jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ioan-Bogdan Magdău | Gábor Cśanyi
Source: Nature Machine Intelligence (article, 2025-07-10)
Field: Materials Science — Materials Chemistry

A predictive framework for liquid electrolytes takes root with BAMBOO Bamboo ; Electrolyte ; Root (linguistics) ; Environmental science ; Business ; Materials science ; Chemistry ; Composite material ; Electrode Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Protein Structure and Dynamics
Authors: Francesco Tavani | Alessandro Tofoni | Marco Vandone | Matteo Busato | Luca Braglia | Piero Torelli | Maximillian G. Stanzione | A. Robert Armstrong | Russell E. Morris | Valentina Colombo | P. D’Angelo
Source: Chemical Science (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

A combined soft X-ray and theoretical investigation discloses the water harvesting behaviour of Mg-MOF-74 at the crystal surface Metal-organic frameworks (MOFs) are receiving growing interest as transformative materials for real-world atmospheric water harvesting applications. However, obtaining molecular-level details on how surface effects regulate MOF water uptake has proven to be elusive. Here, we present a novel methodology based on ambient pressure soft X-ray absorption spectroscopy (AP-NEXAFS), machine learning-assisted theoretical spectroscopy and molecular dynamics simulations to gain selective insights into the behaviour of water at a MOF crystal surface. We applied our interdisciplinary method to investigate the structural and dynamical properties of water at the surface of the Mg-MOF-74 system, while obtaining complementary information on the water uptake and release from the bulk by synchrotron powder X-ray diffraction. Our investigation pointed out the simultaneous presence of Mg open sites and residual gas-phase water during dehydration, and proved that during water release a high number of surface Mg sites still interact with one or two water molecules. Conversely, when looking at the bulk, a significantly lower number of Mg sites have been found to interact with water molecules in the same experimental conditions. This behaviour suggests that the water adsorption (desorption) process starts from the interior of the material and propagates towards the channel openings. The combined approach based on AP-NEXAFS, PXRD experimental determinations and ML-supported theoretical analyses has been found to be a valuable tool to provide a thorough description of the water harvesting process at both surface and bulk of the crystal. Crystal (programming language) ; Materials science ; Surface (topology) ; Crystallography ; Chemical physics ; Mineralogy ; Chemical engineering ; Chemistry ; Geometry ; Mathematics ; Computer science ; Engineering Magnesium Oxide Properties and Applications ; Inorganic Fluorides and Related Compounds ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Md. Azharul Islam | Dwyer Deighan | Shayan Bhattacharjee | Daniel Tantalo | Pratyush Singh | David Salač | Danial Faghihi
Source: Computational Materials Science (article, 2025-06-25)
Field: Chemistry — Spectroscopy

Stochastic deep learning surrogate models for uncertainty propagation in microstructure–properties of ceramic aerogels Microstructure ; Ceramic ; Materials science ; Surrogate model ; Composite material ; Statistical physics ; Computer science ; Machine learning ; Physics Aerogels and thermal insulation ; Radiative Heat Transfer Studies ; High voltage insulation and dielectric phenomena
Authors: Edgar Iván Sánchez Medina | Ann-Joelle Minor | Kai Sundmacher
Source: Systems and Control Transactions (article, 2025-06-30)
Field: Chemistry — Spectroscopy

