jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Fanli Zhou | Hao Chen | Pengxiang Xu | Kai Yang | Zongrui Pei | Xianglin Liu
Source: arXiv (Cornell University) (preprint, 2026-03-22)
Field: Materials Science — Materials Chemistry

Towards Computational Microscope of Chemical Order-Disorder via ML-Accelerated Monte Carlo Simulation Tailoring the performance of next-generation high entropy materials requires a deep understanding of the competition between entropy-driven random solid solution and enthalpy-driven chemical ordering. Investigating such order and disorder complexity demands atomistic simulations that achieve high accuracy, efficiency, and generalizability across vast spatial, temporal, and especially chemical scales. While machine learning (ML) interatomic potentials have transformed molecular dynamics, they remain limited in capturing diffusion-driven chemical evolution over long timescales. The recently introduced SMC-X method brings exciting opportunities. Realizing its full potential requires a comprehensive study, which is the focus of this work. To assess model performance, we systematically benchmark invariant and equivariant architectures using a density functional theory dataset of more than 10,000 configurations spanning seven elements: Fe, Co, Ni, Al, Ti, Ta, and V. To understand the roles of pairwise and higher-order interactions, we decouple their contributions across chemical space using an explainable machine learning approach. We also examine the impact of lattice relaxation by comparing models trained on datasets with and without structural relaxation. Our results clarify how to choose ML surrogate models for Monte Carlo simulations, bridge the gap between theory and experiment, and lay a foundation for establishing ML-accelerated Monte Carlo as a computational microscope for chemical complexity. Monte Carlo method ; Randomness ; Pairwise comparison ; Monte Carlo molecular modeling ; Kinetic Monte Carlo ; Suite ; Chemical space ; Computational simulation ; Generalizability theory Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Electrocatalysts for Energy Conversion
Authors: R. Docherty | Sam G. Riley | John Donald Morley | Evangelos Papoutsellis | Antonia D. Bobitan | Jack Donoghue | Katy Rankin | Fernando Alvarez Borges | Luis Salinas‐Farran | Š. Michalik | Alexander Liptak | Genoveva Burca | Pierre‐Olivier Autran | Jonathan P. Wright | W. Kockelmann | Olof Gutowski | Ann-Christin Dippel | M. v. Zimmermann | Dorota Matras | Bartlomiej Winiarski | John S. Mangum | Melissa Popeil | Donal P. Finegan | J. Gott | Daniela Proprentner | Geoff West | L. F. J. Piper | Hongyang Dong | Matthew P. Jones | Francesco Iacoviello | Alice V. Llewellyn | Rhodri Jervis | Yuta Kimura | Koji Amezawa | O. Sekizawa | M. G. Ardakani | Aigerim Omirkhan | Professor Mary Ryan | James O. Douglas | Siyang Wang | Finn Giuliani | Neil Mulcahy | S. Conroy | Chandramohan George | Andrew M. Beale | Simon D. M. Jacques | Samuel J. Cooper | Antonis Vamvakeros
Source: ChemRxiv (article, 2026-02-23)
Field: Materials Science — Materials Chemistry

Battery Imaging Library: Multi-length scale and multi-modal synchrotron and laboratory battery imaging data for all Battery research increasingly relies on advanced imaging, yet open access to such data remains rare, scattered across various sources, and difficult to find. The Battery Imaging Library (BIL) is the first open, curated collection of multi-modal and multi-length scale battery imaging datasets, accompanied by a searchable, FAIR-compliant website. Distinctive features include the release of raw experimental data (radiographs, sinograms, X-ray and electron diffraction patterns) together with rare operando and multi-resolution datasets. Each dataset is linked to Zenodo DOIs with metadata, ensuring persistence and citability; open-source Python scripts for preprocessing and reconstruction are also provided for various CT modalities. BIL enables algorithm benchmarking, machine learning, and teaching using experimental and industrially relevant data. By combining coverage across modalities, length scales, and chemistries with raw data accessibility and a FAIR-aligned web platform, the Battery Imaging Library provides a foundation for openness and reproducibility in battery imaging. The library is available here: https://www.batteryimaginglibrary.com Battery (electricity) ; Scripting language ; Python (programming language) ; Preprocessor ; Modular design ; Raw data ; Synchrotron Machine Learning in Materials Science ; Advanced Battery Technologies Research ; Atomic and Subatomic Physics Research
Authors: Abdoul Salam Issiaka Ibrahim | Ahmad Naim Ahmad Yahaya | Sena SAYDI | Gürbüz Çomak | Özgür Sönmez
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Chemistry — Organic Chemistry

