jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: M. Darracq-Ghitalla-Ciock | E. Ay-Albrecht | C. Charmeau-Genevois
Source: Toxicology Letters (article, 2025-09-01)
Field: Chemistry — Analytical Chemistry

P18-53 AOrTA: a reliable in silico tool to predict acute oral toxicity for regulatory purposes In the context of the 3Rs, acute oral toxicity is one of the last endpoints of REACH Annex VII which needs to be replaced by new alternative methods (NAMs). Existing in silico tools are struggling to provide models with good performance and reliable prediction in regards of QSAR Assessment Framework [1] for regulatory use. In this context, a new in silico global model named AOrTA v1.0 for Acute Oral Toxicity Alert model, was developed to predict acute toxicity estimation (ATE) for acute oral toxicity (AOT) according to the CLP classification for regulatory purpose. The dataset of the model has been constituted from AOT studies extracted from ECHA dossiers and scientific literature. Only organic monoconstituents were used to develop the model. Studies were validated individually in agreement with the OECD guidelines in order to build a model using a high-quality dataset. Dataset splitting was performed using a genetic algorithm, optimising parameters to ensure structural representativity between the training and test sets. Then, a machine learning approach was applied using structural features of the substances, and an applicability domain was defined based on structural analogues using Tanimoto similarity index. Furthermore, analogues provided to the users were identified using Tanimoto similarity index and the mechanisms of toxic action (MechoA). For a test item, the ATE classification is predicted using AOrTA as “Cat. 1 or 2”, “Cat. 3”, “Cat. 4” or “non-classified” according to CLP. Whenever a test item is predicted to be “Cat. 1 or 2”, a refinement of the prediction is performed by the model based on the closest analogues from the training set to distinguish Cat. 1 and Cat. 2 classifications for the test item. In the same way, a prediction refinement to provide a GHS category is performed for substances predicted to be non-classified according to CLP ( i.e . “GHS category 5” or “non-classified according to GHS”). Furthermore, AOrTA v1.0 is linked to local models with restrictive applicability domain for specific chemical substances ( e.g. simple ester, simple carboxylic acid, simple alcohol, thiol, …). Therefore, whenever a test item falls within a local model applicability domain, a consensus prediction of the AOT category according to CLP is provided to the user based on AOrTA global model and local models. AOrTA v1.0 was developed to assess AOT based on structural features of the substances to predict ATE with high confidence. In a near future, further local models will be developed for substances that are not well-covered by the global model in order to improve confidence and reliability of the AOT predictions. In silico ; Context (archaeology) ; Quantitative structure–activity relationship ; Similarity (geometry) ; Training set ; Acute toxicity Analytical Methods in Pharmaceuticals
Authors: Xiaoyu Song | Xinghua Liu | Lei Xu | Aochi Liu | Xiangyu Meng | Yuening Wang | Ming‐Jian Zhang | Guangcheng Xi | Jie Lin | Xiaotian Wang | Gongnan Xie
Source: Chinese Journal of Structural Chemistry (article, 2025-12-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Sub-nanoscale oxygen-defect-rich MoO3−: A versatile platform for label-free ultrasensitive SERS biodetection Substrate (aquarium) ; Raman scattering ; Polystyrene ; Rhodamine ; Pollutant ; Rhodamine B ; Quantum dot Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Nanoplatforms for cancer theranostics
Authors: Mohammed Lemaalem | Selva Chandrasekaran Selvaraj | Ilias Papailias | Naveen K. Dandu | Arash Namaeighasemi | Larry A. Curtiss | Amin Salehi‐Khojin | Anh T. Ngo
Source: ArXiv.org (preprint, 2025-01-19)
Field: Materials Science — Materials Chemistry