Systematic comparison between Graph Neural Networks and UNIFAC-IL for solvent pre-selection in liquid-liquid extraction Solvent selection is a critical decision-making process that balances economic, environmental, and societal factors. The vast chemical space makes evaluating all potential solvents impractical, necessitating pre-selection strategies to identify promising candidates. Predictive thermodynamic models, such as the UNIFAC model, are commonly used for this purpose. Recent advancements in deep learning have led to models like the Gibbs-Helmholtz Graph Neural Network (GH-GNN), which overall offers higher accuracy in predicting infinite dilution activity coefficients over a broader chemical space than UNIFAC. This study presents a systematic comparison of solvent pre-selection using GH-GNN and UNIFAC-IL in the context of liquid-liquid extraction. The original GH-GNN model is extended to simultaneously predict organic and ionic systems. This extended GH-GNN model predicts more than 92 % of the logarithmic IDACs with an absolute error of less than 0.3. By comparison, UNIFAC-based models only achieve such accuracy for less than 65 %. A case study is used involving the ionic liquid ethyl-3-methylimidazolium tetrafluoroborate ([EMIM][BF4]) and caprolactam, relevant for the solvolytic depolymerization of polyamide 6. Results indicate a significant correlation in solvent rankings across both methods, with a Spearman�s coefficient of 0.62, suggesting that deep learning-based models like GH-GNN are viable alternatives for solvent pre-selection. Additionally, chemical similarity metrics, such as Tanimoto similarity, can assess confidence in solvent rankings, allowing users to determine acceptable risk levels in predictions across a vast chemical space. UNIFAC ; Solvent ; Selection (genetic algorithm) ; Graph ; Extraction (chemistry) ; Artificial neural network ; Computer science ; Solvent extraction ; Chromatography ; Chemistry ; Artificial intelligence ; Organic chemistry ; Theoretical computer science ; Phase equilibrium Analytical Chemistry and Chromatography ; Crystallization and Solubility Studies ; Computational Drug Discovery Methods
Authors: Jian Qu Zhu | Yongsheng Ren | Wei Zhou | Jiajia Xu | Zhao Niu | Zhan Shu | Wenhui Ma
Source: Materials Today Communications (article, 2025-02-25)
Field: Materials Science — Materials Chemistry

LLM-mambaformer: Integrating mamba and transformer for crystalline solids properties prediction Materials science ; Transformer ; Voltage ; Electrical engineering ; Engineering Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallization and Solubility Studies
Authors: Fredrik Sverdrup | Antonio Pellegrino | Vito L. Tagarielli | Burcu Tasdemir
Source: Materials Today Communications (article, 2025-04-01)
Field: Materials Science — Materials Chemistry

A machine learning approach to modelling temperature-dependent cyclic behaviour This study presents a methodology for developing a temperature-dependent cyclic plasticity surrogate model as an efficient alternative to phenomenological temperature-dependent constitutive models. The titanium alloy Ti-6Al-4V, known for its widespread use in various engineering applications, was selected for this investigation. The surrogate model, based on a feedforward neural network, was trained using random amplitude stress-strain histories at various temperatures. To generate the training dataset, constitutive models were calibrated at specific temperatures using both experimental and available literature data, enabling the simulation of virtual temperature-dependent experiments. Cyclic loading simulations were performed at random axial strains within the range [-4 %, 4 %] and temperatures of 20℃, 400℃, 500℃, and 600℃. The predictive accuracy of the surrogate model was evaluated using unseen random stress-strain histories and temperature conditions, demonstrating high accuracy and computational efficiency. • A surrogate model predicts cyclic behaviour of Ti-6Al-4V under varying temperatures. • Neural networks efficiently model temperature-dependent material behaviour. • The model accurately predicts cyclic stress-strain responses in 20°C −600°C. • The methodology offers an efficient alternative temperature dependent material model. Materials science ; Thermodynamics Machine Learning in Materials Science ; High Temperature Alloys and Creep ; Protein Structure and Dynamics
Authors: Xin Yue Wu | Ping Zhang | Mengran Bai | Hui Sun | Bin Cui
Source: Current Organic Chemistry (article, 2025-10-06)
Field: Chemistry — Inorganic Chemistry