Machine Learning-Assisted Prediction and Optimization of Microwave-Assisted Esterification of Levulinic Acid for Ethyl Levulinate Production Levulinic acid ; Mean squared error ; Gradient boosting ; Rank (graph theory) ; Boosting (machine learning) ; Random forest ; Coefficient of determination ; Hyperparameter Microwave-Assisted Synthesis and Applications ; Catalysis for Biomass Conversion ; Food Drying and Modeling
Authors: Chris J. Benmore | Stephen K. Wilke | David W. Lipke | Richard Weber
Source: Advances in Physics X (article, 2026-03-14)
Field: Materials Science — Materials Chemistry

High temperature diffraction from aerodynamically levitated materials Aerodynamic levitation combined with laser beam heating has become an established technique for studying the structure of materials at ultra-high temperatures and under non-equilibrium conditions. This article briefly highlights some recent technical and scientific advancements in understanding the relationships between a material's behavior and its structure, investigated using diffraction methods. It focuses on three evolving frontiers: sophisticated sample environments for accessing metastable states and reactive chemistries, high-flux photon and neutron probes to reveal atomic structure, and advanced computational modeling frameworks. Free from contamination, containerless processing (levitation) can minimize heterogeneous nucleation at the interface, enabling access to deeply supercooled melts or providing insights into chemical processes, such as steelmaking, aerospace materials and ultra-high temperature ceramics manufacturing. Additionally, the study of highly radioactive materials, including plutonium oxide are now feasible, and recent advancements in machine learning methods can extract bonding information well beyond that obtained using standard diffraction analyses. The prospect for future opportunities is discussed with a focus on hyperbaric levitation to extend the range of extreme chemical conditions. Diffraction ; Temperature measurement ; Compression (physics) ; Temperature control ; Welding Ultrasound and Cavitation Phenomena ; Magnetic and Electromagnetic Effects ; Cavitation Phenomena in Pumps
Authors: Guanlin Yang | Hongjian Chen | Run Li | Shifang Xiao | Wangyu Hu
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Cross-Layer Plasticity of InSe Van der Waals Crystals Derived From Continuous Phase Transitions Plasticity ; van der Waals force ; Phase transition ; Nucleation ; Phase (matter) ; Monoclinic crystal system ; Tetragonal crystal system ; Pairing 2D Materials and Applications ; Organic and Molecular Conductors Research ; Solid-state spectroscopy and crystallography
Authors: Le Tao | Chunyang Liao | Guibin Jiang
Source: Journal of Environmental Sciences (article, 2026-03-01)
Field: Materials Science — Materials Chemistry

Predicting multi-heavy-metal adsorption on bimetallic ZIFs via interpretable machine learning: from latin hypercube design to web implementation Bimetallic strip ; Sketch ; Hypercube ; Latin hypercube sampling ; Adsorption Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; Chromium effects and bioremediation
Authors: Zhao Zhang | Qinghai Shu | Wei Wu | Hao-ming Zou | Xijuan Lv | Shuai Zhao | Jing Wang | Manman Wang | Shu-sen Chen
Source: Energetic Materials Frontiers (article, 2026-01-06)
Field: Materials Science — Materials Chemistry

Automatic detection, segmentation, and characterization of 3-nitro-1,2,4-triazol-5-one (NTO) crystal particles during cooling crystallization using deep learning The complex cooling crystallization process of 3-nitro-1,2,4-triazol-5-one (NTO) directly affects its performance. The introduction of process analysis technology is expected to provide an effective solution for process monitoring and optimization. This study proposes a crystal instance segmentation method based on the Mask R-CNN framework and develops a semi-automatic annotation strategy combining model-assisted labeling and manual refinement. Additionally, image augmentation techniques were used to enhance model stability. Comparative experiments show that the model trained with data augmentation and semi-automatic annotation achieved an AP50 of 94.4% on the validation set, improving annotation efficiency by approximately 12-fold. The model was successfully applied to the automatic segmentation and parameter extraction of NTO crystallization images, enabling effective quantitative analysis of particle count, size distribution, and morphology. This approach offers an efficient technical solution for online image monitoring of complex crystallization systems, with significant potential for industrial application. • Mask R-CNN was used for instance segmentation of NTO crystallization images. • A semi-automatic labeling strategy improved annotation efficiency and accuracy. • Data augmentation boosted model performance in multi-scale particle detection. • Crystal morphology was quantitatively analyzed during NTO crystallization. Crystallization ; Segmentation ; Process (computing) ; Crystal (programming language) ; Image segmentation ; Characterization (materials science) ; Annotation ; Particle (ecology) Crystallization and Solubility Studies ; Enzyme Structure and Function ; Crystallography and molecular interactions
Authors: Xuewu Liu | W. Li | Licheng Liang | Chao Zhang | Bo Zheng | Lan Feng | Quanbing Ren | Qingqing Yang | Fei Liang
Source: Journal of Energy Storage (article, 2026-03-18)
Field: Materials Science — Materials Chemistry

