jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Timofei Miryashkin | Olga Klimanova | Alexander V. Shapeev
Source: ArXiv.org (preprint, 2025-06-21)
Field: Materials Science — Materials Chemistry

Clarifying the Ti-V Phase Diagram Using First-Principles Calculations and Bayesian Learning Conflicting experiments disagree on whether the titanium-vanadium (Ti-V) binary alloy exhibits a body-centred cubic (BCC) miscibility gap or remains completely soluble. A leading hypothesis attributes the miscibility gap to oxygen contamination during alloy preparation. To resolve this disagreement, we use an ab initio + machine-learning workflow that couples an actively-trained Moment Tensor Potential with Bayesian inference of free energy surface. This workflow enables construction of the Ti-V phase diagram across the full composition range with systematically reduced statistical and finite-size errors. The resulting diagram reproduces all experimental features, demonstrating the robustness of our approach, and clearly favors the variant with a BCC miscibility gap terminating at T = 980 K and c = 0.67. Because our simulations model a perfectly oxygen-free Ti-V system, the observed gap cannot originate from impurity effects, in contrast to recent CALPHAD reassessments. CALPHAD ; Phase diagram ; Inference ; Spinodal decomposition ; Miscibility ; Ab initio ; Bayesian probability ; Bayesian inference ; Workflow Machine Learning in Materials Science ; Titanium Alloys Microstructure and Properties ; Model Reduction and Neural Networks
Authors: Ishana Saroha
Source:  (preprint, 2025-06-29)
Field: Chemistry — Analytical Chemistry

Early Detection of Drug Toxicity using Neural Networks Toxicity prediction of drugs is a critical step in the drug development process, as it evaluates the safety of drugs.An abundance of resources go into the development of new drugs, yet only 12% of all drugs are considered by the FDA, since many potential drugs are toxic.By the time toxicity has been identified in conventionally developed drugs, anywhere from $1 to $2 billion could've been invested.Therefore, it is imperative to have an early detection of drug toxicity.AI can be used to predict toxicity, by using QSAR and machine learning methods.Some ML based (DNN, SVM, RF) solutions have been proposed that use only molecular features of the compounds, not molecular structure.The goal of this project was to create a GNN ML model (which uses both atomic features and molecular structure information) that could make better toxicity predictions.Tox21 toxicity dataset was used for all training and evaluation.Three GNN models were created and then compared to a SVM model.Statistical analysis of results showed that the GNN models performed better than the SVM model, with the GNN models having better F1-scores (3.34%-6.44%improvement) and MCC values (5.48%-9.40%improvement).Results of this project show that GNN-based models have better toxicity prediction compared to SVM-based models.GNNs have great potential to augment existing QSAR methods used in predicting toxicity in the drug development industry, thus reducing cost, time and resources invested, and alleviating ethical concerns of animals/clinical trials when compared to conventionally developed drugs.This model can help in molecule toxicity prediction for pharmaceutical and other industries. Toxicity ; Drug ; Drug toxicity ; Artificial neural network ; Computer science ; Artificial intelligence ; Pharmacology ; Medicine ; Internal medicine Analytical Methods in Pharmaceuticals ; Computational Drug Discovery Methods
Authors: Sri Vishnuvardhan
Reddy Akepati (18190103) | Nitant Gupta (1699606) | Jay Shah (4051903) | Stephen Kronenberger (19440828) | Vaibhav Venkat (22692841) | Rohan Adhikari Sridhar (22692844) | Simona Bianco (21340442) | Dave J. Adams (1297446) | Arthi Jayaraman (629537)
Source: Figshare (article, 2025-11-26)
Field: Materials Science — Biomaterials

