jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Lois Smith | John Steele | Hossein Ali Karimi-Varzaneh | Paola Carbone
Source: ACS Applied Polymer Materials (article, 2026-03-18)
Field: Materials Science — Materials Chemistry

Hierarchical Virtual Screening for Plasticizer Discovery with Active Learning Polymer solubility is a topic widely studied in polymer science; far less is understood about the factors governing the solubility of small molecules within polymer matrices. The dispersion of such molecules, known as plasticizers, in the polymer is critical for the processability of the material and an unavoidable step in the formulation of polymer composites, and yet their selection remains largely empirical, and robust design rules are almost nonexistent. Here, we present a multiscale, data-driven framework for the virtual screening of hydrocarbon-based plasticizers for rubber and polyolefins. Starting from a 730 molecule design space, our active learning strategy narrows the required sampling by an order of magnitude, to only 76 polymer/plasticizer systems, and identifies side-chain rigidity as a primary factor driving phase separation. Importantly, the framework captures packing and entropy driven contributions to miscibility in polymer-rich melts that are not represented by traditional solubility parameter approaches. Finally, by calculating the packing lengths and Voronoi volume distributions, we show that phase behavior in a key area of our design space is primarily governed by the plasticizer’s packing efficiency, providing a basis for straightforward design rules for tuning miscibility. The surrogate model reproduces available experimental data and yields physical, interpretable structure-properties relationships, identifying the key molecular features of the plasticizers that promote miscibility in polymer-rich environments. These insights provide a mechanistic understanding and actionable design rules for tuning polymer/plasticizer miscibility within nonpolar mixtures, and a predictive route for improving polymer/additive compatibility in soft-material formulations. Miscibility ; Plasticizer ; Polymer ; Hildebrand solubility parameter ; Solubility ; Entropy (arrow of time) ; Polymer blend Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Advanced Polymer Synthesis and Characterization
Authors: Muhammad Fiji | Shangcheng Yan | Prashant Kumar | Daniel Schwalbe‐Koda | Jeffrey D. Rimer
Source: Journal of the American Chemical Society (article, 2026-02-13)
Field: Chemistry — Inorganic Chemistry

Design Principles for the Synthesis of Self-Pillared ZSM-5 Zeolite Nanosheets The design of next-generation materials for emerging energy and environmental applications heavily relies on empirical approaches to direct nonclassical nucleation and crystal growth pathways, polymorphism, intercrystalline transformations, and seed-assisted growth processes. A long-standing obstacle to nanoporous materials design is the complexity of their crystallization, which hinders the development of predictive models and/or physical descriptors that can guide their synthesis. In this study, we use a combination of state-of-the-art synthesis, characterization, and computational design to prepare hierarchical MFI-type zeolites, which we couple with benchmark catalytic testing to assess structure-property-performance relationships. These hierarchical materials are intergrowths of two commercially relevant zeolite frameworks, MFI and MEL, prepared as self-pillared pentasil (SPP) zeolites through seed-assisted, organic-free syntheses for which little theoretical guidance existed. By comparing a large library of zeolite seeds with different pore sizes, dimensions, and structural composite building units, we determined the relative impact of seed and silica source selection, among other synthesis variables. Combined experimental and computational studies are used to test several hypotheses in literature to rationalize the choice of seed structure and establish a more robust selection criteria for seed-assisted synthesis of zeolites. Specifically, we show that a data-driven approach to develop structural descriptors correlates to new, facile routes to rationally design SPP zeolites, addressing knowledge gaps in the fundamental understanding of (non)classical crystal growth mechanisms that are characteristic of nanoporous aluminosilicates. Nanoporous ; Zeolite ; Benchmark (surveying) ; Nucleation ; Design elements and principles ; Selection (genetic algorithm) ; Crystal (programming language) Zeolite Catalysis and Synthesis ; Mesoporous Materials and Catalysis ; Catalysis for Biomass Conversion
Authors: Harjeet | Sunita Srivastava
Source: Journal of Energy Storage (article, 2026-01-07)
Field: Chemistry — Inorganic Chemistry