Interpretable machine learning framework for hydrogen storage capacity prediction in Ti-Zr-Mn-Cr-V high-entropy alloys: Influenced by valence electron average Valence (chemistry) ; Pattern recognition (psychology) ; Hydrogen storage ; Artificial neural network ; Hydrogen ; Support vector machine Machine Learning in Materials Science ; Hydrogen Storage and Materials ; Electrocatalysts for Energy Conversion
Authors: Z. C. Li | Xiao‐Tong Li | Zhaoqi Chen | Yushan Geng | Hao Gong | Sijia Hu | Wenli Song | Wanshun Xia | Chuanzheng Li | Linfa Peng | Yue Fan | Guan Pz | Bo Zhang | Weihua Wang | Yong Yang
Source: Advanced Science (article, 2026-02-10)
Field: Materials Science — Materials Chemistry

Transforming Grain‐Boundary Brittle Precipitates to Ductility Pathways in Complex Concentrated Alloy Conventional wisdom holds that hard grain-boundary (GB) precipitates embrittle structural alloys by acting as crack initiation sites. In this work, we overturn this paradigm through atomic-scale interfacial engineering, transforming brittle GB phases into ductility pathways in a machine-learning identified model complex concentrated alloy. By precisely tailoring thermomechanical processing, we fabricated compositionally and structurally graded interfaces (GIs) that enable sequential plasticity activation and coordinated deformation across GBs. This interfacial architecture converts an intrinsically brittle multi-phase alloy into a ductile material, achieving an exceptional yield strength of ∼1.2 GPa with a total elongation of ∼20%. The achieved strength-ductility synergy, realized via interfacial plasticity programming, establishes a generalizable materials design strategy to overcome the persistent challenge of GB embrittlement in precipitation-strengthened alloys. Brittleness ; Ductility (Earth science) ; Embrittlement ; Alloy ; Plasticity ; Elongation ; Deformation (meteorology) Microstructure and mechanical properties ; Machine Learning in Materials Science ; Microstructure and Mechanical Properties of Steels
Authors: Ibrar Muhammad Khan | Safir Ullah Khan | Samiullah Khan | Mohammed Alissa | Alaa S. Alhegaili | Sitah Almotiry | Zaigui Wang
Source: Coordination Chemistry Reviews (article, 2026-02-27)
Field: Chemistry — Inorganic Chemistry

Coordination chemistry in a one health framework: From design principles to applications in agriculture, food, and health Transformative learning ; Cornerstone ; Food systems ; Coordination complex ; Systems thinking ; Paradigm shift Metal-Organic Frameworks: Synthesis and Applications ; Molecular Sensors and Ion Detection ; Advanced Nanomaterials in Catalysis
Authors: Agus Arip Munawar | Kusumiyati Kusumiyati | Andasuryani Andasuryani | Zulfahrizal Zulfahrizal | I.  Wayan Budiastra | Daniel Mörlein
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Chemistry — Analytical Chemistry

Robust and Update NIRS Models for Predicting Quality Attributes on Intact Mango by means of optimized Machine Learning and Artificial Neural Networks Artificial neural network ; Partial least squares regression ; Support vector machine ; Random forest ; Regression ; Linear regression ; Regression analysis ; Extreme learning machine ; Boosting (machine learning) Spectroscopy and Chemometric Analyses ; Postharvest Quality and Shelf Life Management ; Smart Agriculture and AI
Authors: Ang‐Yu Lu | Rui Chen | Aijia Yao | Mao Chen | Ji‐Hoon Park | Tianyi Zhang | Xudong Zheng | Nannan Mao | Jiangtao Wang | Zhien Wang | Tomás Palacios | Jing Kong
Source: Advanced Materials (article, 2026-03-12)
Field: Materials Science — Materials Chemistry