Tutorial:Machine-Learning-Based CREASE-2D Analysisof 2D SAXS Profiles to Characterize Anisotropic Nanostructures inSoft Materials We present a tutorial to guide users on how to extend the Computational Reverse Engineering Analysis of Scattering Experiments-2D (CREASE-2D) framework to interpret their experimental two-dimensional small-angle scattering (SAS) data from soft materials (e.g., polymers, peptide amphiphiles, biomolecular fibrils). Unlike most traditional SAS analysis approaches, which typically rely on azimuthally averaged one-dimensional (1D) profiles, CREASE-2D utilizes the complete 2D scattering profile to reveal information about anisotropy in the structure. In past applications, CREASE has provided insights into complex structural features, including the cross-sectional shapes of assembled nanostructures and dispersity in these features, which are difficult to discern with existing analytical models. While (1D-) CREASE has been applied to SANS and SAXS data, this tutorial shares the steps for implementing CREASE-2D using an example of a dipeptide solution system, for which we have SAXS data. We present details for these steps involved in using CREASE-2D to interpret SAXS profiles: how to preprocess SAXS data, define relevant structural features, generate three-dimensional real-space structures for specific values of these features, train a machine learning (ML) surrogate model to predict scattering profiles for given structural features, and optimize these features using genetic algorithms (GA). Then, we use these steps to interpret complex 2D-SAXS data collected from dipeptide solutions that, in microscopy images, exhibit nanoscale structures that could be elliptical tubes/flat tapes/cylinders or a combination of these cross sections. Open-source codes, computational hardware, and software requirements, as well as the strengths and limitations of this protocol, are also presented. We expect researchers working with (soft) biomaterials, peptide amphiphiles, amphiphilic polymer solutions, polymer nanocomposites, and blends of particles/polymers will find this CREASE-2D method and this tutorial of use. Small-angle X-ray scattering ; Scattering ; Nanostructure ; Small-angle scattering ; Anisotropy ; Segmentation Supramolecular Self-Assembly in Materials ; Block Copolymer Self-Assembly ; Advanced Polymer Synthesis and Characterization
Authors: Yifei Yang | Zhenyu Guo | Yun Zhao | Haobo Dong | Maria‐Magdalena Titirici | Frederic Cegla | Valerie J. Pinfield | Bo Lan
Source: ChemRxiv (article, 2025-11-14)
Field: Chemistry — Physical and Theoretical Chemistry

Towards Inline Ultrasonic Characterisation of Battery Slurry Mixing: Opportunities, Challenges, and Perspectives Inefficiencies in the slurry mixing stage are a major factor in high scrap rates in battery manufacturing, thus hindering sustainable production. Current offline characterisation techniques for slurry microstructure and rheology are slow and inadequate for closed-loop quality control or process optimisation. This review evaluates ultrasound as a promising inline, non-invasive characterisation tool to address this crucial need. We critically examine decades of developments and applications in ultrasonic evaluation techniques, assessing their relevance and identifying challenges specific to the high-concentration, non-Newtonian battery slurries. Key wave-slurry interaction mechanisms, including attenuation, wave speed, scattering, and guided wave propagation, are discussed in the context of characterising microstructural features (e.g. particle dispersion and agglomeration) and macroscopic rheological properties (e.g. viscosity and viscoelasticity). To make full use of the crucial yet limited information accessible via ultrasound, we propose a hybrid framework marrying ultrasonic and other offline data through physics-informed machine learning for accurate and comprehensive property estimation. With the analyses and framework, this review points to a clear path towards achieving robust inline monitoring and efficient optimisation of battery slurry mixing. Scrap ; Ultrasonic sensor ; Context (archaeology) ; Battery (electricity) ; Slurry ; Rheology ; Process (computing) ; Viscosity ; Relevance (law) Electrostatics and Colloid Interactions ; Ultrasound and Cavitation Phenomena ; Advanced Battery Materials and Technologies
Authors: Jinyoung Park | Ruth M. Muthoka | Sunghyun Jang | Yongjin Lee
Source: Industrial & Engineering Chemistry Research (article, 2025-09-26)
Field: Materials Science — Materials Chemistry

