jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jana Radaković | Katarina Batalović | Nikola Novaković
Source: Journal of Cheminformatics (article, 2026-02-09)
Field: Materials Science — Materials Chemistry

Word embeddings as autonomous predictors in materials design—the effect of inherent variability on information transfer We propose that word embeddings of atoms derived from scientific literature are revisited as autonomous machine learning predictors in materials design. If static word embeddings encode comprehensive physicochemical information, joined embeddings of chemical elements constituting a chemical compound represent a viable source of physicochemical knowledge. Nevertheless, static word embeddings are susceptible to variability due to information heterogeneity within training material. We analysed whether variability occurs in embeddings affiliated with physicochemical entities, including explicit atoms, and whether it affects therein-encoded domain-specialized information or inhibits the information transfer. Results demonstrate the substantial variability in individual atomic embeddings, which is highly dependent on vocabulary terms selected for language modelling. Regardless, variability does not obstruct the mapping of materials' composite predictors into physicochemical properties when joined atomic embeddings are implemented within a regression model estimating the compound stability by predicting its formation energy. Moreover, the encoded information and the model's predictive performance maintained stability following compound vector calibration via dimensional reduction.Scientific contributionThe magnitude of variability in word embeddings of physicochemical entities, including chemical elements, occurring due to information heterogeneity in complementary training material of materials science, chemistry, and physics scientific literature was observed and quantified. The research shows that notable variability of vectorial representations of chemical elements does not obstruct the underlying statistical properties, nor does it inhibit the information transfer. Accordingly, regardless of their origin, conjoined atomic embeddings representing chemical compounds facilitate stable predictive performance when implemented within a regression model. ENCODE ; Word (group theory) ; Stability (learning theory) ; Vocabulary ; Regression ; Support vector machine ; Chemical stability ; Regression analysis Machine Learning in Materials Science ; Computational Drug Discovery Methods ; History and advancements in chemistry
Authors: Maksim Kuznetsov | Zulfat Miftahutdinov | Rim Shayakhmetov | Mikolaj Mizera | Roman Schutski | Bogdan Zagribelnyy | Ivan Ilin | Nikita Bondarev | Thomas MacDougall | Mathieu Reymond | Mihir Bafna | Kaeli Kaymak-Loveless | Eugene Babin | Maxim Malkov | Lechner, Mathias | Ramin Hasani | Alexander Amini | Vladimir Aladinskiy | Alex Aliper | Alex Zhavoronkov
Source: ArXiv.org (article, 2026-03-03)
Field: Materials Science — Materials Chemistry

MMAI Gym for Science: Training Liquid Foundation Models for Drug Discovery General-purpose large language models (LLMs) that rely on in-context learning do not reliably deliver the scientific understanding and performance required for drug discovery tasks. Simply increasing model size or introducing reasoning tokens does not yield significant performance gains. To address this gap, we introduce the MMAI Gym for Science, a one-stop shop molecular data formats and modalities as well as task-specific reasoning, training, and benchmarking recipes designed to teach foundation models the 'language of molecules' in order to solve practical drug discovery problems. We use MMAI Gym to train an efficient Liquid Foundation Model (LFM) for these applications, demonstrating that smaller, purpose-trained foundation models can outperform substantially larger general-purpose or specialist models on molecular benchmarks. Across essential drug discovery tasks - including molecular optimization, ADMET property prediction, retrosynthesis, drug-target activity prediction, and functional group reasoning - the resulting model achieves near specialist-level performance and, in the majority of settings, surpasses larger models, while remaining more efficient and broadly applicable in the domain. Foundation (evidence) ; Drug discovery ; Benchmarking ; Modalities ; Training (meteorology) ; Discovery learning Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Zhongbao Hu | Zhoukun Wang | Yunyi Peng | Zhen Gao | Chanyuan Jin | Zechao Zhuang | Jian-Feng Li
Source: Communications Chemistry (article, 2026-03-24)
Field: Materials Science — Materials Chemistry