Synthesis, Development, and Applications of Chiral Salen Ligands in Asymmetric Catalysis Chiral salen ligands, distinguished by their exceptional spatial and electronic tunability, serve as pivotal scaffolds in asymmetric catalysis, forming stable complexes with diverse metal ions through a robust [O,N,N,O] tetradentate coordination motif derived from the condensation of vicinal diamines with salicylaldehyde derivatives; this review consolidates advances over the past two decades, encompassing synthetic methodologies, structural evolution, and catalytic applications, where Mn-salen complexes facilitate enantioselective oxidations with high stereocontrol, exemplified by oxidative kinetic resolution of secondary alcohols and sulfide oxidations, Co-salen complexes exhibit unparalleled efficiency in hydrolytic kinetic resolution and CO2/epoxide copolymerization, achieving quantitative conversions and exceptional enantiomeric excesses (ee &gt;99%), and Ti-salen systems deliver high enantioselectivity in sulfide oxidations across diverse substrates due to broad electronic tolerance. Innovative extensions include enantioselective olefin epoxidation, asymmetric nitroalkene cyanation, and sustainable polycarbonate synthesis from CO2, offering significant advantages such as low toxicity, recyclability, and operational efficiency in aqueous media, while computational studies provide mechanistic insights, elucidating atomic-level behavior and the electronic origins of catalytic performance; future research priorities emphasize machine learning-guided design of programmable derivatives, development of redox-tunable electrocatalytic platforms, and lifecycle-optimized synthesis to enhance stability, selectivity, and reusability, thereby underscoring the structural versatility of salen scaffolds in advancing green chemistry, pharmaceutical synthesis, and CO2 utilization, with interdisciplinary innovation addressing current challenges to unlock full potential in sustainable catalysis and refine performance metrics for practical implementation. Enantioselective synthesis ; Kinetic resolution ; Salicylaldehyde ; Catalysis ; Enantiomer ; Sulfide ; Supramolecular chemistry Asymmetric Hydrogenation and Catalysis ; Catalysis and Oxidation Reactions ; Surface Chemistry and Catalysis
Authors: Oliver Goldstein | March, Samuel
Source: arXiv (Cornell University) (preprint, 2025-01-23)
Field: Chemistry — Physical and Theoretical Chemistry

Representing Molecules with Algebraic Data Types: Beyond SMILES and SELFIES Benchmarks of molecular machine learning models often treat the molecular representation as a neutral input format, yet the representation defines the syntax of validity, edit operations, and invariances that models implicitly learn. We propose MolADT, a typed intermediate representation (IR) for molecules expressed as a family of algebraic data types that separates (i) constitution via Dietz-style bonding systems, (ii) 3D geometry and stereochemistry, and (iii) optional electronic annotations. By shifting from string edits to operations over structured values, MolADT makes representational assumptions explicit, supports deterministic validation and localized transformations, and provides hooks for symmetry-aware and Bayesian workflows. We provide a reference implementation in Haskell (open-source, archived with DOI) and worked examples demonstrating delocalised/multicentre bonding, validation invariants, reaction extensions, and group actions relevant to geometric learning. Representation (politics) ; Algebraic number ; Computer science ; Molecule ; Algebra over a field ; Theoretical computer science ; Mathematics ; Pure mathematics ; Chemistry ; Political science ; Organic chemistry ; Mathematical analysis History and advancements in chemistry ; Computational Drug Discovery Methods
Authors: Luis Armando González-Ortiz | Lisset Noriega | Filiberto Ortíz‐Chi | Gabriela Vidales-Ayala | Emmanuel Soberanis-Cáceres | Amilcar Meneses‐Viveros | Alán Aspuru‐Guzik | Gabriel Merino
Source: Chemical Science (article, 2025-11-13)
Field: Materials Science — Materials Chemistry

Grammar-driven SMILES standardization with <i>TokenSMILES</i> The redundancy of <i>SMILES</i> notation, where multiple strings can describe the same molecule, remains a challenge in computational chemistry and cheminformatics. To mitigate this issue, we introduce <i>TokenSMILES</i>, a grammatical framework that standardizes <i>SMILES</i> into structured sentences composed of context-free words. By applying five syntactic constraints (including branch limitations, balanced parentheses, and aromaticity exclusion), <i>TokenSMILES</i> minimizes redundant <i>SMILES</i> enumerations for alkanes while maintaining valence and octet compliance through semantic parsing rules. <i>TokenSMILES</i> does not replace <i>SMILES</i> but rather formalizes its syntax into a standardized, machine-interpretable form. This grammatical structure enables controlled generation and manipulation of valid <i>SMILES</i> strings, ensuring syntactic and semantic consistency while substantially reducing redundancy. Implemented into <i>SmilX</i>, an open-source tool, <i>TokenSMILES</i> generates valid <i>SMILES</i> with accuracy comparable to existing computational implementations for molecules with low hydrogen deficiency (HDI ≤ 4). Its applicability extends beyond alkanes through stoichiometric modifications such as bond insertion, cyclization, and heteroatom substitution. Nevertheless, challenges remain for highly unsaturated systems, where canonicalization artifacts highlight the need for dynamic feasibility checks. By integrating linguistic principles with cheminformatics, <i>TokenSMILES</i> establishes a scalable framework for systematic chemical space exploration, supporting applications in drug discovery, materials design, and machine learning-driven molecular innovation. Parsing ; Redundancy (engineering) ; Syntax ; Standardization ; Scalability ; Consistency (knowledge bases) ; Semantics (computer science) ; Implementation Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Zhiqing Yang | Min Chen | Si‐Ping Gao | Rongxue Wu | Haofan Zhang | Mengya Zhang | Yang Yu | Qin Yao | Peng Li
Source: Journal of Food Composition and Analysis (article, 2025-11-22)
Field: Chemistry — Analytical Chemistry

