jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mehrpouya Javdani Gandomani | Taghi Javdani Gandomani | Maedeh Dashti
Source: Computational Materials Science (article, 2025-07-17)
Field: Materials Science — Materials Chemistry

Ai-driven superconductor prediction: An attention-based deep learning approach Superconductivity ; Deep learning ; Artificial intelligence ; Materials science ; Computer science ; Condensed matter physics ; Physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Nuclear Physics and Applications
Authors: An Niza El Aisnada | Kajjana Boonpalit | Robin van der Kruit | Koen M. Draijer | Jon López-Zorrilla | Masahiro Miyauchi | Akira Yamaguchi | Nongnuch Artrith | Sub Inorganic Chemistry and Catalysis | Materials Chemistry and Catalysis
Source: Utrecht University Repository (Utrecht University) (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Cost-Effective Strategy of Enhancing Machine Learning Potentials by Transfer Learning from a Multicomponent Data Set on ænet-PyTorch Machine learning potentials (MLPs) offer efficient and accurate material simulations, but constructing the reference ab initio database remains a significant challenge, particularly for catalyst-adsorbate systems. Training an MLP with a small data set can lead to overfitting, thus limiting its practical applications. This study explores the feasibility of developing computationally cost-effective and accurate MLPs for catalyst-adsorbate systems with a limited number of ab initio references by leveraging a transfer learning strategy from subsets of a comprehensive public database. Using the Open Catalyst Project 2020 (OC20)─a data set closely related to our system of interest─we pretrained MLP models on OC20 subsets using the ænet-PyTorch framework. We compared several strategies for database subset selection. Our findings indicate that MLPs constructed via transfer learning exhibit better generalizability than those constructed from scratch, as demonstrated by the consistency in the dynamics simulations. Remarkably, transfer learning enhances the stability and accuracy of MLPs for the CuAu/H2O system with approximately 600 reference data points. This approach achieved excellent extrapolation performance in molecular dynamics simulations for the larger CuAu/6H2O system, maintaining stable and accurate predictions for up to 250 ps, whereas MLPs without transfer learning become unstable before reaching 50 ps. We also examine the potential limitations of this strategy. This work proposes an alternative, cost-effective approach for constructing MLPs for the challenging simulation of catalytic systems. Finally, we anticipate that this methodology will pave the way for broader applications in materials science and catalysis research, facilitating more efficient and accurate simulations across various systems. Generalizability theory ; Transfer of learning ; Extrapolation ; Stability (learning theory) ; Set (abstract data type) ; Consistency (knowledge bases) ; Limiting Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; CO2 Reduction Techniques and Catalysts
Authors: Xiaochen Du (7400399) | Mengren Liu (21752816) | Jiayu Peng (4114330) | Hoje Chun (5277392) | Alexander Hoffman (5570567) | Bilge Yildiz (1276563) | Lin Li (28817) | Martin Z. Bazant (1284582) | Rafael Gómez-Bombarelli (1933810)
Source: Figshare (article, 2025-07-21)
Field: Materials Science — Materials Chemistry

Acceleratingand Enhancing ThermodynamicSimulations of Electrochemical Interfaces Electrochemical interfaces are crucial in catalysis, energy storage, and corrosion, where their stability and reactivity depend on complex interactions between the electrode, adsorbates, and electrolyte. Predicting stable surface structures remains challenging, as traditional surface Pourbaix diagrams tend to either rely on expert knowledge or costly <i>ab initio</i> sampling and neglect thermodynamic equilibration with the environment. Machine learning (ML) potentials can accelerate static modeling but often overlook dynamic surface transformations. Here, we extend the Virtual Surface Site Relaxation-Monte Carlo (VSSR-MC) method to autonomously sample surface reconstructions modeled under aqueous electrochemical conditions. Through fine-tuning foundational ML force fields, we accurately and efficiently predict surface energetics, recovering known Pt(111) phases and revealing new LaMnO<sub>3</sub>(001) surface reconstructions. By explicitly accounting for bulk-electrolyte equilibria, our framework enhances electrochemical stability predictions, offering a scalable approach to understanding and designing materials for electrochemical applications. Pourbaix diagram ; Surface (topology) ; Electrochemistry ; Stability (learning theory) ; Scalability ; Sampling (signal processing) ; Chemical stability Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Ammonia Synthesis and Nitrogen Reduction
Authors: Ákos Pomázi | Gergely Magyar | Andrea Toldy
Source: Polymer Degradation and Stability (article, 2025-12-15)
Field: Materials Science — Polymers and Plastics