A Multi-Stage Graph Neural Network–Physics-Informed Neural Network (GNN–PINN) Framework for Thermodynamic Property Prediction Accurately predicting thermodynamic properties across various conditions remains a critical challenge, particularly in scenarios involving sparse data or complex molecular interactions. This study proposes a multistage hybrid modeling framework that integrates Graph Neural Networks (GNN) and Physics-Informed Neural Networks (PINN) to predict essential thermodynamic properties, including enthalpy and entropy, for pure substances under various conditions. The model is developed in three distinct stages. First, a GNN encoder captures atomic-level interactions (both bonded and nonbonded) from molecular structures, generating structurally enriched molecular embeddings while leveraging critical constants and reduced state variables through a masking strategy that enables learning from single-phase data sets. Second, a regression submodel utilizes these embeddings to accurately predict saturation pressure (Psat) from molecular structure and temperature, modeling phase equilibrium behavior. Finally, the third stage employs PINN-based fine-tuning, embedding thermodynamic constraints─such as Gibbs free energy equality at phase equilibrium and enthalpy–entropy coupling─as penalties in the loss function to enforce thermodynamic consistency. This integrated GNN–PINN approach accurately predicts vapor- and liquid-phase enthalpies, entropies, and saturation pressures, maintaining robust performance even at equilibrium conditions. The model offers a physically consistent and reliable method for predicting thermodynamic properties, effectively capturing complex molecular interactions while adhering to fundamental physical laws. Artificial neural network ; Gibbs free energy ; Embedding ; Thermodynamic process ; Graph ; Enthalpy ; Molecular graph ; Material properties ; Thermodynamic system ; Thermodynamic state Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Yang Huang (354741) | Jingrun Chen (1675852)
Source: Figshare (article, 2025-09-17)
Field: Materials Science — Materials Chemistry

A Generalized SolidSolution Framework for the GibbsFree Energy Calculation We propose a generalized solid solution model for calculating configurational contribution to the Gibbs free energy at finite temperatures, incorporating a crystal graph-based on-site energy approach. By leveraging linear graph neural networks, our method unifies pair-based and cluster expansion approaches, enabling broad applicability across crystal structures. Fractional occupation is physically interpreted via mean-field theory, while entropy is modeled using ideal mixing with extended site constraints. To resolve the constants of compositions, we implement three key strategies. First, we employ a softmax-based variable transformation. Second, we introduce a gradient projection method that preserves species composition throughout the optimization process by constraining updates within a subspace that maintains the desired elemental ratios. Finally, a renormalization step is incorporated to correct numerical deviations, ensuring strict adherence to the target composition. We then apply our model to the Mo–Nb–Ta-W quaternary system, achieving an energy model MAE of 1.24 meV. Predicted phase transition temperatures for equal atomic binary alloys align well with expectations, identifying phase separation in MoNb and order–disorder transitions in MoTa, MoW, TaW, NbTa, and NbW. At low temperatures, stable configurations lie below the convex hull of the training data set, demonstrating the model’s predictive accuracy. Further analysis of MoNbTaW reveals transition temperatures at 950 and 400 K, with observed asymmetry in Mo/W sublattices. Finally, we extend our approach to ternary phase diagram predictions using Gibbs free energy interpolation and second-derivative analysis, yielding phase diagrams in agreement with optimized atomic configurations. Gibbs free energy ; Subspace topology ; Phase diagram ; Phase transition ; Ternary operation ; Projection (relational algebra) ; Solution set ; Asymmetry ; Convex hull Machine Learning in Materials Science ; High Entropy Alloys Studies ; Nuclear Materials and Properties
Authors: Jinyoung Park (134860) | Ruth M. Muthoka (16858978) | Sunghyun Jang (22317635) | Yongjin Lee (1428223)
Source: Figshare (article, 2025-09-26)
Field: Materials Science — Materials Chemistry

A Multi-Stage Graph Neural Network–Physics-InformedNeural Network (GNN–PINN) Framework for Thermodynamic PropertyPrediction Accurately predicting thermodynamic properties across various conditions remains a critical challenge, particularly in scenarios involving sparse data or complex molecular interactions. This study proposes a multistage hybrid modeling framework that integrates Graph Neural Networks (GNN) and Physics-Informed Neural Networks (PINN) to predict essential thermodynamic properties, including enthalpy and entropy, for pure substances under various conditions. The model is developed in three distinct stages. First, a GNN encoder captures atomic-level interactions (both bonded and nonbonded) from molecular structures, generating structurally enriched molecular embeddings while leveraging critical constants and reduced state variables through a masking strategy that enables learning from single-phase data sets. Second, a regression submodel utilizes these embeddings to accurately predict saturation pressure (<i>P</i><sup>sat</sup>) from molecular structure and temperature, modeling phase equilibrium behavior. Finally, the third stage employs PINN-based fine-tuning, embedding thermodynamic constraintssuch as Gibbs free energy equality at phase equilibrium and enthalpy–entropy couplingas penalties in the loss function to enforce thermodynamic consistency. This integrated GNN–PINN approach accurately predicts vapor- and liquid-phase enthalpies, entropies, and saturation pressures, maintaining robust performance even at equilibrium conditions. The model offers a physically consistent and reliable method for predicting thermodynamic properties, effectively capturing complex molecular interactions while adhering to fundamental physical laws. Artificial neural network ; Gibbs free energy ; Thermodynamic process ; Embedding ; Graph ; Thermodynamic system ; Property (philosophy) ; Enthalpy ; Material properties Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Phase Equilibria and Thermodynamics
Authors: Xiangyang Deng | Huan Ma | Jinjia Liu | Wenping Guo | Xiaodong Wen
Source: The Journal of Physical Chemistry C (article, 2025-12-02)
Field: Chemistry — Inorganic Chemistry