Machine learning-guided design of energy-related catalysts from nanoparticles to single-atom sites Machine learning (ML) has been widely applied to accelerate the design and discovery of new molecules and materials. Recently, there has been growing interest in integrating ML into the design of heterogeneous catalysts, including nanoparticle catalysts (NC) and, in particular, single-atom catalysts (SACs). With the rapid advancements in related research, there is an urgent need for a timely and comprehensive review in the catalysis and ML communities to provide valuable insights and guidance for future research. Here we summarize the recent advances in using ML to develop NCs and SACs for energy applications, with a focus on the characteristics of ML models, particularly in selecting features and descriptors from atom-scale structural information. First, we introduce the main ML algorithms currently in use and present examples of their application in the design of traditional NCs, which can offer valuable insights and methodological references for SAC research. We then provide a detailed overview of ML-assisted SAC design, including the establishment of structure-performance relationships, high-throughput screening, and stability prediction. Finally, we identify the challenges in this emerging field and discuss the opportunities for developing next-generation SACs using reliable datasets and advanced ML models to create better catalysts for energy applications. Field (mathematics) ; Catalysis ; Selection (genetic algorithm) ; Nanoparticle ; Rational design ; Reactor design Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; CO2 Reduction Techniques and Catalysts
Authors: Changwen Xu | Shang Zhu | Venkatasubramanian Viswanathan
Source: Nature Communications (article, 2026-03-17)
Field: Materials Science — Materials Chemistry

CLOUD: A Scalable and Physics-Informed Foundation Model for Crystal Representation Learning Predicting crystal properties is essential for understanding structure-property relationships and accelerating material discovery. However, conventional approaches like experimental measurements or density functional theory calculations are resource-intensive, limiting their scalability. While machine learning offers a promising alternative by learning complex structure-property relationships from data, existing models often rely on labeled data, adopt representations insufficiently capturing essential structural characteristics, and lack integration of physics, limiting their generalizability and interpretability. Here, we introduce CLOUD (Crystal Language mOdel for Unified and Differentiable materials modeling), a transformer-based framework trained on a Symmetry-Consistent Ordered Parameter Encoding (SCOPE) that encodes crystal symmetry, Wyckoff positions, and composition in a compact, coordinate-free string representation. Pre-trained on over six million crystals, CLOUD is fine-tuned on downstream tasks and achieves competitive performance across diverse material properties, demonstrating strong scaling with respect to data and model size. Furthermore, as a proof-of-concept of differentiable materials modeling, CLOUD is applied to predict the phonon-related properties by integrating with the Debye model. This approach enforces thermodynamic consistency and enables temperature-dependent property prediction without requiring additional data. These results demonstrate CLOUD's potential as a scalable and physics-informed foundation model for crystalline materials, unifying symmetry-consistent representations with physics-grounded learning for property prediction and materials discovery. Scalability ; Differentiable function ; Consistency (knowledge bases) ; Property (philosophy) ; Representation (politics) ; Cloud computing ; String (physics) ; Limiting ; Generalizability theory Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Inorganic Chemistry and Materials
Authors: Umut Saraç | Lam Vu Truong | Dung Nguyen Trong | Hoang Thi Phuong | Ştefan Ţălu
Source:  (article, 2026-01-28)
Field: Materials Science — Materials Chemistry

Recent advances in the application of Density Functional Theory (DFT) and Molecular Dynamics (MD) simulations to elucidate metal corrosion mechanisms Over the past decade, atomistic simulation techniques, particularly Density Functional Theory (DFT) and Molecular Dynamics (MD), have become essential for understanding metal corrosion at electronic and atomic scales. DFT offers quantitative insight into adsorption energetics, electronic structure evolution, and reaction pathways governing metal–environment interactions, whereas MD enables dynamic modeling of ion transport, inhibitor adsorption, and passive film stability under realistic conditions. Recent studies show that integrating DFT and MD significantly improves predictive understanding of corrosion involving aggressive species (Cl⁻, SO₄²⁻, H⁺), oxide film growth, alloying effects, and inhibitor performance. Moreover, multiscale approaches linking atomistic simulations with continuum models allow quantitative prediction of corrosion kinetics and material degradation. This review summarizes advances in DFT- and MD-based corrosion modeling from 2020 to 2025, discusses key methodological limitations, and highlights emerging trends such as reactive force fields, ab initio molecular dynamics, and machine learning-assisted simulations, with emphasis on validation and predictive reliability. Molecular dynamics ; Density functional theory ; Corrosion ; Oxide ; Stability (learning theory) ; Ab initio Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Machine Learning in Materials Science
Authors: Xiaochen Du | Juno Nam | Sulin Liu | Rafael Gomez-Bombarelli
Source: ArXiv.org (article, 2026-03-16)
Field: Materials Science — Materials Chemistry

