jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Suman Itani | Yibo Zhang | Jiadong Zang
Source: Nature Communications (article, 2025-10-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

The northeast materials database for magnetic materials The discovery of magnetic materials with high operating temperature ranges and optimized performance is essential for advanced applications. Current data-driven approaches are limited by the lack of accurate, comprehensive, and feature-rich databases. This study aims to address this challenge by using Large Language Models (LLMs) to create a comprehensive, experiment-based, magnetic materials database named the Northeast Materials Database (NEMAD), which consists of 67,573 magnetic materials entries ( www.nemad.org ). The database incorporates chemical composition, magnetic phase transition temperatures, structural details, and magnetic properties. Enabled by NEMAD, we trained machine learning models to classify materials and predict transition temperatures. Our classification model achieved an accuracy of 90% in categorizing materials as ferromagnetic (FM), antiferromagnetic (AFM), and non-magnetic (NM). The regression models predict Curie (Néel) temperature with a coefficient of determination (R<sup>2</sup>) of 0.87 (0.83) and a mean absolute error (MAE) of 56K (38K). These models identified 25 (13) FM (AFM) candidates with a predicted Curie (Néel) temperature above 500K (100K) from the Materials Project. This work shows the feasibility of combining LLMs for automated data extraction and machine learning models to accelerate the discovery of magnetic materials. Curie temperature ; Ferromagnetism ; Antiferromagnetism ; Curie ; Work (physics) ; Magnetic susceptibility ; Linear regression Magnetic and transport properties of perovskites and related materials ; Magnetic Properties of Alloys ; High-pressure geophysics and materials
Authors: Jan Goran T. Tomacruz | Michael T. Castro | Miguel Francisco M. Remolona | Allan Abraham B. Padama | Joey D. Ocon
Source: ChemistryOpen (article, 2025-01-29)
Field: Materials Science — Materials Chemistry

Atomic Adsorption Energies Prediction on Bimetallic Transition Metal Surfaces Using an Interpretable Machine Learning‐Accelerated Density Functional Theory Approach In this study, we identified features with the largest contributions and property trends in predicting the adsorption energies of carbon, hydrogen, and oxygen adsorbates on transition metal (TM) surfaces by performing Density Functional Theory (DFT)-based calculations and Machine Learning (ML) regression models. From 26 monometallic and 400 bimetallic fcc(111) TM surfaces obtained from Catalysis-hub.org, three datasets consisting of fourteen elemental, electronic, and structural properties were generated using DFT calculations, site calculations, and online databases. The number of features was reduced using feature selection and then finely-tuned random forest regression (RFR), gaussian process regression (GPR), and artificial neural network (ANN) algorithms were implemented for adsorption energy prediction. Finally, model-agnostic interpretation methods such as permutation feature importance (PFI) and shapely additive explanations (SHAP) provided rankings of feature contributions and directional trends. For all datasets, RFR and GPR demonstrated the highest prediction accuracies. In addition, interpretation methods demonstrated that the largest contributing features and directional trends in the regression models were consistent with structure-property-performance relationships of TMs like the d-band model, the Friedel model, and higher-fold adsorption sites. Overall, this interpretable ML-DFT approach can be applied to TMs and their derivatives for atomic adsorption energy prediction and model explainability. Bimetallic strip ; Density functional theory ; Random forest ; Adsorption ; Artificial intelligence ; Artificial neural network ; Quantitative structure–activity relationship ; Feature selection ; Machine learning ; Chemistry ; Computer science ; Kriging ; Materials science ; Computational chemistry ; Metal ; Physical chemistry ; Organic chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalysis and Hydrodesulfurization Studies
Authors: Wubshet Getachew Mengesha | Kaveer Nagessar
Source: Discover Materials (review, 2025-09-30)
Field: Materials Science — Materials Chemistry