Rapid and non-destructive origin authentication of goji berries using Raman spectroscopy and a lightweight deep learning framework Deep learning ; Traceability ; Raman spectroscopy ; Convolutional neural network ; Identification (biology) ; Authentication (law) ; Feature extraction ; Pattern recognition (psychology) Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Polysaccharides and Plant Cell Walls
Authors: Yoshihiro Yamazaki | Kota Tsujikawa | Junji Hyodo | Susumu Fujii
Source: Solid State Ionics (article, 2025-10-27)
Field: Materials Science — Materials Chemistry

Materials discovery through interpretation: An application to proton-conducting oxides Machine learning has become a pivotal tool in materials discovery, but conventional data-driven models often behave as “black boxes” limited by the scope of their training data. In this perspective, we outline the current status of machine learning-driven materials discovery and present Materials Discovery through Interpretation (MDI), a framework that integrates scientific reasoning into the ML loop to enhance the reliability and impact of materials design. Instead of relying solely on statistical correlations, the MDI iteratively incorporates domain knowledge and insights from experimental and/or computational results such as phase formability and materials functionality into the predictive process. This interpretive strategy enables meaningful extrapolation even when underlying physics and chemical correlations remain poorly understood, guiding the identification of new candidate materials while simultaneously refining the models. Applied to proton-conducting oxides, MDI successfully identified previously unrecognized compounds, including those achieving conductivity above 0.01 Scm −1 at 300 °C, a critical benchmark for fuel cell electrolytes, while providing clear rationales for their selection. By combining predictive power with interpretability, MDI offers a flexible pathway for accelerating materials innovation beyond the confines of existing data. • Proposes MDI framework integrating scientific reasoning to ML for materials design. • MDI reliably predicts material functions using domain knowledge and expert insights. • Applied to proton conductors, MDI finds stable oxides with >0.01 S cm −1 at 300 °C. Applicability domain ; Extrapolation ; Benchmark (surveying) ; Domain (mathematical analysis) ; Reliability (semiconductor) ; Identification (biology) ; Scope (computer science) ; Domain knowledge Electronic and Structural Properties of Oxides ; Machine Learning in Materials Science ; Advancements in Solid Oxide Fuel Cells
Authors: Jiayi Xu | Zisheng Luo | Xuenan Zhang | Zhenbiao Li | Dong Li | Yanpei Chen | Xingyu Lin | Fabián Guillén | Yanqun Xu
Source: Comprehensive Reviews in Food Science and Food Safety (article, 2025-12-08)
Field: Chemistry — Analytical Chemistry