Methods for predicting the fire behaviour of fibre reinforced thermoset composites • The fibre content of the composites is always in the top four crucial input parameters concerning the flammability. • Prediction of the fire performance is more accurate using algorithms based on decision trees than on artificial neural networks due to the small amount of data. • Extreme Gradient Boosting was the most successful for predicting peak heat release rate, while Random Forest Classifier performed the best at predicting time to ignition. • Artificial Neural Network-based model produced reasonable statistical metrics, but decision tree-based models can handle smaller, nonlinear datasets better and are more efficient to avoid overfitting. Destructive tests are typically used to evaluate the fire performance of polymers and their composites, implying high material costs and long testing times. Developing numerical models to predict flammability requires advanced mathematical expertise, IT resources, and realistic input parameters. In this study, we aimed to predict the key flammability parameters based on the chemical structure of the resin matrices and fibre content of composites, providing a potential alternative to costly experimental methods. We employed Random Forest Classifier (RFC), XGBoost algorithms, and an artificial neural network (ANN) model to predict key combustion parameters: peak heat release rate (pHRR), time to ignition (TTI), total heat release (THR) and the char residue (CR) solely based on chemical structure of the epoxy matrix and fibre content of the composite. After making the predictions, we assessed the performance of the models using consistent statistical indicators (mean absolute error (MAE), mean square error (MSE), and the determination parameter (R 2 )). Artificial neural network ; Flammability ; Combustion ; Fire performance ; Thermosetting polymer ; Char ; Nonlinear system ; Random forest ; Ignition system ; Nonlinear programming Flame retardant materials and properties ; Fire dynamics and safety research ; Fire effects on concrete materials
Authors: pinku nath | Y. Ôno | Yu Harabuchi | Yasunori Yamamoto | Satoshi Maeda | Tetsuya Taketsugu | Masaharu Yoshioka
Source: ChemRxiv (article, 2025-08-31)
Field: Chemistry — Spectroscopy

CHEMONTO: A reusable explicit chemical ontology-based method to expedite reaction path searches CHEMONTO is a new computational framework developed to extract and apply chemical knowledge from reaction intermediates generated during automated reaction path searches. By integrating chemical and geometric knowledge generated using chemical ontology and topology, CHEMONTO identifies chemically relevant reaction paths and geometries to guide the reaction path search. Combined with an automated reaction path search method, Artificial Force Induced Reaction (AFIR) and applied to the Heck reaction, the CHEMONTO-AFIR approach efficiently identified all key intermediates and elementary steps, including major and side products, even in high-energy regions where conventional AFIR need much larger computational efforts for extensive conformational sampling. This knowledge-driven approach significantly reduces computational costs by eliminating chemically irrelevant paths and structures. CHEMONTO relies on three main inputs: the reaction setup, chemically informed assumptions encoded as Elementary Reaction Process Ontologies (ERPOs), and a set of reaction rules. Together, these enable efficient use of extracted knowledge to accelerate reaction exploration. Unlike machine learning models, it requires no training on datasets and is broadly applicable to a wide range of organometallic systems, offering a robust tool for mechanistic analysis and rational catalyst design, especially in systems where human insight remains indispensable. Path (computing) ; Ontology ; Computer science ; Information retrieval ; Programming language ; Epistemology ; Philosophy Analytical Chemistry and Chromatography ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Thomas Gasevic | Marcel Müller | Jonathan Schöps | Stephanie Lanius | Jan Hermann | Stefan Grimme | Andreas Hansen
Source: ChemRxiv (preprint, 2025-06-26)
Field: Chemistry — Organic Chemistry