Scaling Autoregressive Models for Lattice Thermodynamics Predicting how materials behave under realistic conditions requires understanding the statistical distribution of atomic configurations on crystal lattices, a problem central to alloy design, catalysis, and the study of phase transitions. Traditional Markov-chain Monte Carlo sampling suffers from slow convergence and critical slowing down near phase transitions, motivating the use of generative models that directly learn the thermodynamic distribution. Existing autoregressive models (ARMs), however, generate configurations in a fixed sequential order and incur high memory and training costs, limiting their applicability to realistic systems. Here, we develop a framework combining any-order ARMs, which generate configurations flexibly by conditioning on any known subset of lattice sites, with marginalization models (MAMs), which approximate the probability of any partial configuration in a single forward pass and substantially reduce memory requirements. This combination enables models trained on smaller lattices to be reused for sampling larger systems, while supporting expressive Transformer architectures with lattice-aware positional encodings at manageable computational cost. We demonstrate that Transformer-based any-order MAMs achieve more accurate free energies than multilayer perceptron-based ARMs on both the two-dimensional Ising model and CuAu alloys, faithfully capturing phase transitions and critical behavior. Overall, our framework scales from $10 \times 10$ to $20 \times 20$ Ising systems and from $2 \times 2 \times 4$ to $4 \times 4 \times 8$ CuAu supercells at reduced computational cost compared to conventional sampling methods. Ising model ; Autoregressive model ; Monte Carlo method ; Scaling ; Lattice (music) ; Importance sampling ; Phase transition ; Sampling (signal processing) Machine Learning in Materials Science ; Quantum many-body systems ; Theoretical and Computational Physics
Authors: Xudong Jia | Li Chen | Kun Xiong | Yujie Wang | Linjie Zhou | Xiaohui Xu | Shuang Li | Mao Wang | Arne Thomas | Chong Cheng
Source: Nature Synthesis (article, 2026-03-25)
Field: Materials Science — Materials Chemistry

Machine learning-accelerated discovery of covalent organic frameworks for hydrogen peroxide photosynthesis Abstract Covalent organic frameworks (COFs) are promising photocatalysts for hydrogen peroxide (H 2 O 2 ) production, yet their rational design remains challenging. Although machine learning has advanced the prediction of properties of porous materials, its application to COF-based photocatalysis faces two major challenges: the representation of multilevel structural features and the limited availability of training datasets. Here we present a comprehensive computational framework, termed ‘information co-evolution’, that accelerates the discovery of efficient COF structures for H 2 O 2 photosynthesis. This framework integrates two pathways: to mitigate data limitations, we introduce data augmentation techniques and ensemble modelling; concurrently, to address the structural encoding challenge, we introduce a cross-level feature fusion strategy that integrates these fragment descriptors with mechanism-driven physical descriptors. These strategies collectively reduced the validation root mean square error from 4.70 to 3.31. Among over 10,000 candidates, our framework can successfully identify high-performance COFs for H 2 O 2 photosynthesis, for example, COF-343 achieves a H 2 O 2 photosynthetic rate of 12,978.7 μmol h −1 g −1 . The model interpretation further unveiled critical structural motifs, offering information for the rational design of COF photocatalysts beyond traditional trial-and-error methods. Rational design ; Hydrogen peroxide ; Feature (linguistics) ; Covalent bond ; Representation (politics) ; Photosynthesis ; Sensor fusion ; Encoding (memory) Covalent Organic Framework Applications ; Advanced Photocatalysis Techniques ; TiO2 Photocatalysis and Solar Cells
Authors: P. Swetha | R. Sagayaraj
Source: Prabha Materials Science Letters (article, 2026-03-01)
Field: Materials Science — Materials Chemistry