A Benchmark Framework for the Fast and Reliable Evaluation of Zeolite Surface Stability Accurately identifying the external surface structure and crystal morphology of zeolites is essential for establishing structure–performance relationships. Surface free energy serves as a quantitative descriptor of surface stability, which is a prerequisite for theoretical investigations of surface properties. However, a standard computational protocol that effectively balances accuracy and efficiency has yet to be established. To address this, we propose an efficient computational workflow developed through systematic benchmarking. This workflow integrates two key components: an analytical model developed using the SISSO machine learning approach to replace expensive frequency calculations and a configuration screening process based on machine learning potentials to account for the vast number of possible surface terminations. Using this workflow, we efficiently calculate the surface free energies of MFI and FAU zeolites and explore their morphological evolution under dynamic conditions. This study establishes a general and efficient theoretical framework for investigating the thermodynamic properties of zeolite surfaces. Benchmark (surveying) ; Surface (topology) ; Workflow ; Stability (learning theory) ; Process (computing) ; Key (lock) ; Zeolite ; Protocol (science) Zeolite Catalysis and Synthesis ; Machine Learning in Materials Science ; Catalysis and Oxidation Reactions
Authors: Hossein Tahmasbi | Andreas Knüpfer | Thomas D. Kühne | Hossein Mirhosseini
Source: arXiv (Cornell University) (preprint, 2025-12-23)
Field: Materials Science — Materials Chemistry

Benchmarking Universal Machine Learning Interatomic Potentials on Elemental Systems The rapid emergence of universal Machine Learning Interatomic Potentials (uMLIPs) has transformed materials modeling. However, a comprehensive understanding of their generalization behavior across configurational space remains an open challenge. In this work, we introduce a benchmarking framework to evaluate both the equilibrium and far-from-equilibrium performance of state-of-the-art uMLIPs, including three MACE-based models, MatterSim, and PET-MAD. Our assessment utilizes Equation-of-State (EOS) tests to evaluate near-equilibrium properties, such as bulk moduli and equilibrium volumes, alongside extensive Minima Hopping (MH) structural searches to probe the global Potential Energy Surface (PES). Here, we assess universality within the fundamental limit of unary (elemental) systems, which serve as a necessary baseline for broader chemical generalization and provide a framework that can be systematically extended to multicomponent materials. We find that while most models exhibit high accuracy in reproducing equilibrium volumes for transition metals, significant performance gaps emerge in alkali and alkaline earth metal groups. Crucially, our MH results reveal a decoupling between search efficiency and structural fidelity, highlighting that smoother learned PESs do not necessarily yield more accurate energetic landscapes. Benchmarking ; Maxima and minima ; Universality (dynamical systems) ; Granularity ; Interatomic potential ; Benchmark (surveying) ; Generalization ; Decoupling (probability) Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Quantum many-body systems
Authors: Vikas Tiwari (9046553) | Tarak Karmakar (810867)
Source: Figshare (article, 2025-03-31)
Field: Materials Science — Materials Chemistry