Frameworks for the Future: Metal Organic Frameworks and their Composites based solutions for Hydrogen Storage Hydrogen storage ; Metal-organic framework ; Fossil fuel ; Energy storage ; Hydrogen ; Hydrogen fuel ; Porosity ; Adsorption Metal-Organic Frameworks: Synthesis and Applications ; Hydrogen Storage and Materials ; Covalent Organic Framework Applications
Authors: Shuhei Naganuma | Jiro Kitagawa
Source: Journal of Magnetism and Magnetic Materials (article, 2026-03-24)
Field: Materials Science — Materials Chemistry

Experimental investigation of magnetic properties of MnFeCo4Si2 discovered by GNoME Curie temperature ; Ferromagnetism ; Trigonal crystal system ; Hexagonal crystal system ; Curie ; Magnet ; Focus (optics) ; Magnetic anisotropy Machine Learning in Materials Science ; Heusler alloys: electronic and magnetic properties ; Magnetic and transport properties of perovskites and related materials
Authors: Yuhang Li | Pengyu Cai | Hongxu Shi | Jili Feng | Zhongyi He | Yonggang Wang | Xiangqiong Zeng
Source: Journal of Molecular Liquids (article, 2026-01-20)
Field: Materials Science — Materials Chemistry

Green Corrosion Inhibitors for Copper Protection: Advances in Experimental and Theoretical Research Copper ; Adsorption ; Corrosion ; Density functional theory ; Corrosion inhibitor ; Erosion corrosion of copper water tubes ; Passivation ; Heteroatom Corrosion Behavior and Inhibition ; Copper-based nanomaterials and applications ; Nanoporous metals and alloys
Authors: Yuejiu Zheng | Yiduo Wang | Suran Li | Zhiyong Liu | Dongxu Guo | Yu Wang | Xuebing Han | Minggao Ouyang
Source: Journal of Energy Storage (article, 2026-01-27)
Field: Materials Science — Materials Chemistry

Efficient construction of heterogeneous scientific battery databases via a distilled dual-model framework Security token ; Battery (electricity) ; Key (lock) ; Information extraction ; Data extraction ; Extraction (chemistry) ; Component (thermodynamics) Machine Learning in Materials Science ; Advanced Battery Technologies Research ; Advanced battery technologies research
Authors: Chao Feng | Yanbo Li
Source: The Journal of Physical Chemistry Letters (article, 2026-01-29)
Field: Materials Science — Surfaces, Coatings and Films

Hard X-ray Photoelectron Spectroscopy for Depth-Resolved Analysis of Optoelectronic and Photoelectrochemical Devices Laboratory-based hard X-ray photoelectron spectroscopy (HAXPES) extends conventional XPS into the multi-keV regime, enabling 20-30 nm probing depths while preserving high chemical-state sensitivity and energy resolution. This Perspective highlights recent advances that establish HAXPES as a practical and quantitative tool for characterizing buried interfaces in optoelectronic and photoelectrochemical devices. We discuss analytical strategies (multienergy excitation, angle-resolved detection, inelastic-background modeling, and Auger-parameter analysis) that permit depth-resolved quantification of band offsets, band bending, interface dipoles, and chemical gradients while distinguishing initial-state effects from final-state screening. Representative case studies span perovskite, organic, quantum-dot, and chalcogenide photovoltaics; oxide-based optoelectronic systems; catalyst-semiconductor and protection-semiconductor junctions; and operando solid-liquid measurements that track oxidation-state evolution and ion redistribution. We also outline key challenges including cross-instrument calibration, limited laboratory photon flux, and the development of stable operando cells. Finally, we identify opportunities in standardized measurement protocols, data-driven spectral analysis, and correlative in situ and operando methods that directly link the buried electronic structure to device performance and long-term stability. X-ray photoelectron spectroscopy ; Chalcogenide ; Spectroscopy ; Interface (matter) ; Chalcogenide glass ; Band gap ; Photon ; Heterojunction ; Conduction band Electron and X-Ray Spectroscopy Techniques ; Electronic and Structural Properties of Oxides ; Machine Learning in Materials Science
Authors: Erwin Lam | Kourosh Shariat | Leo Chevalier | Guillaume Draznieks | Scott Sussex | Riccardo De Santi | Samuel Brugger | T.B. Gradinger | Yuhui Hou | Hannes Frey | Mojmír Mutný | Andreas Krause | Christophe Copéret | Paco Laveille
Source: ChemRxiv (article, 2026-04-07)
Field: Materials Science — Materials Chemistry