A Comprehensive Review on the Synthesis, Characterization, and Applications of Spinel Ferrite Nanoparticles Spinel ferrite materials, particularly spinel ferrites, have gained immense popularity due to their unique magnetic properties and versatility in modern technology. With the general formula AB2O4, where A and B are metal cations, these materials can be classified as soft or hard ferrites, each with distinct applications ranging from microwave components and inductors to permanent magnets and data storage. This review focuses on the synthesis and characterization of spinel ferrite nanoparticles. We examine various synthesis methods, including co-precipitation, sol-gel, hydrothermal, and combustion techniques, highlighting their respective advantages and disadvantages in terms of cost, particle size control, and purity. The review also details the use of advanced characterization techniques. X-ray Diffraction (XRD) is crucial for confirming crystal structure, phase purity, and determining crystallite size and lattice constants, while Fourier-Transform Infrared (FTIR) spectroscopy identifies characteristic metal-oxygen bonds. Furthermore, Electron Microscopy (TEM and SEM) is used to analyse particle morphology, size, and agglomeration, and Vibrating Sample Magnetometry (VSM) is essential for evaluating key magnetic properties like saturation magnetization and coercivity. The review synthesizes findings from a wide range of studies, underscoring the critical relationship between synthesis parameters, doping, and the resulting structural and magnetic properties of these nanomaterials, which are key to their functional applications in fields like targeted drug delivery and magnetic resonance imaging. Spinel ; Ferrite (magnet) ; Crystallite ; Magnetic nanoparticles ; Ferromagnetic resonance ; Coercivity ; Magnetometer ; Magnet ; Nanoparticle Magnetic Properties and Synthesis of Ferrites ; Nanoparticle-Based Drug Delivery ; Pigment Synthesis and Properties
Authors: M. Mataev | Altynai Madiyarova | Moldir Abdraimova | Zhanar Tursyn | K. Ramachandran
Source: International Journal of Molecular Sciences (article, 2026-02-24)
Field: Materials Science — Materials Chemistry

Structure Property–Application Relationships of Spinel Ferrite Nanoparticles: From Synthesis to Functional Systems This review article provides a systematic analysis of synthesis methods, structural characteristics, and functional properties of spinel-structured ferrite nanoparticles (MFe<sub>2</sub>O<sub>4</sub>). The physicochemical principles, advantages, and limitations of various synthesis techniques-including co-precipitation, combustion, sol-gel, thermal decomposition, hydrothermal, solvothermal, microwave-assisted, sonochemical, electrochemical, and solid-state reaction methods-are comparatively discussed. The influence of synthesis parameters on crystal structure, morphology, and cation distribution between tetrahedral and octahedral sites, as well as on magnetic, dielectric, and optical properties, is critically analyzed. Furthermore, the capabilities of characterization techniques such as X-ray diffraction (XRD), scanning electron microscopy with energy-dispersive spectroscopy (SEM/EDS), Fourier-transform infrared spectroscopy (FTIR), FT-Raman spectroscopy, dielectric measurements, and magnetic measurements for investigating spinel ferrites are comprehensively summarized. Finally, the high potential of spinel ferrite nanoparticles for applications in electronics, microwave devices, water treatment, catalysis, sensors, and biomedical fields is highlighted. Spinel ; Ferrite (magnet) ; Nanoparticle ; Scanning electron microscope ; Infrared spectroscopy ; Crystal structure ; Spectroscopy ; Octahedron ; Characterization (materials science) Magnetic Properties and Synthesis of Ferrites ; Nanoparticle-Based Drug Delivery ; Electromagnetic wave absorption materials
Authors: Haocheng Tang | Xingyu Dang | Junmei Wang
Source: arXiv (Cornell University) (article, 2026-04-03)
Field: Materials Science — Materials Chemistry