Advanced thermal and magnetic materials for high-power and high-temperature applications: a comprehensive review This comprehensive review explores the advancements, applications, and challenges of advanced thermal and magnetic materials in high-power and high-temperature environments. These materials, including high-temperature superconductors, ferromagnetic materials, and magnetic alloys, are crucial for industries such as energy, aerospace, automotive, and electronics. They are crucial for managing heat, converting energy, and storing it, which boosts the efficiency and dependability of renewable energy systems, electric vehicles, and aerospace technologies. Nonetheless, they encounter major obstacles, such as material breakdown under harsh conditions, expensive production, and sustainability issues. The study explores essential thermal properties like heat capacity, thermal conductivity, thermal expansion, and thermal stress, highlighting the significance of ceramics, composites, metals, carbon nanotubes, and phase-change materials. It also investigates magnetic properties like permeability, coercivity, and remanence, essential for high-power applications. Emerging technologies such as nanotechnology, additive manufacturing, and machine learning offer promising solutions to overcome existing limitations. The review highlights the need for interdisciplinary research to develop sustainable and cost-effective materials capable of withstanding extreme conditions while maintaining performance. It underscores the importance of continuing research and development to address global energy and environmental challenges, paving the way for next-generation technologies. In summary, cutting-edge thermal and magnetic materials are set to transform industries, paving the way for more efficient and dependable technological advancements. Aerospace ; Sustainability ; Dependability ; Efficient energy use ; Thermal ; Renewable energy ; Thermal energy Thermal Expansion and Ionic Conductivity ; Thermal properties of materials ; Magnetic Properties of Alloys
Authors: Jan Vincent M. Madayag | Marcel Roy B. Domalanta | Reymark D. Maalihan | Eugene B. Caldona
Source: Journal of environmental chemical engineering (article, 2025-02-06)
Field: Materials Science — Biomaterials

Valorization of extractible soybean by-products for polymer composite and industrial applications Composite number ; Polymer ; Chemistry ; Materials science ; Organic chemistry ; Composite material Nanocomposite Films for Food Packaging ; Food composition and properties ; biodegradable polymer synthesis and properties
Authors: Rui Su | Chao Ma | Bing Han | Hongjie Zhang | Kai Liu
Source: Small (review, 2025-02-05)
Field: Materials Science — Biomaterials

Proteins for Hyperelastic Materials Meticulous engineering and the yielded hyperelastic performance of structural proteins represent a new frontier in developing next-generation functional biomaterials. These materials exhibit outstanding and programmable mechanical properties, including elasticity, resilience, toughness, and active biological characteristics, such as degradability and tissue repairability, compared with their chemically synthetic counterparts. However, there are several critical issues regarding the preparation approaches of hyperelastic protein-based materials: limited natural sequence modules, non-hierarchical assembly, and imbalance between compressive and tensile elasticity, leading to unmet demands. Therefore, it is pivotal to develop an alternative strategy for biofabricating hyperelastic materials. Herein, the molecular design, engineering, and property regulation of hyperelastic structural proteins are overviewed. First, methodologies for deeper exploration of mechanical modules, including machine learning-aided de novo design, random mutations of natural sequences, and multiblock fusion techniques, are actively introduced. These methodologies facilitate the generation of elastomeric protein modules and demonstrate enhanced structural and functional versatility. Subsequently, assembly tactics of hyperelastic proteins (i.e., physical modulation, genetic adaptations, and chemical modifications) are reviewed, yielding hierarchically ordered structures. Finally, advances in biophysical techniques for more nuanced characterization of protein ensembles are discussed, unveiling the tuning mechanisms of protein elasticity across scales. Future developments in structural hyperelastic protein-based biomaterials are also envisioned. Hyperelastic material ; Materials science ; Polymer science ; Nanotechnology ; Engineering ; Structural engineering ; Finite element method Silk-based biomaterials and applications ; biodegradable polymer synthesis and properties ; Electrospun Nanofibers in Biomedical Applications
Authors: Thomas Plé | Olivier Adjoua | Anouar Benali | Evgeny Posenitskiy | Corentin Villot | Louis Lagardère | Jean‐Philip Piquemal
Source: ChemRxiv (article, 2025-12-17)
Field: Materials Science — Materials Chemistry

