystallographic theory, implemented in the variational deep learning framework Careless. Doing so strongly improves the detection of protein dynamics, element-specific anomalous signals, and the binding of drug candidates, offering a robust approach to comparative crystallography and, potentially, to detection of protein dynamics by other structure determination methods. Observable ; Protein structure ; Function (biology) ; Dynamics (music) ; Prior probability ; Protein function ; Biomolecular structure ; Molecular dynamics Enzyme Structure and Function ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Nguyễn Thiên Phúc Từ | A. Knoll | Jörg Behler | Christopher N. Rowley
Source: ChemRxiv (preprint, 2025-08-10)
Field: Materials Science — Materials Chemistry

Long-Range Interactions in High-Dimensional Neural Network Potentials: A Benchmark Study for Small Organic Molecules Electrostatics ; Intermolecular force ; Statistical physics ; Cutoff ; Chemistry ; Test set ; Density functional theory ; Artificial neural network ; Dipole ; Physics ; Molecular physics ; Molecule ; Quantum mechanics ; Computational chemistry ; Computer science ; Artificial intelligence Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Yu Zhang (12946) | Tongle Xu (6315515) | Luling He (10502240) | Weibin Ma (12577478) | Xiong Li (112522) | Minjie Li (1320591) | Guangrui Xu (168774) | Wei Lv (319498) | Peng Ding (119927)
Source: Figshare (article, 2025-09-01)
Field: Materials Science — Polymers and Plastics

High-PerformancePolyimide Design: Machine Learning–AcceleratedMultiobjective Optimization Polyimides (PIs) with superior transparency, mechanical robustness, and thermal stability are essential for advanced applications such as flexible electronics, foldable displays, and aerospace components, but simultaneous optimization of these properties remains challenging. Here, we present a data-driven multiobjective optimization framework for systematic polyimide design, supported by high-quality data sets extracted with the assistance of large language models. Leveraging robust machine learning models validated by the experimental synthesis and evaluation of five PI films, our method precisely predicts material performance and effectively deciphers complex structure–property relationships influenced by charge transfer complexes. The demonstrated capability of rapidly screening over 20,000 potential PI formulations significantly accelerates material innovation. Delivers PI-a, PI-b, and PI-e films that exceed current commercial benchmarks, achieving glass transition temperature values of 336–376 °C, tensile strengths of 207–324 MPa, and transmittance above 89.6%. This framework establishes a broadly applicable, data-driven approach for designing multifunctional polymers tailored for next-generation flexible electronics and energy storage devices. Polyimide ; Multi-objective optimization ; Aerospace ; Transmittance ; Electronics ; Stability (learning theory) ; Thermal stability ; Flexible electronics ; Compensation (psychology) Synthesis and properties of polymers ; Machine Learning in Materials Science ; Advanced Sensor and Energy Harvesting Materials
Authors: D. Widdowson | V. Kurlin
Source: ArXiv.org (preprint, 2025-09-18)
Field: Materials Science — Materials Chemistry

Higher-order, generically complete, continuous, and polynomial-time isometry invariants of periodic sets Periodic point sets model all solid crystalline materials (crystals) whose atoms can be considered zero-sized points with or without atomic types. This paper addresses the fundamental problem of checking whether claimed crystals are novel, not noisy perturbations of known materials obtained by unrealistic atomic replacements. Such near-duplicates have skewed ground-truth because past comparisons relied on unstable cells and symmetries. The proposed Lipschitz continuity under noise is a new essential requirement for machine learning on any data objects that have ambiguous representations and live in continuous spaces. For periodic point sets under isometry (any distance-preserving transformation), we designed invariants that distinguish all known counter-examples to the completeness of past descriptors and detect thousands of (near-)duplicates in large high-profile databases of crystals within two days on a modest desktop computer. Isometry (Riemannian geometry) ; Point (geometry) ; Completeness (order theory) ; Lipschitz continuity ; Set (abstract data type) ; Noise (video) Machine Learning in Materials Science ; Topological and Geometric Data Analysis ; Quasicrystal Structures and Properties
Authors: Yujing Zhang (385508) | Huabing Cai (20377508) | Beien Zhu (2567632) | Zhong-Kang Han (6270353) | Hui Li (32376) | M. Verónica Ganduglia-Pirovano (1834411) | Yi Gao (112914)
Source: Figshare (article, 2025-04-06)
Field: Materials Science — Materials Chemistry