Fine-tuning DeepSeek-OCR-2 for Molecular Structure Recognition Optical Chemical Structure Recognition (OCSR) is critical for converting 2D molecular diagrams from printed literature into machine-readable formats. While Vision-Language Models have shown promise in end-to-end OCR tasks, their direct application to OCSR remains challenging, and direct full-parameter supervised fine-tuning often fails. In this work, we adapt DeepSeek-OCR-2 for molecular optical recognition by formulating the task as image-conditioned SMILES generation. To overcome training instabilities, we propose a two-stage progressive supervised fine-tuning strategy: starting with parameter-efficient LoRA and transitioning to selective full-parameter fine-tuning with split learning rates. We train our model on a large-scale corpus combining synthetic renderings from PubChem and realistic patent images from USPTO-MOL to improve coverage and robustness. Our fine-tuned model, MolSeek-OCR, demonstrates competitive capabilities, achieving exact matching accuracies comparable to the best-performing image-to-sequence model. However, it remains inferior to state-of-the-art image-to-graph modelS. Furthermore, we explore reinforcement-style post-training and data-curation-based refinement, finding that they fail to improve the strict sequence-level fidelity required for exact SMILES matching. PubChem ; Task (project management) ; Matching (statistics) ; Training set ; Fidelity ; Pattern recognition (psychology) ; Key (lock) ; Optical character recognition Machine Learning in Materials Science ; Graph Theory and Algorithms ; Computational Drug Discovery Methods
Authors: Hanbum Ko | Chanhui Lee | Ye Rin Kim | Rodrigo Hormázabal | Sehui Han | Sungbin Lim | Sungwoong Kim
Source: arXiv (Cornell University) (article, 2026-03-13)
Field: Materials Science — Materials Chemistry

RetroReasoner: A Reasoning LLM for Strategic Retrosynthesis Prediction Retrosynthesis prediction is a core task in organic synthesis that aims to predict reactants for a given product molecule. Traditionally, chemists select a plausible bond disconnection and derive corresponding reactants, which is time-consuming and requires substantial expertise. While recent advancements in molecular large language models (LLMs) have made progress, many methods either predict reactants without strategic reasoning or conduct only a generic product analysis, rather than reason explicitly about bond-disconnection strategies that logically lead to the choice of specific reactants. To overcome these limitations, we propose RetroReasoner, a retrosynthetic reasoning model that leverages chemists' strategic thinking. RetroReasoner is trained using both supervised fine-tuning (SFT) and reinforcement learning (RL). For SFT, we introduce SyntheticRetro, a framework that generates structured disconnection rationales alongside reactant predictions. In the case of RL, we apply a round-trip accuracy as reward, where predicted reactants are passed through a forward synthesis model, and predictions are rewarded when the forward-predicted product matches the original input product. Experimental results show that RetroReasoner not only outperforms prior baselines but also generates a broader range of feasible reactant proposals, particularly in handling more challenging reaction instances. Retrosynthetic analysis ; Task (project management) ; Disconnection ; Product (mathematics) ; Range (aeronautics) ; Core (optical fiber) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Asymmetric Hydrogenation and Catalysis
Authors: Sreya Vangara | J.S. Sharma Nanda | Yan‐Kai Tzeng | Eric Darve
Source: ArXiv.org (article, 2026-01-14)
Field: Materials Science — Materials Chemistry

SpectraQuery: A Hybrid Retrieval-Augmented Conversational Assistant for Battery Science Scientific reasoning increasingly requires linking structured experimental data with the unstructured literature that explains it, yet most large language model (LLM) assistants cannot reason jointly across these modalities. We introduce SpectraQuery, a hybrid natural-language query framework that integrates a relational Raman spectroscopy database with a vector-indexed scientific literature corpus using a Structured and Unstructured Query Language (SUQL)-inspired design. By combining semantic parsing with retrieval-augmented generation, SpectraQuery translates open-ended questions into coordinated SQL and literature retrieval operations, producing cited answers that unify numerical evidence with mechanistic explanation. Across SQL correctness, answer groundedness, retrieval effectiveness, and expert evaluation, SpectraQuery demonstrates strong performance: approximately 80 percent of generated SQL queries are fully correct, synthesized answers reach 93-97 percent groundedness with 10-15 retrieved passages, and battery scientists rate responses highly across accuracy, relevance, grounding, and clarity (4.1-4.6/5). These results show that hybrid retrieval architectures can meaningfully support scientific workflows by bridging data and discourse for high-volume experimental datasets. SQL ; Bridging (networking) ; Workflow ; Query language ; Parsing ; Relational database Machine Learning in Materials Science ; Spectroscopy Techniques in Biomedical and Chemical Research ; Scientific Computing and Data Management