A Foundation Model for Accurate Atomistic Simulations in Drug Design While artificial intelligence has revolutionized the prediction of static protein structures, characterizing their dynamics and interactions with drug candidates remains a computational bottleneck. Here, we introduce FeNNix-Bio1, a foundation machine learning model designed to power accurate, reactive atomistic simulations of biological systems at an unprecedented speed and scalability. Trained exclusively on synthetic quantum chemistry data, FeNNix-Bio1 accurately captures complex condensed-phase phenomena such as ion solvation and subtle liquid water properties for which it outperforms state-of-the-art specialized force fields. We demonstrate its versatility across a full spectrum of drug design applications, including the calculation of hydration free energies (HFEs), the reversible folding of small proteins, the simulation of protein-ligand absolute binding free energies and chemical reactions. Notably, FeNNix-Bio1 sets a new standard for the precise prediction of HFEs for the more than 600 molecules of the Freesolv dataset, providing sub-kcal/mol accuracy. By enabling scalable, quantum-accurate molecular dynamics without the need for manual parametrization, FeNNix-Bio1 bridges the gap between static structure prediction and dynamic biological reality: it is likely to have a strong impact in Drug Design. Solvation ; Molecular dynamics ; Foundation (evidence) ; Folding (DSP implementation) ; Molecular biophysics ; Quantum chemical ; Complex system ; Quantum ; Water model Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: W. Wang | Ge Zhou | Haoyu Zhang | Siqian Zhang | Nannan Zhang | Li Jia Chen | Peter K. Liaw
Source: Journal of Material Science and Technology (article, 2025-04-29)
Field: Materials Science — Materials Chemistry

Establishing a quantitative relationship between processing, microstructure, and formability in Ni47Ti33Hf20 shape memory alloy through multivariate decision tree optimization Formability ; Multivariate statistics ; Microstructure ; Materials science ; Decision tree ; Alloy ; Multivariate analysis ; Metallurgy ; Tree (set theory) ; Computer science ; Mathematics ; Artificial intelligence ; Machine learning ; Combinatorics Shape Memory Alloy Transformations ; Topology Optimization in Engineering ; Metallurgy and Material Forming
Authors: Kang Liang | Xinke Qi | Xu Xiao | Li Wang | Jinglai Zhang
Source: Chemical Science (article, 2025-12-23)
Field: Materials Science — Polymers and Plastics

Chemically-informed active learning enables data-efficient multi-objective optimization of self-healing polyurethanes Self-healing polyurethanes (PUs) exhibit an inherent trade-off between mechanical strength and self-healing efficiency. Although optimizing the feed ratio can address the above limitations, identifying an appropriate ratio through trial-and-error is not straightforward. Machine learning offers promising approaches for composition-property optimization. However, the multi-property optimization for PUs with specific ingredients remains challenging, especially with minimal samples. A chemically-informed active learning (CIAL) framework is developed that integrates domain knowledge with machine learning to optimize fluorescent self-healing PU with merely 20 experimental datasets. By combining gradient boosting regression with multi-objective optimization, the framework successfully achieves co-optimization of mechanical properties and self-healing efficiency, with the relative error of the comprehensive performance index below 12%. The framework's efficiency is further highlighted to achieve optimal results with only 15 samples when discrete performance data are available. The developed P20B sample serves as an intelligent protective coating for Q235, simultaneously achieving real-time fluorescence visualization of damage sites and long-term anti-corrosion. This work provides an innovative solution for intelligent design of polymer materials using tiny experimental data. Boosting (machine learning) ; Active learning (machine learning) ; Visualization ; Coating ; Domain (mathematical analysis) ; Gradient boosting Polymer composites and self-healing ; Topology Optimization in Engineering ; Hydrogels: synthesis, properties, applications
Authors: A. Viardin | K. Nöth | C. Pickmann | L. Sturz
Source: Integrating materials and manufacturing innovation (article, 2025-03-01)
Field: Materials Science — Materials Chemistry

