jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Wei Bin How | Pol Febrer | Sanggyu Chong | Arslan Mazitov | Filippo Bigi | Matthias Kellner | Sergey Pozdnyakov | Michele Ceriotti
Source: Digital Discovery (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

A universal machine learning model for the electronic density of states In the last few years several "universal" interatomic potentials have appeared, using machine-learning approaches to predict energy and forces of atomic configurations with arbitrary composition and structure, with an accuracy often comparable with that of the electronic-structure calculations they are trained on. Here we demonstrate that these generally-applicable models can also be built to predict explicitly the electronic structure of materials and molecules. We focus on the electronic density of states (DOS), and develop PET-MAD-DOS, a rotationally unconstrained transformer model built on the Point Edge Transformer (PET) architecture, and trained on the Massive Atomic Diversity (MAD) dataset. We demonstrate our model's predictive abilities on samples from diverse external datasets, showing also that the DOS can be further manipulated to obtain accurate bandgap predictions. A fast evaluation of the DOS is especially useful in combination with molecular simulations probing matter in finite-temperature thermodynamic conditions. To assess the accuracy of PET-MAD-DOS in this context, we evaluate the ensemble-averaged DOS and the electronic heat capacity of three technologically relevant systems: lithium thiophosphate (LPS), gallium arsenide (GaAs), and a high entropy alloy (HEA). By comparing with bespoke models, trained exclusively on system-specific datasets, we show that our universal model achieves semi-quantitative agreement for all these tasks. Furthermore, we demonstrate that fine-tuning can be performed using a small fraction of the bespoke data, yielding models that are comparable to, and sometimes better than, fully-trained bespoke models. Energy (signal processing) ; Interatomic potential ; Computational learning theory ; Feature (linguistics) ; Electronic structure ; Artificial neural network Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions ; Advanced Chemical Physics Studies
Authors: Tian Gu | Chengzhi Wang | Fengyu Gao | Qiumei Hou | Dong Zhao | Zhongliang Zhang
Source: Fuel (article, 2026-01-14)
Field: Materials Science — Materials Chemistry

Recent advances in NH3-SCR of NOx by Fe-based catalysts: design strategies, optimization pathways and influencing factors NOx ; Flue gas ; Catalysis ; Sulfidation ; Selective catalytic reduction ; Design elements and principles ; Rational design Catalytic Processes in Materials Science ; Industrial Gas Emission Control ; Advanced oxidation water treatment
Authors: Sara Estaji | Shahab Moghari | Sogand Ahmadi | Pouya Khattami Kermanshahi | Hosein Ali Khonakdar
Source: Chemical Engineering Journal Advances (article, 2026-01-10)
Field: Materials Science — Materials Chemistry

Recent trends in porous materials science doped with MXene: toward AI-assisted design and performance optimization MXenes are an emerging family of two-dimensional transition metal carbides and nitrides that have attracted considerable attention due to their exceptional electrical conductivity, surface chemistry tunability, and layered architecture. Despite rapid progress, the rational design of porous MXene-based composites remains fragmented, with limited cross-cutting analyses that connect porous-host selection, structure–property relationships, scalability, and data-driven optimization. In this review, building upon and extending our prior contributions, we present a comprehensive and critical synthesis of organic and inorganic porous materials integrated with MXenes, including metal–organic frameworks (MOFs), covalent organic frameworks (COFs), zeolites, porous polymers, and carbon-based hosts. Beyond a descriptive survey, this work introduces a unified comparative framework that quantitatively contrasts porous hosts in terms of conductivity enhancement, mechanical robustness, manufacturability, and compatibility with machine-learning-assisted optimization. One of the key innovations of this review is the explicit integration of artificial intelligence into porous MXene materials design: we systematically analyze AI-assisted and non-AI design paradigms, propose a closed-loop AI–experiment workflow, and introduce a figure of merit (FoM_AI design) to benchmark predictive reliability, data efficiency, and resource utilization across different AI strategies. By linking hierarchical porosity, MXene electronic structure, and data-centric intelligence, this review moves beyond conventional trial-and-error approaches and establishes a conceptual roadmap for predictive, scalable, and application-oriented porous MXene composites. The insights provided here define clear short-, mid-, and long-term research directions for next-generation materials targeting energy storage, electrocatalysis, sensing, electromagnetic interference shielding, and environmental remediation. MXenes ; Porous medium ; Porosity ; Compatibility (geochemistry) ; Figure of merit ; Material Design ; Mesoporous material ; Rational design MXene and MAX Phase Materials ; Electromagnetic wave absorption materials ; Advanced Memory and Neural Computing
Authors: Yufan Liu | Dexin Zhu | Zhihao Tian | Jiayi Liu | Xing Ran | Zhe Wang | Chengjiang Tang | Lifei Wang | Wei Xu | Xin Lü
Source: International Journal of Minerals Metallurgy and Materials (article, 2026-02-23)
Field: Materials Science — Materials Chemistry