Comparative Study of Bayesian Optimization Strategies for Batch Optimization of Heterogeneous Catalysts in CO2 Hydrogenation High-throughput and automated experimentation platforms combined with artificial intelligence are increasingly used to accelerate catalyst discovery. Batch Bayesian optimization (BO) (i.e., the simultaneous selection of multiple candidate experiments per iteration) is widely adopted for navigating large compositional spaces; however, its practical behavior under realistic high-throughput and batch constraints remains poorly understood. In this work, we systematically evaluate four BO strategies to optimize the catalyst composition for the hydrogenation of CO2 to methanol. For each BO method, 48 catalysts were synthesized by co-precipitation, utilizing an automated synthesis platform, and evaluated in high-throughput fixed-bed reactors over seven batch iterations. The composition space comprised up to four metallic elements selected from a pool of nine. Although all BO approaches identified competitive catalyst formulations, most optimization trajectories rapidly converged toward distinct local optima that were strongly influenced by the initial random design and exhibited limited global exploration of the compositional space. This case study provides practical insights into the limitations of BO for batch catalyst optimization and highlights their strong dependence on the initial random design, parameter space definition, and batch execution strategy. Bayesian optimization ; Catalysis ; Batch processing ; Selection (genetic algorithm) ; Bayesian probability ; Batch reactor ; Batch production Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalysts for Methane Reforming
Authors: Musa Husaini
Source: Open MIND (article, 2026-02-08)
Field: Materials Science — Materials Chemistry

Biogenic Nano-Inhibitors for Sustainable and Green Corrosion Protection of Metals Corrosion of metals is a persistent challenge in industrial, construction, marine, and energy applications, leading to significant economic losses, safety hazards, and environmental contamination. Conventional chemical inhibitors, although widely used, often involve toxic compounds, poor biodegradability, and high production costs, limiting their sustainability. Biogenic nano-inhibitors, derived from plants, microbes, and renewable biomaterials, have emerged as promising eco-friendly alternatives. Their nanoscale properties enhance adsorption, surface coverage, and barrier formation on metal surfaces, resulting in superior corrosion protection. This review critically evaluates recent research on the synthesis, characterization, and performance of biogenic nano-inhibitors. Factors influencing efficiency, such as nanoparticle size, shape, surface functional groups, and bioactive components, are discussed alongside adsorption mechanisms and electrochemical behavior. Challenges in scalability, reproducibility, long-term stability, and industrial integration are highlighted. Future perspectives, including hybrid composites, computational and AI-assisted design, pilot-scale testing, and life-cycle assessment, are explored. Overall, biogenic nano-inhibitors provide a sustainable and high-performance approach to corrosion mitigation, aligning with green chemistry principles and industrial needs. Corrosion ; Renewable energy ; Limiting ; Adsorption ; Metal Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Calcium Carbonate Crystallization and Inhibition
Authors: Wout Bittremieux
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2026-02-24)
Field: Chemistry — Spectroscopy