Automatic Detection of Dendritic Microstructure Using Computer Vision Deep Learning Models Trained with Phase Field Simulations Abstract In this study, a novel approach to analyze the dendritic microstructure in solidification processes is presented, using an innovative method to prepare datasets for deep learning training with minimal human intervention through phase field simulations. This simulation technique, known for its accurate description of dendritic morphologies, enables the creation of comprehensive and precise microstructure datasets. By using advanced deep learning techniques, in particular Faster R-CNN and Mask R-CNN methods, we have successfully automated the detection of dendritic growth in various scenarios. In microgravity and terrestrial solidification experiments with optically transparent alloys, faster R-CNN was particularly effective in identifying loosely connected dendrites in experimental images and showed superior performance over thresholding methods, especially in detecting optically overlapping dendrites. For contiguous dendrites in directionally solidified polycrystalline metal alloys, mask R-CNN proved to be extremely proficient due to its ability to accurately delineate closely spaced dendrites. The use of phase field simulations to generate datasets played a crucial role in training and testing these models. Our research highlights the significant potential of deep learning in describing complex microstructural patterns, contributing to a deeper understanding of the solidification process and its effects on material properties, with the added benefit of facilitating dataset generation through a highly accurate microstructure generation method. Furthermore, this method can be applied to any type of microstructure and to different types of materials, as the phase field simulations can accurately simulate many microstructural properties. Deep learning ; Microstructure ; Computer science ; Artificial intelligence ; Thresholding ; Dendrite (mathematics) ; Phase (matter) ; Field (mathematics) ; Materials science ; Process (computing) ; Pattern recognition (psychology) ; Physics ; Image (mathematics) ; Geometry ; Metallurgy Solidification and crystal growth phenomena ; Aluminum Alloy Microstructure Properties ; Machine Learning in Materials Science
Authors: Jason M. Stevens | Jacob M. Ganley | Matthew J. Goldfogel | A.H. Furman | Steven R. Wisniewski
Source: Organic Process Research & Development (article, 2025-01-02)
Field: Materials Science — Materials Chemistry