Revealing theNon-Arrhenius Migration of Oxygen Vacanciesat the CeO<sub>2</sub>(111) Surface Ceria (CeO<sub>2</sub>) is renowned for its exceptional oxygen storage capacity, which makes it highly valuable in various applications. Central to its functionality is the migration of oxygen vacancies (V<sub>O</sub>’s). While previous studies have extensively examined the distribution of V<sub>O</sub>’s and Ce<sup>3+</sup> polarons, their kinetic behaviors and interactions, especially in the presence of multiple vacancies, are not yet fully understood. In this study, we employ density functional theory (DFT), ab initio molecular dynamics (AIMD) simulations, and machine-learning methods to investigate these phenomena. Our findings reveal a nonmonotonic temperature dependence of the migration rate of V<sub>O</sub>’s at the CeO<sub>2</sub>(111) surface. Our theoretical model further demonstrates that the migration of V<sub>O</sub>’s and the hopping of polarons are intricately coupled. Notably, frequent polaron hopping at high temperatures hinders V<sub>O</sub> migration, indicating a non-Arrhenius mechanism. This finding is further validated through long-time molecular dynamics simulation enhanced with neural network potentials. Our results provide a microscopic understanding of the interplay between V<sub>O</sub>’s and Ce<sup>3+</sup> polarons, offering crucial insights into the complex dynamics governing oxygen vacancy migration in ceria. This knowledge paves the way for improved material design and functionality. Polaron ; Oxygen ; Molecular dynamics ; Density functional theory ; Ab initio ; Vacancy defect ; Surface hopping ; Dynamics (music) Catalytic Processes in Materials Science ; Advancements in Solid Oxide Fuel Cells ; Electrocatalysts for Energy Conversion
Authors: Solé, Àlex | Mosella-Montoro, Albert | Cardona, Joan | Aravena, Daniel | Gómez-Coca, Silvia | Ruiz, Eliseo | Ruiz-Hidalgo, Javier
Source: ArXiv.org (article, 2025-11-25)
Field: Materials Science — Materials Chemistry

PRISM: Periodic Representation with multIscale and Similarity graph Modelling for enhanced crystal structure property prediction Crystal structures are characterised by repeating atomic patterns within unit cells across three-dimensional space, posing unique challenges for graph-based representation learning. Current methods often overlook essential periodic boundary conditions and multiscale interactions inherent to crystalline structures. In this paper, we introduce PRISM, a graph neural network framework that explicitly integrates multiscale representations and periodic feature encoding by employing a set of expert modules, each specialised in encoding distinct structural and chemical aspects of periodic systems. Extensive experiments across crystal structure-based benchmarks demonstrate that PRISM improves state-of-the-art predictive accuracy, significantly enhancing crystal property prediction. Property (philosophy) ; Representation (politics) ; Graph ; Encoding (memory) ; Set (abstract data type) ; Feature (linguistics) ; Crystal (programming language) ; Crystal structure Machine Learning in Materials Science ; Crystallography and molecular interactions ; Advanced Graph Neural Networks
Authors: Solé, Àlex | Mosella-Montoro, Albert | Cardona, Joan | Aravena, Daniel | Gómez-Coca, Silvia | Ruiz, Eliseo | Ruiz-Hidalgo, Javier
Source: arXiv (Cornell University) (preprint, 2025-11-25)
Field: Materials Science — Materials Chemistry