Learning the Rules of Molecular Behavior from Open Mass Spectrometry Repositories High-throughput mass spectrometry has generated repositories containing billions of peptide and small-molecule spectra. Beyond their immediate analytical value, these repositories provide an unprecedented opportunity to uncover fundamental biophysical principles when combined with modern bioinformatics and AI. In this talk, I will illustrate how large-scale reanalysis and synthesis of public data can transform raw spectra into molecular insight. First, I will show how deep contrastive learning embeds hundreds of millions of spectra into a shared latent space, where spectra from the same peptide naturally cluster. Strikingly, this representation captures intrinsic biophysical parameters—including precursor mass, charge state, and sequence composition—without explicit supervision. The learned embedding reveals latent order in fragmentation processes and enables systematic exploration of the "dark proteome." Second, I will introduce a language-model approach to de novo peptide sequencing that treats mass spectra as a symbolic language of fragment ions. By directly translating spectra into amino acid sequences, the model learns the physics of peptide fragmentation from data alone. In addition to outperforming prior methods, it demonstrates that complex, sequence-dependent fragmentation pathways can be captured through large-scale, data-driven modeling. Finally, I will highlight the construction of a repository-scale "suspect library" that links hundreds of millions of spectra into molecular networks. By propagating structural information across related spectra, this approach uncovers families of metabolites connected by systematic chemical modifications. The resulting network maps expose how structural diversity emerges across metabolism, natural products, and environmental chemistry. Together, these examples illustrate how coupling large-scale public data with powerful AI approaches enables us to extract generalizable physical and chemical principles that govern molecular behavior in biological systems. Fragmentation (computing) ; Representation (politics) ; Raw data ; Embedding ; Mass spectrum ; Peptide ; Sequence (biology) Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; RNA and protein synthesis mechanisms
Authors: Bekzod Nomozov
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2026-02-16)
Field: Materials Science — Materials Chemistry

THE HISTORY AND FUTURE OF COMPOSITE MATERIALS Composite materials have played a transformative role in human technological development, from ancient straw-reinforced clay bricks to advanced carbon fiber-reinforced polymers used in aerospace and biomedical engineering. This thesis examines the historical evolution of composite materials, analyzes key scientific and technological breakthroughs, and explores future trends including nanocomposites, smart materials, sustainable composites, and additive manufacturing applications. The study applies a qualitative literature review methodology combined with comparative technological analysis. Findings demonstrate that composite materials have consistently driven innovation by offering superior strength-to-weight ratios, corrosion resistance, durability, and multifunctionality. Future development is expected to focus on sustainability, recyclability, integration of artificial intelligence in material design, and bio-inspired composite structures. The research confirms that composite materials will remain central to aerospace, automotive, construction, renewable energy, and medical industries. Transformative learning ; Aerospace ; Composite number ; Component (thermodynamics) ; Sustainable development Material Selection and Properties ; Natural Fiber Reinforced Composites ; Fiber-reinforced polymer composites
Authors: Shiwei Su | Nobuyuki Tanaka | Yoshitaka Ushiku | Kazuya Takahashi
Source: ACS Applied Materials & Interfaces (article, 2026-04-06)
Field: Materials Science — Surfaces, Coatings and Films

BB-EIT: A Generalized Prediction Model for Protein Adsorption on Polymer Brushes Using Augmented Chemical Embeddings Precise control of protein adsorption on polymer surfaces is essential in materials science and biomaterial design, with applications in antifouling materials, biosensors, cell culture, and drug delivery systems. However, the complex interactions between polymers and proteins and the limited availability of high-quality interaction data remain major challenges in polymer informatics. Current approaches often lack the generalizability needed to model diverse polymer-protein systems within a single unified framework, and there is a paucity of comprehensive predictive models capable of handling diverse polymer-protein interactions. To address these challenges, we introduce BB-EIT (Biointerface BERT Encoder for Interaction Translation), a novel generalized model designed to accurately predict the amount of diverse protein adsorption on polymer brushes. BB-EIT leverages the pretrained ChemBERTa large language model (LLM) architecture using SMILES strings for robust chemical representation and convenient data augmentation through SMILES enumeration. By adapting the pretrained model with an extended layer integrating a comprehensive set of physicochemical and biochemical features, including polymer thickness, water contact angle, and surface charge as well as protein isoelectric point (pI) and size, the BB-EIT showed state-of-the-art performance and strong generalizability. The model accurately predicted the adsorption behavior in previously unseen polymer and protein systems. This work represents an important step toward the data-driven design of biomaterials with tailored protein adsorption properties. Polymer ; Protein adsorption ; Isoelectric point ; Adsorption ; Representation (politics) ; Generalizability theory ; Set (abstract data type) ; Surface (topology) Polymer Surface Interaction Studies ; Hydrogels: synthesis, properties, applications ; Diatoms and Algae Research
Authors: Hyunsoo Park | Tianshu Li | Ashna Jose | Seung-Jae Shin | Aron Walsh
Source: ChemRxiv (article, 2026-02-24)
Field: Chemistry — Inorganic Chemistry