Interactive Multiscale Modeling to Bridge Atomic Properties and Electrochemical Performance in Li-CO$_2$ Battery Design Li-CO$_2$ batteries are promising energy storage systems due to their high theoretical energy density and CO$_2$ fixation capability, relying on reversible Li$_2$CO$_3$/C formation during discharge/charge cycles. We present a multiscale modeling framework integrating Density Functional Theory (DFT), Ab-Initio Molecular Dynamics (AIMD), classical Molecular Dynamics (MD), and Finite Element Analysis (FEA) to investigate atomic and cell-level properties. The considered Li-CO$_2$ battery consists of a lithium metal anode, an ionic liquid electrolyte, and a carbon cloth cathode with Sb$_{0.67}$Bi$_{1.33}$Te$_3$ catalyst. DFT and AIMD determined the electrical conductivities of Sb$_{0.67}$Bi$_{1.33}$Te$_3$ and Li$_2$CO$_3$ using the Kubo-Greenwood formalism and studied the CO$_2$ reduction mechanism on the cathode catalyst. MD simulations calculated the CO$_2$ diffusion coefficient, Li$^+$ transference number, ionic conductivity, and Li$^+$ solvation structure. The FEA model, parameterized with atomistic simulations data, reproduced the available experimental voltage-capacity profile at 1 mA/cm$^2$ and revealed spatio-temporal variations in Li$_2$CO$_3$/C deposition, porosity, and CO$_2$ concentration dependence on discharge rates in the cathode. Accordingly, Li$_2$CO$_3$ can form large and thin film deposits, leading to dispersed and local porosity changes at 0.1 mA/cm$^2$ and 1 mA/cm$^2$, respectively. The capacity decreases exponentially from 81,570 mAh/g at 0.1 mA/cm$^2$ to 6,200 mAh/g at 1 mA/cm$^2$, due to pore clogging from excessive discharge product deposition that limits CO$_2$ transport to the cathode interior. Therefore, the performance of Li-CO$_2$ batteries can be improved by enhancing CO$_2$ transport, regulating Li$_2$CO$_3$ deposition, and optimizing cathode architecture. Bridge (graph theory) ; Electrochemistry ; Battery (electricity) ; Materials science ; Multiscale modeling ; Computer science ; Chemistry ; Electrode ; Physics ; Computational chemistry ; Thermodynamics ; Physical chemistry Machine Learning in Materials Science ; Advancements in Battery Materials
Authors: Xuyang Xu | Mengyang Zhang | Jincheng Wu | Ziyan Shen | Yang Liu | Longlu Wang
Source: Nanomaterials (review, 2025-08-13)
Field: Materials Science — Materials Chemistry

Advanced Progress of Non-Stoichiometric Transition Metal Sulfides for Sensing, Catalysis, and Energy Storage Beyond the extensively studied two-dimensional transition metal dichalcogenides, a wide range of non-stoichiometric transition metal sulfides, such as molybdenum sulfides and tungsten sulfides (Mo<sub>2</sub>S<sub>3</sub>, W<sub>2</sub>S<sub>3</sub>, Mo<sub>6</sub>S<sub>8</sub>, Mo<sub>6</sub>S<sub>6</sub>, NiMo<sub>3</sub>S<sub>4</sub>), have attracted significant attention for their promising applications in sensing, catalysis, and energy storage. It is necessary to review the current advanced progress of non-stoichiometric transition metal sulfides for various applications. Here, we systematically summarize the synthesis strategies of the non-stoichiometric transition metal sulfides, encompassing methods such as the molten salt synthesis method, high-metal-content growth strategy, and others. Particular emphasis is placed on how variations in the metal-to-sulfur ratio give rise to distinct crystal structures and electronic properties, and how these features influence their conductivity, stability, and performance. This review will deepen the understanding of the state of the art of non-stoichiometric transition metal sulfides, including the synthesis, characterization, modification, and various applications. Stoichiometry ; Transition metal ; Materials science ; Catalysis ; Characterization (materials science) ; Nanotechnology ; Metal ; Sulfur ; Tungsten Compounds ; Tungsten ; Inorganic chemistry ; Chemistry ; Physical chemistry ; Metallurgy ; Organic chemistry 2D Materials and Applications ; Gas Sensing Nanomaterials and Sensors ; Quantum Dots Synthesis And Properties
Authors: Bolong Huang | Qiyuan He | Ye Chen‐Izu | Chaoliang Tan | Qiyao Huang
Source: Chemical Research in Chinese Universities (article, 2025-05-17)
Field: Materials Science — Biomaterials