Chemical Space Exploration with Artificial ”Mindless” Molecules We introduce MindlessGen, a Python-based generator for creating chemically diverse, “mindless” molecules through random atomic placement and subsequent geometry optimization. Using this framework, we constructed the MB2061 benchmark set, containing 2061 molecules with high-level PNO-LCCSD(T)-F12 reference data for decomposition reactions mediated by H₂. This set provides a challenging benchmark for testing, validation, and training of density functional approximations (DFAs), semiempirical methods, force fields, and machine learning potentials using molecular structures beyond the conventional chemical space. For DFAs, we initially hypothesized that highly parameterized functionals might perform poorly on this set. However, no consistent relationship between fitting strategy and accuracy was observed. A clear Jacob’s ladder trend emerges, with ωB97X-2 achieving the lowest mean absolute error (MAE) of 8.4 kcal·mol⁻¹ and r²SCAN-3c offering a robust cost-efficient alternative (19.6 kcal·mol⁻¹). Furthermore, we discuss the performance of selected semiempirical methods and contemporary machine learned interatomic potentials. Space (punctuation) ; Chemical space ; Astrobiology ; Psychology ; Environmental science ; Cognitive science ; Computer science ; Biology ; Bioinformatics Supramolecular Chemistry and Complexes ; Supramolecular Self-Assembly in Materials
Authors: Pinku Nath | Y. Ono | Yu Harabuchi | Yasunori Yamamoto | Satoshi Maeda | Tetsuya Taketsugu | Masaharu Yoshioka
Source: ACS Catalysis (article, 2025-12-21)
Field: Materials Science — Materials Chemistry

ChemOntology: A Reusable Explicit Chemical Ontology-Based Method to Expedite Reaction Path Searches ChemOntology is a computational framework developed to extract and apply chemical knowledge from reaction intermediates generated during automated reaction path searches. By integrating chemical and geometric knowledge generated using chemical ontology and topology, ChemOntology identifies chemically relevant reaction paths and geometries to guide the reaction path search. Combined with an automated reaction path search method, Artificial Force Induced Reaction (AFIR) and applied to the Heck reaction, the ChemOntology-AFIR approach efficiently identified all key intermediates and elementary steps, including major and side products, even in high-energy regions where conventional AFIR need much larger computational efforts for extensive conformational sampling. This knowledge-driven approach significantly reduces computational costs by eliminating chemically irrelevant paths and structures. ChemOntology relies on three main inputs: the reaction setup, chemically informed assumptions encoded as Elementary Reaction Process Ontologies (ERPOs), and a set of reaction rules. Together, these enable efficient use of extracted knowledge to accelerate reaction exploration. Unlike machine learning models, it requires no training on data sets and is broadly applicable to a wide range of organometallic systems, offering a robust tool for mechanistic analysis and rational catalyst design, especially in systems where human insight remains indispensable. Path (computing) ; Set (abstract data type) ; Process (computing) ; Ontology ; Chemical reaction ; Key (lock) ; Range (aeronautics) ; Chemical process Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Asymmetric Hydrogenation and Catalysis
Authors: Marco Bortoli | Sigbjørn Løland Bore | Odile Eisenstein | Michele Cascella
Source: Journal of Catalysis (article, 2025-12-08)
Field: Materials Science — Materials Chemistry