Atomistic Detailsof Nanocluster Formation from Machine-Learned-Potential-BasedSimulations Understanding the mechanism for the formation of metal nanoclusters is an open challenge in nanoscience. Computational modeling can provide molecular details of nanocluster formation that are otherwise inaccessible. However, simulating nanocluster nucleation in solution presents significant challenges, including inaccurate energy predictions and limitations on the system size and time scale. This work addresses these challenges by combining deep neural networks (DNNs) with well-tempered metadynamics (WT-MetaD) to model the nucleation of a prototypical nanocluster, Ag<sub>6</sub>(SCNH<sub>2</sub>)<sub>6</sub> in methanol. A neural-network-potential-based unbiased molecular dynamics simulation captured the cluster’s dynamic behavior, while WT-MetaD simulations revealed an almost barrierless transition from dispersed precursors to a nucleated state. The method’s robustness was further demonstrated by scaling up to 30 randomly distributed precursors, which resulted in spontaneous nucleation. This study presents the first successful DNN model of nanocluster formation in solution with density-functional-theory-level accuracy, paving the way for advancements in the field. Metadynamics ; Nanoclusters ; Nucleation ; Molecular dynamics ; Scaling ; Robustness (evolution) ; Complex system Machine Learning in Materials Science ; Nanocluster Synthesis and Applications ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Cesare Roncaglia (10778467) | Fábio Lopes (20970616) | Nick Goossens (20970619) | Michael Stuer (14782079) | Daniele Passerone (1761244)
Source: Figshare (article, 2025-04-01)
Field: Materials Science — Materials Chemistry

Machine LearningLattice Parameters of M<sub>2</sub>AX Phases MAX phases, with a general composition of M<sub><i>n</i>+1</sub>AX<sub><i>n</i></sub>, are layered materials with hexagonal symmetry that have increasingly captivated a lot of attention because of their unique way of combining ceramic and metallic properties into a homogeneous bulk material. We developed a machine learning approach to predict the lattice parameters a and c of M<sub>2</sub>AX phases. This approach consists of training an ensemble model on a data set collecting all experimentally synthesized M<sub>2</sub>AX phases’ lattice parameters. Our approach combines a data augmentation scheme with state-of-the-art regression models and hyperparameter optimization tools. We tested our model on newly synthesized compositionally complex high-entropy M<sub>2</sub>AX phases with positive results. Finally, we also show that our machine learning predictions of lattice parameters are useful as initial values for variable-cell relaxations of M<sub>2</sub>AX structures with the density functional theory. Lattice (music) ; Homogeneous ; Hyperparameter ; Hexagonal crystal system ; Crystal structure ; Ensemble learning ; Hexagonal lattice MXene and MAX Phase Materials ; Inorganic Chemistry and Materials ; Machine Learning in Materials Science
Authors: Zhangqian Wei | Jin-En He | S. K. Li | Zhe‐Ning Chen | Lu Zhang | Jun Chen
Source: The Journal of Chemical Physics (article, 2025-12-16)
Field: Materials Science — Materials Chemistry

Resolving the “CO puzzle”: Disentangling electronic structure and dynamic effects via an <i>operando</i> dynamics framework The adsorption and activation of carbon monoxide (CO) on catalytic surfaces are fundamental steps in heterogeneous catalytic processes relevant to energy conversion, environmental remediation, and fine chemical synthesis. A notable theoretical challenge in this context is the "CO puzzle," wherein conventional density functional theory methods incorrectly predict a hollow-site preference for CO adsorption on Pt(111), contrary to experimental observations favoring the top site. This discrepancy has been primarily attributed to limitations in describing the electronic structure; however, recent studies have highlighted the potential role of dynamical effects. The respective contributions of electronic structure and dynamics to site preference remain unresolved. In this study, we systematically examine the interplay between electronic structure and dynamics by employing high-dimensional neural network potential energy surfaces (NN-PESs) integrated with molecular dynamics simulations, using both PBE and vdW-DF functionals. Our results demonstrate that dynamic effects, particularly entropic contributions, are crucial for accurately capturing temperature-dependent adsorption behavior. PBE-based PES simulations predict a preference for hollow-site adsorption at low temperatures but shift toward top-site adsorption at elevated temperatures, aligning with experimental trends. In comparison, vdW-DF-based simulations consistently favor the top site across all temperatures. In addition, comparison with lower-dimensional (6D) rigid PES simulations underscores the importance of adsorbate-surface atoms coupling in accurately modeling thermodynamic properties and site preferences. These findings disentangle the roles of electronic structure and dynamics in resolving the CO puzzle, establishing a robust framework for modeling catalytic processes under operando conditions. Electronic structure ; Molecular dynamics ; Context (archaeology) ; Adsorption ; Coupling (piping) ; Density functional theory ; Dynamics (music) ; Carbon monoxide Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; CO2 Reduction Techniques and Catalysts