Transfer learning-enabled performance prediction of metallic materials: Methods, applications and prospects Leverage (statistics) ; Interpretability ; Transfer of learning ; Inductive transfer ; Generalization ; Knowledge transfer ; Adaptive learning Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Advanced Graph Neural Networks
Authors: Janine L. Gray | Zhangping Xiao | Vanessa V. Rogga | Xinyue Zhang | Edward W. Tate
Source: Journal of the American Chemical Society (article, 2026-02-02)
Field: Chemistry — Organic Chemistry

From Serendipity to Strategy: Rationalizing Molecular Glue Discovery and Proximity-Induced Pharmacology through Chemical Biology Molecular glues represent a new paradigm in drug discovery, stabilizing novel protein-protein interactions between two proteins to elicit targeted cellular outcomes. Historically discovered through serendipity, molecular glue identification is becoming increasingly systematic. In this perspective, we discuss the current advances and challenges of this field, highlighting the transition toward rational discovery through the convergence of four complementary approaches. Innovations in library design and screening platforms are expanding access to glue-relevant chemical space, guided by a deeper mechanistic understanding of proximity-induced pharmacology. These efforts are further enabled by functional genomic approaches that reveal gluable interfaces. Finally, the integration of chemical and biological data through machine learning is beginning to support rational de novo glue design. Serendipity ; Drug discovery ; Chemical biology ; Identification (biology) ; Rational design ; Chemical genetics ; Pharmaceutical technology Click Chemistry and Applications ; Chemical Synthesis and Analysis ; Protein Degradation and Inhibitors
Authors: Seher Polat | Mucize Sarıhan | Roya Boudaghi Malidarreh3 | Sabiha Anas Boussaa | Nurdan Karpuz | Nuray KUTU
Source: International Journal of Applied Sciences and Radiation Research (article, 2026-02-02)
Field: Materials Science — Materials Chemistry

ANN Prediction of Linear Attenuation Coefficients for 40MgO–30B2O3–30SiO2 System Radiation shielding is of great importance for protecting human health, which has led to increasing research on alternative shielding materials. Glass materials are widely used in various applications, making it essential to investigate their radiation attenuation properties. In this study, the radiation shielding characteristics of magnesium borosilicate glasses were predicted by partially substituting B₂O₃ with MgO using Artificial Neural Network (ANN). The glass samples were prepared with compositions of 30SiO₂–30B₂O₃–40MgO. The linear attenuation coefficient (LAC) was obtained and compared with the results obtained Phy-X/PSD. Attenuation ; Borosilicate glass ; Electromagnetic shielding ; Attenuation coefficient ; Radiation shielding ; Artificial neural network Radiation Shielding Materials Analysis ; Polymer Nanocomposite Synthesis and Irradiation ; Graphite, nuclear technology, radiation studies
Authors: Devavrat Thosar | Abhijit Bhakte | Zukui Li | Rajagopalan Srinivasan | Vinay Prasad
Source: Computers & Chemical Engineering (article, 2026-03-03)
Field: Materials Science — Materials Chemistry