Accelerating the Development of Sustainable Catalytic Processes through Data Science Herein, we describe the optimization of reaction conditions for a nickel-catalyzed borylation using a combination of machine learning, high-throughput experimentation, and Bayesian optimization. A machine learning model, trained on a data set from Bristol Myers Squibb, was employed to predict the yields of 144 potential reaction conditions across three potential substrates for the target borylation reaction. These predictions guided a high-throughput experimentation study, which identified promising conditions for further development. The most promising condition underwent four rounds of Bayesian optimization, resulting in reaction conditions optimized for both chemical yield (>99%) and cost efficiency. The entire hit-identification and optimization process was completed in just 120 experiments over the course of 1 week by a single scientist. These optimized conditions were successfully validated on a 40 g scale to achieve 83.5% isolated yield. Catalysis ; Chemistry ; Biochemical engineering ; Process engineering ; Computer science ; Nanotechnology ; Organic chemistry ; Materials science ; Engineering Machine Learning in Materials Science ; Chemistry and Chemical Engineering ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Seong‐Hoon Jang | Di Zhang | Hung Ba Tran | Xue Jia | Koichi Konno | Ryuhei Sato | Shin‐ichi Orimo | Hao Li
Source: Chemical Science (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Physically interpretable descriptors drive the materials design of metal hydrides for hydrogen storage Designing metal hydrides for hydrogen storage remains a longstanding challenge due to the vast compositional space and complex structure-property relationships. Herein, for the first time, we present physically interpretable models for predicting two key performance metrics, gravimetric hydrogen density <i>w</i> and equilibrium pressure <i>P</i> <sub>eq,RT</sub> at room temperature, based on a minimal set of chemically meaningful descriptors. Using a rigorously curated dataset of 5089 metal hydride compositions from our recently developed Digital Hydrogen Platform (<i>DigHyd</i>) based on large-scale data mining from available experimental literature of solid-state hydrogen storage materials, we systematically constructed over 1.6 million candidate models using combinations of scalar transformations and nonlinear link functions. The final closed-form models, derived from 2-3 descriptors each (<i>e.g</i>., atomic mass, electronegativity, molar density, and ionic filling factor), achieve predictive accuracies on par with state-of-the-art machine learning methods, while maintaining full physical transparency. Strikingly, descriptor-based design maps generated from these models reveal a fundamental trade-off between <i>w</i> and <i>P</i> <sub>eq,RT</sub>: saline-type hydrides, composed of light electropositive elements, offer high <i>w</i> but low <i>P</i> <sub>eq,RT</sub>, whereas interstitial-type hydrides based on heavier electronegative transition metals show the opposite trend. Notably, beryllium (Be)-based systems, such as Be-Na alloys, emerge as rare candidates that simultaneously satisfy both performance metrics, attributed to the unique combination of light mass and high molar density for Be. Our models indicate that, while there remains room for improvement between the current state of solid-state hydrogen storage materials and the US-DOE targets, Be-based systems may offer renewed prospects for approaching these benchmarks. These results provide chemically intuitive guidelines for materials design and establish a scalable framework for the rational discovery of materials in complex chemical spaces. The methodology is broadly applicable and could serve as a template for data-driven exploration across other energy-relevant materials domains. Hydrogen storage ; Gravimetric analysis ; Hydrogen ; Beryllium ; Hydride ; Metal ; Molar mass ; Nonlinear system Hydrogen Storage and Materials ; Nuclear Materials and Properties
Authors: Akeshi Aththanayake | Andrew Lininger | Cataldo Strangi | Mark A. Griswold | Giuseppe Strangi
Source: Nanophotonics (article, 2025-02-15)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Tunable holographic metasurfaces for augmented and virtual reality Augmented and virtual reality (AR/VR) is transforming how humans interact with technology in a wide range of fields and industries, from healthcare and education to entertainment. However, current device limitations have impeded wider integration. Tunable holographic metasurfaces represent a promising platform for revolutionizing AR/VR devices by precisely controlling light at the subwavelength scale. This article examines current challenges and opportunities from both the AR/VR and holographic metamaterial perspectives and explores how improvements to state-of-the-art approaches can address these challenges. In particular, we propose a focus on easily manufacturable and broadly electrically tunable metasurface technologies including liquid crystal integration and excitonic tuning in 2D materials. Advanced metasurface optimization techniques including machine learning will also be crucial for exploring the large design space. Holography ; Augmented reality ; Metamaterial ; Virtual reality ; Computer science ; Holographic display ; Focus (optics) ; Entertainment ; Nanotechnology ; Materials science ; Human–computer interaction ; Optoelectronics ; Physics ; Optics Metamaterials and Metasurfaces Applications ; Photonic Crystals and Applications ; Advanced Antenna and Metasurface Technologies
Authors: Huimin Han | Uzair Aslam Bhatti | Yonis Gulzar | Mohannad Alkanan | Mohammad Shuaib Mir | Hao Tang
Source: Spatial Information Research (article, 2025-10-22)
Field: Chemistry — Inorganic Chemistry

Precision agricultural techniques for coconut disease segmentation using enhanced remote sensing images Hyperspectral imaging ; Segmentation ; Multispectral image ; Precision agriculture ; Convolutional neural network ; Crop productivity ; Agriculture ; Remote sensing application ; Productivity Coconut Research and Applications ; Smart Agriculture and AI ; Remote-Sensing Image Classification