The influence of the organic residue and the solvent in the Schlenk equilibrium for Grignard reagents in THF. A molecular dynamics study with machine learning potentials The Schlenk equilibrium is an essential characteristic of the Grignard reagents. However, its quantitative aspects remain poorly understood. In this work, we perform molecular dynamics simulations to characterise how the Schlenk reaction is affected by varying the alkyl R groups (Me, Et, i -Pr, t -Bu). For each R group, the reactivity profiles were obtained through enhanced sampling on fully solvated dinuclear species, leveraging a newly developed machine learning potential trained on ab initio data. While the topologies of the Helmholtz energy maps are qualitatively similar, the energy ranges vary significantly with the R groups. Bulkier R groups disfavour higher solvation of the magnesium centre, with direct implications for the activation and mechanism of the Schlenk exchange. With respect to dichloro-bridged species, the concentrations of monochloro-bridged and separated species increase with the size of R and the degree of solvation. The energy barrier for ligand exchange increases with the size of R, and it is particularly noticeable for R = t -Bu, in agreement with experimental data. As excess solvation of one of the magnesium centres is associated with the most important reaction pathways, it can be deduced that early formation of monochloro-bridged species favours the Cl/R exchange, especially for large R groups. The formation of these species is favoured by higher Mg solvation for small R groups, and by the steric volume of R itself for large R, pointing at a constructive effect of the solvent and R promoting Cl/R exchange. Our study shows that a full characterisation of the speciation of Grignard reagents in solution is possible at reasonable computational costs with machine-learning potentials. • Machine learning allows a fast exploration of the Grignard reagents diversity. • Solvation and sterics influence the structural features of these reagents. • Mono-Cl-bridged dimagnesium species play a role in the Cl/R exchange. Reagent ; Residue (chemistry) ; Catalysis ; Solvent ; Organic solvent ; Molecular dynamics Machine Learning in Materials Science ; Coordination Chemistry and Organometallics ; Inorganic Chemistry and Materials
Authors: Shiho Sakuma | Shunsaku Yasumura | Kenichiro Saita | Tetsuya Taketsugu | Masaru Ogura
Source: ChemRxiv (preprint, 2025-04-01)
Field: Materials Science — Materials Chemistry

Predicting Catalytic Activity for CH4 Combustion on Pd-exchanged Zeolite Catalysts Using Automated Reaction Route Mapping While computational predictions of catalytic activity are highly desired, conventional methods have difficulty capturing the complexity of reactions on solid catalyst surfaces. To address this issue, we employed a novel approach combining neural network potentials (NNPs) with automated reaction route mapping to explore reaction mechanisms of CH4 combustion on Pd-exchanged zeolites (Pd-CHA, Pd-beta, and Pd-MOR). The predicted reaction map of CH4 combustion over Pd2+ site revealed partially oxidized species such as CH2O, HCOOH, and bicarbonate as the potential intermediates toward CO2 + 2H2O. Activation energies (Ea) of the rate-determining step (RDS) were evaluated, revealing the order of Ea as Pd-MOR < Pd-beta < Pd-CHA. A kinetic analysis using rate constant matrix contraction (RCMC) method estimated the catalytic activities of these catalysts. No reaction intermediates with significant lifetimes were observed on Pd-beta and Pd-MOR, whereas stable bicarbonate intermediates were present on Pd-CHA, decreasing the formation rate of CO2 + 2H2O. Kinetic analysis further predicted the pseudo CO2 formation rate with activity order Pd-MOR > Pd- beta > Pd-CHA, aligning with experimental results. These findings demonstrate the potential of the automated reaction route mapping with NNP for predicting the catalytic activity of solid catalysts, enabling their efficient pre-screening and rational design. Catalysis ; Zeolite ; Combustion ; Catalytic combustion ; Chemical engineering ; Chemistry ; Computer science ; Process engineering ; Physical chemistry ; Organic chemistry ; Engineering Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Zeolite Catalysis and Synthesis
Authors: Shitao Sun (7846061) | Ziyi Liu (1540222) | Junqing Li (314038) | Wenhao Meng (11875670) | Huan Yang (36076) | Miaobei Zhang (22420701) | Hanyang Sun (14265275) | An-Hui Lu (1425130) | Dongqi Wang (1483384)
Source: Figshare (article, 2025-10-13)
Field: Materials Science — Materials Chemistry