PRISM: Periodic Representation with multIscale and Similarity graph Modelling for enhanced crystal structure property prediction Crystal structures are characterised by repeating atomic patterns within unit cells across three-dimensional space, posing unique challenges for graph-based representation learning. Current methods often overlook essential periodic boundary conditions and multiscale interactions inherent to crystalline structures. In this paper, we introduce PRISM, a graph neural network framework that explicitly integrates multiscale representations and periodic feature encoding by employing a set of expert modules, each specialised in encoding distinct structural and chemical aspects of periodic systems. Extensive experiments across crystal structure-based benchmarks demonstrate that PRISM improves state-of-the-art predictive accuracy, significantly enhancing crystal property prediction. Property (philosophy) ; Representation (politics) ; Graph ; Encoding (memory) ; Set (abstract data type) ; Feature (linguistics) ; Crystal (programming language) ; Crystal structure Machine Learning in Materials Science ; Crystallography and molecular interactions ; Advanced Graph Neural Networks
Authors: Thomas Hehre (20738709) | Philip E. Klunzinger (21987113) | Bernard J. Deppmeier (21987116) | William Sean Ohlinger (21987119) | Warren J Hehre (21987122)
Source: Figshare (article, 2025-08-05)
Field: Chemistry — Spectroscopy

Practical MachineLearning Strategies 4: Using NeuralNetworks to Replicate Proton and <sup>13</sup>C NMR Chemical ShiftsObtained from ωB97X-D/6-31G* Density Functional Calculations Described are neural networks that accurately reproduce proton and <sup>13</sup>C chemical shifts obtained from ωB97X-D/6-31G*//ωB97X-D/6-31G* density functional model GIAO calculations. They support uncharged, closed-shell molecules comprising H, C, N, O, F, S, Cl, and Br. Development involved training to ≈2.7 million equilibrium geometry and chemical shift calculations for a diverse collection of organic molecules (including synthetic drugs and natural products). Referenced to ωB97X-D/6-31G*//ωB97X-D/6-31G* calculations, chemical shifts from neural networks for 601 marine natural products show RMS errors of 0.05 ppm (proton) and 0.76 ppm (<sup>13</sup>C). RMS errors of 0.09 ppm (proton) and 1.02 ppm (<sup>13</sup>C) shifts result when equilibrium geometries from a previously described “estimated ωB97X-D/6-31G*” neural network model (trained to reproduce ωB97X-D/6-31G* geometries) are utilized. A second assessment of experimental <sup>13</sup>C chemical shifts for 246 natural products is provided. Using neural network models to provide both geometries and chemical shifts: 45% of <sup>13</sup>C shifts reproduce experimental values within 1 ppm, 73% within 2 ppm, and 86% within 3 ppm. Utilizing neural network models for both equilibrium geometries and chemical shifts reduces the computational time required for accurate proton and <sup>13</sup>C chemical shifts from tens to hundreds of minutes to just a few seconds per molecule. Chemical shift ; Artificial neural network ; Proton ; Replicate ; Density functional theory ; Molecule ; Chemical equilibrium Molecular spectroscopy and chirality ; Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Yang Song | Shuaida He | Qiyu Wu
Source: Symmetry (article, 2025-10-03)
Field: Materials Science — Materials Chemistry

DRBoost: A Learning-Based Method for Steel Quality Prediction Steel products play an important role in daily production and life as a common production material. Currently, the quality of steel products is judged by manual experience. However, various inspection criteria employed by human operators and complex factors and mechanisms in the steelmaking process may lead to inaccuracies. To address these issues, we propose a learning-based method for steel quality prediction, which is named DRBoost,based on multiple machine learning techniques, including Decision tree, Random forest, and the LSBoost algorithm. In our method, the decision tree clearly captures the nonlinear relationships between features and serves as a solid baseline for making preliminary predictions. Random forest enhances the model’s robustness and avoids overfitting by aggregating multiple decision trees. LSBoost uses gradient descent training to assign contribution coefficients to different kinds of raw materials to obtain more accurate predictions. Five key chemical elements, including carbon, silicon, manganese, phosphorus, and sulfur, which significantly influence the major performance characteristics of steel products, are selected. Steel quality prediction is conducted by predicting the contents of these chemical elements. Multiple models are constructed to predict the contents of five key chemical elements in steel products. These models are symmetrically complementary, meeting the requirements of different production scenarios and forming a more accurate and universal method for predicting the steel product’s quality. In addition, the prediction method provides a symmetric quality control system for steel product production. Experimental evaluations are conducted based on a dataset of 2012 samples from a steel plant in Liaoning Province, China. The input variables include various raw material usages, while the outputs are the content of five key chemical elements that influence the quality of steel products. The experimental results show that the models demonstrate their advantages in different performance metrics and are applicable to practical steelmaking scenarios. Steelmaking ; Overfitting ; Random forest ; Robustness (evolution) ; Production (economics) ; Decision tree ; Steel mill ; Raw material Machine Learning in Materials Science ; Mineral Processing and Grinding ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Egor Ţurcan | Lorenzo La Rosa | Davide Fioravanti | Francesco Maresca
Source: Acta Materialia (article, 2025-10-30)
Field: Materials Science — Materials Chemistry