Editorial of Special Column on Application of New Material in Energy and Biology Column (typography) ; Nanotechnology ; Polymer science ; Materials science ; Engineering physics ; Engineering ; Mechanical engineering Supramolecular Self-Assembly in Materials
Authors: Yue Yin | Hai Xiao
Source: Chemical Science (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Oxidation states in solids from data-driven paradigms The oxidation state (OS) is an essential chemical concept that embodies chemical intuition but cannot be computed with well-defined physical laws. We establish a data-driven paradigm, with its implementation as Tsinghua Oxidation States in Solids (TOSS), to explicitly compute OSs in crystal structures as the emergent properties from large-sized datasets based on Bayesian maximum <i>a posteriori</i> probability (MAP). TOSS employs two looping structures over the large-sized dataset of crystal structures to obtain an emergent library of distance distributions as the foundation for chemically intuitive understanding and then determine the OSs by minimizing a loss function for each structure based on MAP and distance distributions in the whole dataset. We apply TOSS to a dataset of over one million crystal structures, achieving a superior success rate, and use the resulting OS dataset to train a graph convolutional network (GCN) model as an alternative. Both TOSS and the GCN model are benchmarked against a curated ICSD dataset of structures with human-assigned OSs, yielding high accuracies of 96.09% and 97.24%, respectively. We expect TOSS and the ML-model-based alternative to find a wide spectrum of applications, and this work also demonstrates an encouraging example for data-driven paradigms to explicitly compute the chemical intuition for tackling complex problems in chemistry. Intuition ; Bayesian network ; Bayesian probability ; Graph ; Crystal (programming language) ; Prioritization ; Crystal structure prediction ; Chemical space Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Xavier Bantreil | Olivia Giani | Laure Monconduit | Nicolas Pétry | Julien Pinaud | Béatrice Roy | Frédéric Lamaty
Source: RSC Mechanochemistry (article, 2025-12-18)
Field: Chemistry — Physical and Theoretical Chemistry

Highlights from the Mech'cheM 2025 conference: New forces in Mechanochemistry, Montpellier, France, June 4-6, 2025 Salient features from the Mech'cheM 2025 conference on mechanochemistry are highlighted. Action (physics) ; Mechanochemistry ; Identification (biology) ; Population ; Work (physics) Crystallography and molecular interactions ; Force Microscopy Techniques and Applications ; Inorganic Fluorides and Related Compounds
Authors: Md Tohidul Islam | Carmen M. Ruiz | Claudia Loyola | Joaquín Peralta | Scott Broderick
Source: Solids (article, 2025-06-25)
Field: Materials Science — Materials Chemistry

Enhanced Assessment of Transition Metal Copper Sulfides via Classification of Density of States Spectra Understanding how crystal structure influences electronic properties is crucial for discovering new functional materials. In this study, we utilized Kernel Principal Component Analysis (KPCA) to classify and analyze the Density of States (DOS) of transition metal sulfide (TMS) compounds, particularly copper-based sulfides. By mapping high-dimensional DOS data into a lower-dimensional space, we identify clusters corresponding to different crystal systems and detect outliers with significant deviations from their expected groups. These outliers exhibit unusual electronic configurations, suggesting potential applications in semiconductors, thermoelectric devices, and optoelectronic devices. Projected Density of States (PDOS) analysis further reveals how orbital hybridization governs the electronic structure of these materials, highlighting key differences between structurally similar compounds. Additionally, we analyze phase stability through inter-cluster distance measurements, identifying potential phase transformations between closely related structures. The implications for this work in terms of modifying chemistries and generalized DOS predictions are discussed. Copper ; Transition metal ; Spectral line ; Materials science ; Chemistry ; Metallurgy ; Physics ; Organic chemistry ; Catalysis Electronic and Structural Properties of Oxides ; Machine Learning in Materials Science ; Surface and Thin Film Phenomena
Authors: Tristan Zaborniak | Noora Azadvari | Qiyao Zhu | SM Bargeen Alam Turzo | Parisa Hosseinzadeh | P. Douglas Renfrew | Vikram Khipple Mulligan
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-07-10)
Field: Chemistry — Organic Chemistry