Adaptive physics-informed neural network-based digital twins integrated with Ensemble Kalman Filter Process (computing) ; Artificial neural network ; Adaptive filter ; Kalman filter ; Digital filter ; Ensemble Kalman filter ; Filter (signal processing) ; Reduction (mathematics) Machine Learning in Materials Science ; Digital Transformation in Industry ; Advanced Polymer Synthesis and Characterization
Authors: Artur Wodyński | Martin Kaupp
Source: Journal of Computational Chemistry (article, 2026-01-24)
Field: Materials Science — Materials Chemistry

Local‐Hybrid Functional With a Composite Local Mixing Function Built From a Neural Network and a Strong‐Correlation Model Due to their position-dependent admixture of the exact-exchange (EXX) energy density, local hybrid functionals (LHs) enable a flexible balance between reduced self-interaction errors and smaller static-correlation errors, allowing an escape from the usual zero-sum game between these two central aspects of the development of density functional approximations. Recent LHs with strong-correlation factors incorporated into their local mixing functions (LMFs) governing the position-dependence of EXX admixtures have been particularly successful in this context. As only few exact constraints for LMFs are known regarding valence-space behavior, some recent efforts have used machine learning in this context, and the recent LH24n functional with a "neural-network LMF" (n-LMF, DOI: 10.1021/acs.jctc.4c01503) has shown excellent performance for the large GMTKN55 test suite of main-group energetics. However, so far the construction of n-LMFs that also cover strong-correlation effects has not been successful. Here we report the LH25nP functional that has an n-LMF optimized in the presence of a fixed strong-correlation factor. LH25nP-D4 achieves a remarkable self-consistent WTMAD-2 value of 2.47 kcal/mol for the GMTKN55 set, the so far lowest value for a rung 4 functional. Mean absolute deviations of 2.4 kcal/mol for the large W4-11RE reaction-energy set are also the lowest known currently for rung 4. At the same time, very low fractional-spin errors and excellent performance for the spin-restricted dissociation of covalent bonds, as well as a curing of spin-contamination problems in open-shell transition-metal complexes has been found, suggesting a clear deviation from the usual zero-sum behavior. Transferability to organometallic transition-metal energetics is so far less favorable, suggesting the need for a wider training of n-LMFs that includes data for transition-metal systems. Transferability ; Dissociation (chemistry) ; Hybrid functional ; Artificial neural network ; Mixing (physics) ; Mean absolute error ; Density functional theory ; Composite number ; Absolute deviation Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Prince Kumar Soni | Manmohan L. Satnami | Rekha Nagwanshi | Yogyata Chawre | Ankita Beena Kujur | Akash Sinha | Pinki Miri | Indrapal Karbhal | Kallol K. Ghosh
Source: Nano Today (article, 2026-01-13)
Field: Materials Science — Materials Chemistry

AI-driven design and applications of quantum dots Workflow ; Scalability ; Quantum dot ; Domain (mathematical analysis) ; Flexibility (engineering) ; Variety (cybernetics) ; Big data ; Space (punctuation) Machine Learning in Materials Science ; Quantum Dots Synthesis And Properties ; Nanocluster Synthesis and Applications
Authors: Nawaf Alampara | Anagha Aneesh | Martiño Ríos-García | A.H. Mirza | Mara Schilling-Wilhelmi | Ali Asghar Aghajani | Meiling Sun | Gordan Prastalo | Kevin Maik Jablonka
Source: Chemical Reviews (article, 2026-02-05)
Field: Materials Science — Materials Chemistry