Towards DFT-accurate prediction of twin interface structure and motion in NiTi shape memory alloys Recent atomistic simulations have suggested that twin boundary motion, rather than interface energy, governs twin formation in NiTi shape memory alloys (SMAs). Yet, these findings rely on empirical interatomic potentials (IAPs), whose intrinsic inaccuracies pose uncertainties regarding the quantitative prediction of interface energetics, driving force and transformation mechanisms. In this study, we address these limitations by developing a machine learning IAP using the Performant Atomic Cluster Expansion (PACE) framework, trained on a comprehensive database of density functional theory (DFT) calculations. The resulting PACE-IAP outperforms state-of-the-art empirical and neural network-based potentials, by reproducing accurate lattice parameters, improved elastic constants, and correct features of the B2–B19’ phase transformation. Leveraging this increased accuracy, we model the structure, energetics, and motion of twin interfaces in NiTi. By computing the extrapolation grade, we verify that the local atomic environments at the predicted interfaces are well contained within the DFT configurational space. Our simulations confirm that the driving force for twin boundary motion, rather than the interface energy, controls the hierarchy of twin formation in NiTi. These atomistic insights can be used into mesoscale models of microstructural formation, ultimately enhancing predictions of variant selection and enabling the design of high-performance SMAs. • A machine learning interatomic potential for NiTi is developed using the PACE (Atomic Cluster Expansion) framework. • The interatomic potential accurately reproduces lattice parameters, elastic constants, and phase transformation features of B2 and B19’ phases. • Twin interface structures and motion are captured with DFT-level accuracy, validated via extrapolation grade analysis. • The driving force for twin boundary motion, rather than interface energy, is confirmed to control twin formation in NiTi shape memory alloys. Extrapolation ; Interatomic potential ; Shape-memory alloy ; Nickel titanium ; Lattice (music) ; Molecular dynamics ; Cluster (spacecraft) ; Interface (matter) ; Density functional theory Shape Memory Alloy Transformations ; Machine Learning in Materials Science ; Advanced Memory and Neural Computing
Authors: Miguel
A. García-Castro (1798708) | Fausto Díaz-Sánchez (17637054) | Maura Cárdenas-García (21515839) | Jesús A. Arzola-Flores (21515842) | Vladimir Carranza-Téllez (21515845)
Source: Figshare (article, 2025-06-10)
Field: Chemistry — Organic Chemistry

Theoretical Studyof Absolute Entropy, Entropy ofFormation, and Gibbs Energy of Formation of Two Novel MacromoleculesObtained by the Solid State In this study, we present a solid-state reaction that enables the novel synthesis of diimidetricarboxylic acids (DITAs) in good yields without using harmful solvents. The synthesis route offers advantages, including satisfactory efficiency and easy product purification by sublimation. The reaction utilizes trimellitic anhydride (TMA) with 3,4- and 3,5-diaminobenzoic acid to obtain the desired DITAs. Products were characterized using differential scanning calorimetry (DSC), Fourier transform infrared spectroscopy (FTIR), proton nuclear magnetic resonance (<sup>1</sup>H NMR), and mass spectrometry (MS). The enthalpies of formation in the gas phase of DITAs (Δ<sub><i>f</i></sub><i>H</i>°) were calculated, occupying absolute entropies (<i>S</i>°), entropies of formation (Δ<sub><i>f</i></sub><i>S</i>°), and Gibbs energies of formation (Δ<sub><i>f</i></sub><i>G</i>°). These properties were determined using the hybrid method B3LYP functional and the computational technique machine learning. Our theoretical studies revealed that it is possible to predict thermochemical properties with machine learning as accurately as with density functional theory (DFT). Gibbs free energy ; Fourier transform ; Density functional theory ; Differential scanning calorimetry ; Macromolecule ; Mass spectrometry ; Infrared spectroscopy ; Entropy (arrow of time) Chemical Thermodynamics and Molecular Structure ; Thermal and Kinetic Analysis ; thermodynamics and calorimetric analyses
Authors: Guang Yang
Source: Atomic Spectroscopy (article, 2025-04-03)
Field: Chemistry — Analytical Chemistry