The open-source Masala software suite: Facilitating rapid methods development for synthetic heteropolymer design Although canonical protein design has benefited from machine learning methods trained on databases of protein sequences and structures, synthetic heteropolymer design still relies heavily on physics-based methods. The Rosetta software, which provides diverse physics-based methods for designing sequences, exploring conformations, docking molecules, and performing analysis, has proven invaluable to this field. Nevertheless, Rosetta's aging architecture, monolithic structure, non-open source code, and steep development learning curve are beginning to hinder new methods development. Here, we introduce the Masala software suite, a free, open-source set of C++ libraries intended to extend Rosetta and other software, and ultimately to be a successor to Rosetta. Masala is structured for modern computing hardware, and its build system automates the creation of application programming interface (API) layers, permitting Masala's use as an extension library for existing software, including Rosetta. Masala features modular architecture in which it is easy for novice developers to add new plugin modules, which can be independently compiled and loaded at runtime, extending functionality of software linking Masala without source code alteration. Here, we describe implementation of Masala modules that accelerate protein and synthetic peptide design. We describe the implementation of Masala real-valued local optimizers and cost function network optimizers that can be used as drop-in replacements for Rosetta's minimizer and packer when designing heteropolymers. We explore design-centric guidance terms for promoting desirable features, such as hydrogen bond networks, or discouraging undesirable features, such as unsatisfied buried hydrogen bond donors and acceptors, which we have re-implemented far more efficiently in Masala, providing up to two orders of magnitude of speedup in benchmarks. Finally, we discuss development goals for future versions of Masala. Suite ; Software ; Open source software ; Computer science ; Open source ; Software suite ; Software engineering ; Systems engineering ; Operating system ; Engineering ; Geography Advanced Polymer Synthesis and Characterization ; Model-Driven Software Engineering Techniques ; Advanced Theoretical and Applied Studies in Material Sciences and Geometry
Authors: Evangelos Topakas | Keith L. Hohn
Source:  (article, 2025-12-18)
Field: Materials Science — Materials Chemistry

Abstracts of the 3rd International Electronic Conference on Catalysis Sciences of 64Ammonia synthesis is crucial for global fertilizer production, traditionally relying on iron-based catalysts in the Haber-Bosch process [1,2].However, the limitations of iron catalysts-such as low reaction rates and susceptibility to deactivation-have driven interest in alternative catalysts, particularly Ru-, Co-and high-entropy-based transition metal systems.The use of promoters, both in single and double settings, has become key to enhancing the efficiency and stability of these transition metal catalysts [3].This presentation focuses on the role of various promoters in optimizing Ru-, Co-and Fe-based catalysts, exploring their mechanisms and potential to advance ammonia synthesis.Results: The inclusion of promoters such as K 2 O, Ba, Ce, or BaO, among others, significantly enhances the catalytic performance of Ru-, Co-and Fe-based catalysts.For iron, these promoters improve the adsorption of N 2 and its dissociation, leading to higher ammonia yields and longer catalyst lifespans.In cobalt-based systems, these promoters stabilize the active metal sites and promote efficient nitrogen dissociation within a temperature range of below 500 C. The promoters modify the electronic properties of the catalysts, improving their overall efficiency.The nature of the rate-determining step cannot always be limited to N 2 decomposition; it can also involve other hydrogenation steps.Conclusion: Promoters are crucial in optimizing the performance of transition metal catalysts in ammonia synthesis.These promoters enhance catalyst stability, increase reaction rates, and suppress deactivation, offering a promising pathway for more efficient and sustainable ammonia production.Future research should focus on refining promoter-metal interactions to further improve catalyst performance and reduce energy consumption. Catalysis ; Electronic systems ; Electronic materials Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Academic Writing and Publishing
Authors: Vladimir S. Baturin | Jean‐Claude Crivello
Source: ACS Applied Energy Materials (article, 2025-02-06)
Field: Materials Science — Materials Chemistry

Layered Calcium Borohydride Polymorph via an Enhanced Evolutionary Algorithm Complex hydrides are attracting increasing attention as promising solid hydrogen storage materials, yet theoretically exploring their chemical space is a challenging task due to their often intricate structures. In this work, we present an improved evolutionary crystal structure prediction method tailored to systems with rigid building blocks, enabling a reduction in search space dimensionality without losing relevant solutions and significantly speeding up the search. The developed method was validated by successfully reproducing the known structures of borohydrides and alanates of lithium, sodium, and potassium while also revising their vibrational and energetic properties. Furthermore, this approach, together with in-depth phonon analysis, led to the prediction of a new metastable P3̅cm phase of Ca(BH4)2, only 1.5 meV/atom above the known ground state. This phase exhibits a layered geometry unusual for complex hydrides, combining covalent, ionic, and van der Waals bonding with the potential for both chemical and physical hydrogen storage via intercalation. Borohydride ; Calcium ; Algorithm ; Materials science ; Computer science ; Chemistry ; Metallurgy ; Organic chemistry ; Catalysis Hydrogen Storage and Materials ; Nuclear Materials and Properties ; Chemical Synthesis and Characterization
Authors: Koushik Mahata | Sudipta Paul | Ankit Kumar Kaushik | Sanjib Banerjee
Source: Innovation of Chemistry & Materials for Sustainability (article, 2025-12-18)
Field: Materials Science — Polymers and Plastics