General-Purpose Models for the Chemical Sciences: LLMs and Beyond Data-driven techniques have a large potential to transform and accelerate the chemical sciences. However, chemical sciences also pose the unique challenge of very diverse, small, fuzzy data sets that are difficult to leverage in conventional machine learning approaches. A new class of models, which can be summarized under the term general-purpose models (GPMs) such as large language models, has shown the ability to solve tasks they have not been directly trained on, and to flexibly operate with low amounts of data in different formats. In this review, we discuss the fundamental building principles of GPMs and review recent and emerging applications of those models in the chemical sciences. While many of these applications are still in the prototype phase, we expect that the increasing interest in GPMs will make many of them mature in the coming years. Leverage (statistics) ; Class (philosophy) ; Fuzzy logic ; Chemical toxicity ; Chemical products Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning in Bioinformatics
Authors: Aceer Nadeem | Maryam Rahmani | Yibo Wang | Sepehr Yari | Rodrigo Monroy Lopez | Mijin Kim | Daniel Roxbury
Source: ACS Nano (article, 2026-01-26)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Spectral Fingerprinting of Engineered Nanomaterials for Precision Biosensing Biological systems comprise a complex milieu of macromolecules, small molecules, and ions comprising tens of thousands of distinct species. Various clinical conditions alter the identities and concentrations of these species in a spatiotemporal-dependent manner. While bioanalytical methods such as omics or biochemical assays can precisely identify the targeted biomolecules over space and time, providing in-depth information on biological processes, they are generally considered low-throughput and costly. Spectral fingerprinting of engineered nanomaterials (SFEN) has emerged as an alternative method that addresses many of these limitations in the field of disease detection and chemical biology research. This approach leverages one or more closely related types of engineered nanomaterials to detect subtle biological differences via optical readout such as near-infrared fluorescence or surface-enhanced Raman spectroscopy. Variations of the technique have been developed to detect single or multiplexed target biomarkers as well as whole-cell- and organism-level biological states. In recent years, the incorporation of advanced analytical methods, such as feature extraction and machine learning, has significantly expanded the SFEN capabilities for broader applications with high accuracy. This perspective highlights recent developments of SFEN applications including but not limited to machine-learning-assisted live-cell phenotyping, serum-based cancer detection, and pathogen identification. We further comment on the future directions of this promising technology, which we envision will synergize with next-generation nano-omics and generative AI methods. Bioanalysis ; Nanomaterials ; Biomolecule ; Biosensor ; Foodborne pathogen ; Synthetic biology ; Identification (biology) Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Nanomaterials in Catalysis
Authors: Yury Lysogorskiy | Anton Bochkarev | Ralf Drautz
Source: npj Computational Materials (article, 2026-02-07)
Field: Materials Science — Materials Chemistry

Graph atomic cluster expansion for foundational machine learning interatomic potentials Abstract Foundational machine learning interatomic potentials that can accurately and efficiently model a vast range of materials are critical for accelerating atomistic discovery. We introduce universal potentials based on the graph atomic cluster expansion (GRACE) framework, trained on several of the largest available materials datasets. Through comprehensive benchmarks, we demonstrate that the GRACE models establish a new Pareto front for accuracy versus efficiency among foundational interatomic potentials. We further showcase their exceptional versatility by adapting them to specialized tasks and simpler architectures via fine-tuning and knowledge distillation, achieving high accuracy while preventing catastrophic forgetting. This work establishes GRACE as a robust and adaptable foundation for the next generation of atomistic modeling, enabling high-fidelity simulations across the periodic table. Interatomic potential ; Cluster (spacecraft) ; Graph ; Cluster expansion ; Range (aeronautics) ; Work (physics) Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Artificial Intelligence in Healthcare and Education
Authors: Bilvin Varughese | Troy D. Loeffler | Suvo Banik | Aditya Koneru | Sukriti Manna | Karthik Balasubramanian | Rohit Batra | Mathew J. Cherukara | Orcun Yildiz | Tom Peterka | Bobby G. Sumpter | Subramanian KRS Sankaranarayanan
Source: npj Computational Materials (article, 2026-01-13)
Field: Materials Science — Materials Chemistry