Alumina-SupportedCatalyst Development for PropaneDehydrogenation via Interpretable Machine Learning and ExperimentalValidation The direct propane dehydrogenation (PDH) reaction constitutes one of the key routes for the production of propylene and relies on the development of high-performance catalysts, which is generally achieved following a time-consuming trial-and-error strategy. In this study, a workflow of machine learning running five stages, i.e., data preparation and the development of a reliable machine learning model and its evaluation, interpretation, and application, was established to explore the data-driven research paradigm in the screening and design of catalysts for PDH with propylene yield as the target. Data from the literature on the PDH reaction catalyzed by alumina-supported catalysts were compiled. Twelve algorithms were evaluated, and the CatBoost model exhibits a high accuracy and generalization capability, with a coefficient of determination (<i>R</i><sup>2</sup>) value of 0.992 for the training set and 0.973 for the test set. By employing this model, we screened two highly promising ternary catalysts. Experimental validation demonstrates that the predicted values for these two catalysts are in close agreement with the measured instantaneous propylene yields. Among the screened catalysts, PtSnZr/γ-Al<sub>2</sub>O<sub>3</sub> exhibits a high propylene yield and maintains over 50% yield for 13.5 h. The instantaneous propylene yields on these catalysts are predicted to be further improved upon H<sub>2</sub>S pretreatment conditions. Explainable machine learning tools (Shapley additive explanations and partial dependence plot analysis) were employed to interpret the model. This study offers valuable insights into the application of machine learning in the heterogeneously catalyzed conversion of light alkanes and aids in the development of catalysts by uncovering a hidden structure–activity relationship in literature data. Dehydrogenation ; Yield (engineering) ; Catalysis ; Propane ; Ternary operation ; Workflow ; Generalization Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Asymmetric Hydrogenation and Catalysis
Authors: Ouail Zakary (17008302) | Perttu Lantto (1649755)
Source: Figshare (article, 2025-11-12)
Field: Chemistry — Organic Chemistry

EquivariantNeural Networks Reveal How Host–GuestInteractions Shape <sup>129</sup>Xe NMR in Porous Liquids Understanding host–guest interactions in porous liquids (PLs) formed from porous organic cages (POCs) is pivotal in tailoring their physicochemical properties, therefore providing an avenue for engineering new PLs with enhanced functionalities. In this work, we demonstrate, for the first time, the use of an accurate and efficient machine-learning-based approach for atomistic modeling of host–guest interactions in large-scale PLs. The approach uses <i>E</i>(3)-equivariant graph neural networks (EGNNs) to construct a machine learning interatomic potential and a nuclear magnetic resonance machine learning model. The former enables machine learning molecular dynamics (MLMD) simulations, while the latter computes the <sup>129</sup>Xe isotropic chemical shift, δ<sub>iso</sub>, from MLMD snapshots. Applied to a PL composed of CC3-<i>R</i> POC in 4-(trifluoromethoxy)benzyl alcohol (TBA) solvent loaded with high Xe concentration, this dual-model approach shows that host(CC3)–guest(Xe) interactions are best described by a three-site binding model comprising the CC3 intrinsic cavity, CC3 openings, and TBA solvent, with exchange events occurring between these sites. Good agreement between computed and experimental <sup>129</sup>Xe δ<sub>iso</sub> validates our approach, demonstrating EGNN-based simulations as transformative tools for advancing the understanding of PLs. Artificial neural network ; Isotropy ; Molecular dynamics ; Porosity ; Graph ; Construct (python library) ; Porous medium Supramolecular Chemistry and Complexes ; Covalent Organic Framework Applications ; Supramolecular Self-Assembly in Materials
Authors: Shengli Jiang | Michael A. Webb
Source: npj Computational Materials (article, 2025-12-15)
Field: Materials Science — Materials Chemistry

Generative active learning across polymer architectures and solvophobicities for targeted rheological behavior Modifying solution viscosity is a key functional application of polymers, yet the interplay of molecular chemistry, polymer architecture, and intermolecular interactions makes tailoring precise rheological responses challenging. We introduce a computational framework coupling topology-aware generative machine learning, Gaussian process modeling, and multiparticle collision dynamics to design polymers yielding prescribed shear-rate-dependent viscosity profiles. Targeting thirty rheological profiles of varying difficulty, Bayesian optimization identifies polymers that satisfy all low- and most medium-difficulty targets by modifying topology and solvophobicity, with other variables fixed. In these regimes, we find and explain design degeneracy, where distinct polymers produce near-identical rheological profiles. However, satisfying high-difficulty targets requires extrapolation beyond the initial constrained design space; this is rationally guided by physical scaling theories. This integrated framework establishes a data-driven yet mechanistic route to rational polymer design. Rheology ; Polymer ; Viscosity ; Scaling ; Extrapolation ; Coupling (piping) ; Generative model ; Intermolecular force Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Rheology and Fluid Dynamics Studies