A DANN-Based Qualitative Spectral Analysis Method for Varying Motion Parameters in High Pulse Repetition Frequency LIBS The classification and recycling of alloys hold strategic significance in promoting sustainable development.Traditional LIBS technology has been widely used in factories for online analytical detection.However, most miniaturized handheld LIBS instruments, which are more suitable for factory applications, employ high pulse repetition frequency lasers.Nevertheless, due to its high sensitivity to operational parameters, this poses significant challenges to industrial production.This research tackles three critical challenges in high-speed LIBS analysis: speed-dependent intensity variations, overlapping alloy spectra, and rigid algorithmic processing.Our solution combines adaptive neural networks with spectral focusing mechanisms, tested on 21 industrial alloys (Cu, Al, Fe).Key findings show that faster scanning speeds (4 vs. 1 mm/s) intensify spectral signals through reduced laser defocusing, yet degrade traditional neural network accuracy to 78.00%.The developed dual-focus network maintains 93.95% classification accuracy at high speeds by learning speed-independent patterns and prioritizing critical emission lines, Ssuch as Mg I 285.2 nm and Fe 275.6 nm.Compared to conventional methods, this approach reduces prediction inconsistencies in high PRF LIBS spectra acquired at handheld-device movement speeds by 42.3%, while maintaining over 90% accuracy within the operational speed range of handheld devices used in practical industrial applications.This effectively addresses the challenges of identifying copper, aluminum, and iron alloys in dynamic LIBS environments. Chemistry ; Repetition (rhetorical device) ; Spectral analysis ; Qualitative analysis ; Pulse (music) ; Pulse repetition frequency ; Analytical Chemistry (journal) ; Motion (physics) ; Optics ; Spectroscopy ; Chromatography ; Physics ; Classical mechanics ; Qualitative research ; Aerospace engineering Analytical chemistry methods development ; Metal and Thin Film Mechanics ; Advanced machining processes and optimization
Authors: Yuanqing Lu | Zhiwen Pan | Aaron Reupert | Rene Limbach | N. M. Anoop Krishnan | Katrin Wondraczek | Lothar Wondraczek
Source: Advanced Materials Technologies (article, 2025-08-14)
Field: Materials Science — Ceramics and Composites

Property Prediction and Resilience Analysis in Polyionic Glasses Using a Machine‐Learning Algorithm with Multivariate Output Abstract Designing the chemical composition of multi‐component glasses toward a set of target properties requires information on a complex range of trade‐off correlations. Consistent reference datasets are typically not available for this size, which will allow for the training of multi‐task neural network models. As an alternative, multi‐output Gaussian process regression (mGPR) is employed for multi‐task machine learning (ML) of combinations of glass properties from small, but highly consistent datasets, such as those typically generated in coherent laboratory campaigns. To this end, a dataset on polyionic glasses from the system of Na 2 O‐AlF 3 ‐Al 2 O 3 ‐P 2 O 5 ‐SO 3 is used, in which property resilience to variations in chemical composition is a particular challenge given the presence of multiple volatile species. Using the example of simultaneous predictions of refractive index and Young's modulus, it is demonstrated how mGPR outperforms single‐task modeling even when one of the physical properties is used as an additional output feature. A cloud of ≈6 . 10 7 samples is generated by random‐walk sampling in order to identify the compositional flexibility when aiming for a given set of target properties. In this way, trade‐off correlations between physical properties and chemical boundaries of compositional design are revealed. Property (philosophy) ; Set (abstract data type) ; Resilience (materials science) ; Artificial neural network ; Range (aeronautics) ; Multivariate statistics ; Process (computing) ; Flexibility (engineering) ; Representation (politics) Glass properties and applications ; Material Dynamics and Properties ; Machine Learning in Materials Science