Physically interpretable interatomic potentials via symbolic regression and reinforcement learning The development of next-generation molecular simulation models requires moving beyond predefined functional forms toward machine learning (ML) techniques that directly capture multiscale physics. Here, we demonstrate such an approach using symbolic regression (SR) with equation learner networks and a reinforcement learning search engine to derive interpretable equations for interatomic interactions. Training data were generated through nested ensemble sampling with density functional theory (DFT) energetics, spanning crystalline to highly disordered states. The optimization of the learner network employed continuous-action Monte Carlo Tree Search (MCTS) combined with gradient descent, enabling efficient exploration of function space. For copper as a representative transition metal, an unconstrained search produced models that outperformed fixed-form Sutton–Chen EAM potentials. The SR-derived models (SR1 and SR2) reproduced key material properties—lattice constants, cohesive energies, equations of state, elastic constants, phonon dispersion, defect formation energies, surface/bulk energetics, and phase transformation with significantly improved accuracy. Furthermore, stringent melting simulations using two-phase solid-amorphous interfaces confirmed that SR models accurately capture the interplay of vibrational entropy, cohesive energy, and structural dynamics, surpassing SC-EAM in both qualitative and quantitative predictions. This highlights the potential of SR to deliver fast, accurate, flexible, and physically meaningful potentials, advancing predictive modeling across scales. Symbolic regression ; Transformation (genetics) ; Artificial neural network ; Function (biology) ; Reinforcement learning ; Regression ; Monte Carlo method ; Key (lock) Machine Learning in Materials Science ; Quantum many-body systems ; Inorganic Chemistry and Materials
Authors: Ching-Chia Yang | Ching-Ya Lin | Hsin-Yo Yuan | Hsuan-Cheng Huang | Hsueh-Fen Juan
Source: Journal of Biomedical Science (article, 2026-02-06)
Field: Chemistry — Spectroscopy

Mass spectrometry-based human spatial omics: fundamentals, innovations, and applications Mass spectrometry-based spatial omics is a powerful approach for visualizing the spatial organization of proteins, metabolites, lipids, and other biomolecules in situ, combining the molecular depth of mass spectrometry with spatially resolved imaging. This systematic review traces the rapid technological and computational evolution of this field, including innovations in mass spectrometry imaging (MSI), labeling-based approaches, and proximity labeling techniques. It also highlights recent advances that enhance spatial resolution, expand molecular coverage, and enable deep molecular characterization and review analytical pipelines that integrate deep learning, cross-modality registration, and cloud-optimized data formats. From the multimodal and practical perspective, the integration of MSI with other spatial omics platforms and its transformative applications in tumor microenvironment profiling, neurodegenerative disease, developmental biology, biomarker discovery, and precision medicine are discussed. Finally, this review outlines challenges and opportunities, emphasizing the need for standardization, clinical validation, and interpretable artificial intelligence to enable broader adoption. These advances position MS-based spatial omics as a foundational pillar for multimodal spatial biology and personalized healthcare. Proteogenomics ; Spatial analysis ; Transformative learning ; Precision medicine ; Omics ; Systems biology ; Cyberinfrastructure ; Personalized medicine Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Biotin and Related Studies
Authors: Arul Murugesan | Huanhuan Li
Source: Comprehensive Reviews in Food Science and Food Safety (article, 2026-01-01)
Field: Materials Science — Biomaterials

Toward Sustainable Food Packaging: Innovations in Biodegradable Materials, Smart Technologies, and AI Integration Sustainable food packaging is gaining importance as the world increasingly addresses environmental concerns, food waste, and safety. Although traditional plastic packaging has long been effective, it significantly contributes to pollution and relies on nonrenewable resources. In response, this review highlights recent advancements in sustainable packaging, focusing on biodegradable, compostable, and recyclable materials. Moreover, it explores the integration of active and smart packaging systems, as well as the emerging role of artificial intelligence (AI) in improving packaging efficiency. Material innovations, such as biopolymers, cellulose-based films, algae-derived coatings, and nanomaterials, offer eco-friendly alternatives while still maintaining essential functions like barrier protection, antimicrobial activity, and shelf-life extension. In addition, active and smart packaging technologies enhance preservation through antimicrobial and antioxidant agents and also enable real-time monitoring of food freshness via embedded sensors. Furthermore, AI technologies, particularly machine learning, support the optimization of material selection, shelf-life prediction, and quality control through data analytics. Ultimately, the synergy between sustainable materials and digital technologies is transforming the food packaging landscape. This integrated approach, therefore, offers a promising route to reduce environmental impact, enhance food safety, and improve operational efficiency throughout the supply chain. Active packaging ; Food packaging ; Food industry ; Food quality ; Quality (philosophy) ; Food safety ; Environmental pollution ; Emerging technologies Nanocomposite Films for Food Packaging ; Consumer Packaging Perceptions and Trends ; biodegradable polymer synthesis and properties