Text Mining of CVD Synthesis Recipes for 2D Materials A vast amount of scientific knowledge is embedded in journal articles as unstructured text, creating challenges for efficiently extracting detailed insights. Traditionally, expert-authored reviews summarize research progress, but they often struggle to capture the intricate synthesis protocols in individual papers and provide limited quantitative comparisons of experimental techniques. Recent advancements in machine learning, particularly natural language processing (NLP), have enabled automated text mining and information extraction. However, in materials science, most approaches have focused on refining model architectures rather than addressing domain-specific challenges such as data annotation and the extraction of complex synthesis details. We present a machine learning framework for extracting synthesis protocols of 2D materials, including graphene and TMDs, from publications spanning 1980-2022. By combining named entity recognition (NER) and extractive question answering (EQA), we retrieve both categorical and numerical synthesis parameters. Generative models are further used to summarize and generate experimental recipes, enabling knowledge transfer across material systems. Our domain-specific, fine-tuned models offer improved precision and interpretability compared to general-purpose approaches. This scalable framework helps unlock hidden insights from literature, supporting data-driven synthesis optimization and accelerating materials discovery. Interpretability ; Scalability ; Information extraction ; Knowledge extraction ; Generative grammar ; Annotation ; Categorical variable ; Unstructured data Machine Learning in Materials Science ; Advanced Photocatalysis Techniques ; 2D Materials and Applications
Authors: A. Tripathi
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2026-03-08)
Field: Materials Science — Materials Chemistry

Developing Quantum-Physics Inspired Machine Learning Model for the Partition Coefficient Prediction and Comparing its Performance with the Empirical Descriptors and Molecular Graph-based Models Supplementary Information for logP Prediction. It has dataset, figures, tables, and gnn code. Partition (number theory) ; Pattern recognition (psychology) ; Support vector machine ; Empirical modelling ; Training set ; Predictive modelling Machine Learning in Materials Science ; Big Data and Digital Economy ; Advanced Graph Neural Networks
Authors: Tin Hadži Veljković | Joshua J. C. Rosenthal | Ivor Lončarić | Jan-Willem van de Meent
Source: arXiv (Cornell University) (preprint, 2026-04-02)
Field: Materials Science — Materials Chemistry

Crystalite: A Lightweight Transformer for Efficient Crystal Modeling Generative models for crystalline materials often rely on equivariant graph neural networks, which capture geometric structure well but are costly to train and slow to sample. We present Crystalite, a lightweight diffusion Transformer for crystal modeling built around two simple inductive biases. The first is Subatomic Tokenization, a compact chemically structured atom representation that replaces high-dimensional one-hot encodings and is better suited to continuous diffusion. The second is the Geometry Enhancement Module (GEM), which injects periodic minimum-image pair geometry directly into attention through additive geometric biases. Together, these components preserve the simplicity and efficiency of a standard Transformer while making it better matched to the structure of crystalline materials. Crystalite achieves state-of-the-art results on crystal structure prediction benchmarks, and de novo generation performance, attaining the best S.U.N. discovery score among the evaluated baselines while sampling substantially faster than geometry-heavy alternatives. Transformer ; Representation (politics) ; Crystal structure ; Geometric modeling ; Topology (electrical circuits) ; Crystal (programming language) Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Advanced Electron Microscopy Techniques and Applications
Authors: Shahnawaz Alam | Mohammed Mudassir Uddin | M. A. Aleem Pasha
Source: arXiv (Cornell University) (preprint, 2026-01-18)
Field: Materials Science — Materials Chemistry