Data-Driven Design of Metal-Organic Frameworks for Photoelectrochemical Reactions Metal-organic frameworks (MOFs) are modular materials that are chemical versatile, offering vast design possibilities through the combination of metal clusters, organic ligands, and framework topologies. Traditionally utilized in gas storage and separation, the high porosity and tunable properties of MOFs position them as attractive candidates for (photo)electrochemical applications, including batteries, fuel cells, and solar-driven fuel conversion. We present a data-driven workflow integrating hybrid quantum chemical calculations with a multimodal AI model (MOFTransformer) to efficiently predict oxidation and reduction potentials for over 270,000 known and hypothetical MOFs. Through high-throughput screening, we identify optimal candidates tailored for coupled solar-driven CO2-to-CO reduction and water splitting, highlighting promising structural motifs and properties for targeted energy conversion reactions with significantly reduced computational effort compared to traditional approaches. Modular design ; Workflow ; Metal-organic framework ; Quantum chemical ; Reduction (mathematics) ; Energy transformation ; Porosity ; Efficient energy use Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Covalent Organic Framework Applications
Authors: Bekzod Nomozov
Source: Open MIND (article, 2026-02-16)
Field: Materials Science — Materials Chemistry

THE HISTORY AND FUTURE OF COMPOSITE MATERIALS Composite materials have played a transformative role in human technological development, from ancient straw-reinforced clay bricks to advanced carbon fiber-reinforced polymers used in aerospace and biomedical engineering. This thesis examines the historical evolution of composite materials, analyzes key scientific and technological breakthroughs, and explores future trends including nanocomposites, smart materials, sustainable composites, and additive manufacturing applications. The study applies a qualitative literature review methodology combined with comparative technological analysis. Findings demonstrate that composite materials have consistently driven innovation by offering superior strength-to-weight ratios, corrosion resistance, durability, and multifunctionality. Future development is expected to focus on sustainability, recyclability, integration of artificial intelligence in material design, and bio-inspired composite structures. The research confirms that composite materials will remain central to aerospace, automotive, construction, renewable energy, and medical industries. Transformative learning ; Aerospace ; Composite number ; Component (thermodynamics) ; Sustainable development Material Selection and Properties ; Natural Fiber Reinforced Composites ; Fiber-reinforced polymer composites
Authors: Xinru Song | Gang Li | Pinghui Ge | Chonghe Xu | Lin Wang | Xuetao Yue | Yang Hua
Source: Smart Materials and Structures (article, 2026-04-02)
Field: Materials Science — Materials Chemistry

Status and challenges of mechanoluminescent materials: from basic research to smart sensing applications Abstract In recent years, as a kind of functional material that can directly convert mechanical energy into light energy, mechanoluminescent (ML) materials have shown a broad application in the fields of sensing, medical treatment, and information storage.Progress of ML materials are reviewed in this paper, including their luminescence mechanism, classification, developmental history, representative material systems, and application. The luminescence intensity, sensitivity and stability of the materials can be significantly optimized by modulating the matrix composition, doping ions, and synthesis methods. In terms of applications, ML materials have been successfully used in biosensing, healthcare, information storage, smart wearables, and anti-counterfeiting imaging. In the future, with the further improvement of material properties and the integration with artificial intelligence and other technologies, ML materials are expected to realize a wider range of application prospects in areas such as the Internet of Things, smart sensing, and green energy. Smart material ; Mechanoluminescence ; Basic research ; Luminescence ; Doping ; Sensitivity (control systems) ; Semiconductor materials ; Internet of Things Luminescence Properties of Advanced Materials ; Luminescence and Fluorescent Materials ; Nanoplatforms for cancer theranostics