Smart and Functional Polymers for Sustainable Applications in Health, Energy and Environment The development of sustainable smart polymers has enabled materials capable of responding to diverse chemical, biological, and physical stimuli. This review highlights recent advances in controlled polymerization techniques-including ATRP, RAFT, RDRP, and photoinduced methods with emphasis on environmentally benign synthesis via green solvents and solvent-free routes. The integration of bio-based monomers, green catalysts, and circular design principles facilitates recyclability, biodegradability, and low toxic impact. Owing to their multifunctional behavior, these polymers exhibit significant potential in drug delivery, tissue engineering, flexible electronics, energy storage, energy conversion, and environmental remediation. Representative examples include thermo-responsive hydrogels, conductive polymer networks, self-healing elastomers, and adaptive separation membranes. Key challenges such as operational stability, scalability, and lifecycle management are discussed, underscoring the need for molecular-to-system level strategies. A holistic approach combining molecular design and circular economy frameworks will accelerate the translation of smart polymers into sustainable, high-performance technologies. Smart polymer ; Smart material ; Polymer ; Sustainable energy ; Sustainable design ; Circular economy ; Key (lock) ; Sustainable development Polymer composites and self-healing ; biodegradable polymer synthesis and properties ; Chemistry and Chemical Engineering
Authors: Pramod Aradwad | Arman Arefi | Sharvari Raut | Ahmed Abdelfattah | Mulugeta Admasu Delele | Cornelia Rauh | Barbara Sturm
Source: Food Control (article, 2025-08-20)
Field: Chemistry — Analytical Chemistry

Compositional dynamics, volatome profiling, and hyperspectral-based predictive modeling for quality monitoring of brewer's spent grain during storage Brewer’s spent grain (BSG) is a nutritionally rich by-product of brewing. However, its short shelf life, driven by environmental conditions, limits its applications beyond animal feed. This study investigates the effect of storage conditions (4, 20, and 35 °C) on BSG quality using non-invasive hyperspectral imaging (HSI) and invasive analytical techniques. Significant degradation of sugars (78.2–79.0%), fats (21.4–33.3%), starch (14–19%), protein (9–15%), and hemicellulose (20.3–34.4%) occurred during storage at 20 and 35 °C. In contrast, phenolic content increased by 30–130%, accompanied by a 1.7–7.8-fold increase in antioxidant activity and a pH shift from 6.4 to 8.8. A total of 65 volatile compounds exhibited temperature and time-dependent changes. Key off-flavor compounds such as 1-octen-3-ol, formic acid, octanic acid, butanoic acid, 2-butanone, and amide were associated with rancid, sour, cheesy, earthy, and bitter aromas in BSG stored at 20 °C and 35 °C. Significant changes in color, pH, stickiness, and odor were evident after 24 h at 20°C and 12 h at 35°C. KEGG pathway analysis revealed that pyruvate metabolism, cutin, suberine, and wax biosynthesis, alpha-linolenic acid metabolism, and tyrosine metabolism were the dominant metabolic pathways. HSI combined with machine learning accurately predicted key quality parameters, including total phenols (R 2 p = 0.9908, RMSEp = 0.1492), total color change (R 2 p = 0.9680, RMSEp = 0.8285), sugar (R 2 p = 0.9917, RMSEp = 0.6038), protein (R 2 p = 0.9756, RMSEp = 1.9467), pH (R 2 p = 0.9037, RMSEp = 0.2605), and fat (R 2 p = 0.9866, RMSEp = 0.8618). Specific wavelengths associated with chemical and physical changes were identified, and spatial distribution maps provided real-time visualization of spoilage progression. These findings highlight the potential of HSI as a rapid, non-invasive, and cost-effective technique for real-time quality monitoring and shelf life management of BSG, supporting its broader valorization. • BSG quality degrades rapidly at 20 °C (24 h) and 35 °C (12 h) storage. • Flavor and volatile profiles shift with temperature and duration. • First-order kinetics described most degradation trends (R 2 = 0.85–0.99). • Key KEGG metabolic pathways such as pyruvate metabolism, cutin, suberine and wax biosynthesis, alpha-linolenic acid metabolism . • HSI Predictions for protein, phenol, fat, sugar, pH and color were accurate and reliable. • HSI combined with ML enables effective BSG quality monitoring during storage. Hyperspectral imaging ; Profiling (computer programming) ; Computer science ; Environmental science ; Artificial intelligence Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Advanced Chemical Sensor Technologies