Constraint-Aware Neurosymbolic Uncertainty Quantification with Bayesian Deep Learning for Scientific Discovery Scientific Artificial Intelligence (AI) applications require models that deliver trustworthy uncertainty estimates while respecting domain constraints. Existing uncertainty quantification methods lack mechanisms to incorporate symbolic scientific knowledge, while neurosymbolic approaches operate deterministically without principled uncertainty modeling. We introduce the Constraint-Aware Neurosymbolic Uncertainty Framework (CANUF), unifying Bayesian deep learning with differentiable symbolic reasoning. The architecture comprises three components: automated constraint extraction from scientific literature, probabilistic neural backbone with variational inference, and differentiable constraint satisfaction layer ensuring physical consistency. Experiments on Materials Project (140,000+ materials), QM9 molecular properties, and climate benchmarks show CANUF reduces Expected Calibration Error by 34.7% versus Bayesian neural networks while maintaining 99.2% constraint satisfaction. Ablations reveal constraint-guided recalibration contributes 18.3% performance gain, with constraint extraction achieving 91.4% precision. CANUF provides the first end-to-end differentiable pipeline simultaneously addressing uncertainty quantification, constraint satisfaction, and interpretable explanations for scientific predictions. Uncertainty quantification ; Constraint (computer-aided design) ; Probabilistic logic ; Differentiable function ; Bayesian network ; Domain (mathematical analysis) ; Bayesian probability ; Deep learning ; Artificial neural network Machine Learning in Materials Science ; Explainable Artificial Intelligence (XAI) ; Advanced Graph Neural Networks
Authors: Shahnawaz Alam | Mohammed Mudassir Uddin | M. A. Aleem Pasha
Source: ArXiv.org (article, 2026-01-18)
Field: Materials Science — Materials Chemistry

Constraint-Aware Neurosymbolic Uncertainty Quantification with Bayesian Deep Learning for Scientific Discovery Scientific Artificial Intelligence (AI) applications require models that deliver trustworthy uncertainty estimates while respecting domain constraints. Existing uncertainty quantification methods lack mechanisms to incorporate symbolic scientific knowledge, while neurosymbolic approaches operate deterministically without principled uncertainty modeling. We introduce the Constraint-Aware Neurosymbolic Uncertainty Framework (CANUF), unifying Bayesian deep learning with differentiable symbolic reasoning. The architecture comprises three components: automated constraint extraction from scientific literature, probabilistic neural backbone with variational inference, and differentiable constraint satisfaction layer ensuring physical consistency. Experiments on Materials Project (140,000+ materials), QM9 molecular properties, and climate benchmarks show CANUF reduces Expected Calibration Error by 34.7% versus Bayesian neural networks while maintaining 99.2% constraint satisfaction. Ablations reveal constraint-guided recalibration contributes 18.3% performance gain, with constraint extraction achieving 91.4% precision. CANUF provides the first end-to-end differentiable pipeline simultaneously addressing uncertainty quantification, constraint satisfaction, and interpretable explanations for scientific predictions. Uncertainty quantification ; Constraint (computer-aided design) ; Probabilistic logic ; Differentiable function ; Bayesian network ; Domain (mathematical analysis) ; Bayesian probability ; Deep learning ; Artificial neural network Machine Learning in Materials Science ; Explainable Artificial Intelligence (XAI) ; Advanced Graph Neural Networks
Authors: Ihsan ulhaq Toor
Source: Corrosion and Materials Degradation (article, 2026-01-30)
Field: Materials Science — Metals and Alloys

Advances in Understanding of Secondary Phases and Their Corrosion Implications in Stainless Steel Alloys—A Review The formation and evolution of secondary phases, such as sigma (σ), chi (χ), Laves, carbides (M23C6), and nitrides (Cr2N), have a fundamental impact on the corrosion resistance of stainless steels. These stages alter the matrix’s local chemistry, compromise the passive film’s quality, and promote micro-galvanic interaction, which enhances localized corrosion issues. The thermodynamic stability, precipitation kinetics, and corrosion consequences of secondary phases in austenitic, ferritic, duplex, and lightweight (Fe–Mn–Al–C) stainless-steel systems are thoroughly reviewed and discussed in this paper. Advances in high-resolution characterization techniques, such as TEM, EBSD, atom-probe tomography, and in situ synchrotron techniques, have made it possible to map corrosion problems caused by secondary phases at the nanoscale. Computational thermodynamics (CALPHAD, DICTRA, TC-PRISMA) and emerging machine-learning models now provide quantitative prediction of phase formation and dissolution. Strategies for mitigation through alloy design, thermal treatment, and surface engineering are summarized, together with additive-manufacturing approaches for microstructural tailoring. Finally, this review highlights the integration of multi-scale modeling and sustainable alloy design to ensure phase-stable, corrosion-resistant stainless steels that enhance asset integrity and infrastructure reliability as per Sustainable Development Goals. Corrosion ; Carbide ; Precipitation ; Alloy ; Phase (matter) ; Nitride ; Characterization (materials science) Hydrogen embrittlement and corrosion behaviors in metals ; Additive Manufacturing Materials and Processes ; High Temperature Alloys and Creep