Revolutionizing Fresh Food Quality Control in Supply Chains With Machine Learning: Current Advances and Future Challenges Approximately one-third of fresh food is wasted globally throughout the supply chain. Machine learning (ML), a key branch of artificial intelligence, enhances postharvest logistics and preservation of fresh food by enabling intelligent sensing, precise evaluation, and adaptive control. However, its application is challenged by data standardization, sensor limitations, product variability, and limited model generalizability. This review summarizes current advanced ML applications in the food supply chain, emphasizing their transformative potential for quality control. We explore ML's ability to integrate multi-omics data for deeper insights into molecular changes during transportation and storage, enabling the development and evaluation of management strategies. Practical applications in grading, sensor technology, and intelligent preservation materials are also evaluated. ML models, such as support vector machine (SVM) and convolutional neural network (CNN), enhance precise grading and quality prediction by analyzing sensory attributes and chemical composition. By capturing complex molecular interactions, ML enables innovative sensor surface design with enhanced sensitivity and specificity. ML-driven sensors further support real-time environmental monitoring, while intelligent packaging materials powered by ML maintain freshness and reduce spoilage through adaptive responses to internal conditions. To ensure model robustness and generalizability, appropriate validation strategies such as cross-validation and external validation are essential. Despite its substantial potential, the widespread adoption of ML still faces challenges, including limited varietal and regional generalization, decision-making transparency, computational demands, limited data availability, and algorithm selection. Addressing these issues is critical for achieving effective and sustainable ML integration in postharvest quality control systems. Supply chain ; Food spoilage ; Active packaging ; Food quality ; Robustness (evolution) ; Convolutional neural network ; Quality (philosophy) ; Supply chain management ; Phenomics Spectroscopy and Chemometric Analyses ; Food Supply Chain Traceability ; Postharvest Quality and Shelf Life Management
Authors: T. Ogawa | Makoto Tanaka | Naoki Kawashima | Taishi Ito | Kei Nakayama | Takeharu Kato | Satoshi Kitaoka
Source: Advanced Science (article, 2025-04-26)
Field: Materials Science — Materials Chemistry

Controllable Crystalline Phases of Multi‐Cation Oxides Multi-cation oxides have been extensively studied over the past decade for various solid-state applications. The source of their functionality lies in a wide compositional search space derived from countless cation combinations and diverse crystal structures formed in metal oxides. However, due to the vast space and complexity of structure control, material exploration has been limited to dispersed compositions under different synthesis conditions, hindering their systematic understanding and rational design. Here, a crystalline-phase map of multi-cation rare-earth titanates is reported, where three types of crystals, i.e., cubic and hexagonal, and orthorhombic phases, emerge depending on the composition and temperature and exhibit systematic changes. The crystal structures of each phase are thoroughly characterized with X-ray diffraction, electron microscopy, and first-principles calculations. The configurational entropies calculated from the crystallographic information support the phase-boundary shift between hexagonal and orthorhombic phases observed in the phase map. Further, a machine learning procedure is proposed for constructing the map from sparse experimental data, allowing predictive exploration for stable crystalline phases across a large compositional space. These findings may facilitate the design of multi-cation oxides with a desired structure dispersed in a large search space. Orthorhombic crystal system ; Phase (matter) ; Materials science ; Crystal structure ; Crystallography ; Crystal structure prediction ; Crystal (programming language) ; Diffraction ; Electron diffraction ; Chemical physics ; Chemistry ; Computer science ; Physics ; Optics Electronic and Structural Properties of Oxides ; Ferroelectric and Piezoelectric Materials ; Machine Learning in Materials Science
Authors: Hiroto Yokoyama | Takahiro Umemoto | Akiko Kumada | Masahiro Sato
Source: Applied Physics Letters (article, 2025-12-08)
Field: Materials Science — Materials Chemistry

Extrapolative prediction of polymer properties using physics-informed hierarchical descriptors Accurately predicting the properties of polymers is essential for data-driven materials design. However, such predictions are often challenged by the limited availability of polymer-related data and the fact that high-performance polymers of interest typically lie outside the distribution of existing datasets. In this study, we develop a machine learning model that enhances extrapolative prediction accuracy beyond the training data by leveraging hierarchical, physics-informed descriptors. Specifically, we utilize quantum mechanical (QM) descriptors derived from density functional theory calculations, molecular dynamics (MD) descriptors representing structural and dynamical properties, and force field (FF) descriptors characterizing the interaction parameters used in MD simulations. We investigated two types of extrapolation tasks: extrapolation beyond the range of physical properties and extrapolation to structurally dissimilar molecules. By systematically evaluating all non-zero combinations of QM, MD, and FF descriptors, we find that selected subsets often outperform models using the full descriptor set. This highlights the critical role of dimensionality reduction and descriptor relevance, especially under data-scarce conditions. Comparisons with structure-based models employing molecular fingerprints or molecular graphs further demonstrated the superiority of the proposed model based on selected physics-based descriptors. Extrapolation ; Curse of dimensionality ; Dimensionality reduction ; Field (mathematics) ; Range (aeronautics) ; Force field (fiction) ; Molecular dynamics ; Molecular descriptor Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Advanced Polymer Synthesis and Characterization