jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yuan Wang | Heng Liu | Yusuke Hashimoto | Kazuyuki Iwase | H. C. Li | Takaaki Tomai
Source: ChemRxiv (article, 2026-01-29)
Field: Chemistry — Inorganic Chemistry

Materials Process Informatics-Assisted Precise Particle Size Control of Metal Organic Framework Metal-organic framework (MOF) has attracted considerable attention for applications in catalysis, separation, and drug delivery. The particle size of MOF is a key parameter that strongly influences its properties, such as surface area, pore accessibility, and mass transport. However, the precise modulation of particle size during synthesis remains a significant challenge, as conventional methodologies remain predominantly empirical and dependent on experimental intuition. Herein, we established a machine learning (ML) framework designed to correlate multi-dimensional synthesis parameters with the particle size of MOF. Using Zeolitic Imidazolate Framework-8 (ZIF-8) as a model system, a comprehensive database was curated from existing literature to train various ML models. Among these, the Categorical Boosting (CB) algorithm demonstrated superior predictive performance, achieving a coefficient of determination (R2) of 0.90 on the hold-out test set. Subsequent SHapley Additive exPlanations (SHAP) analysis identified zinc precursor concentration as the most influential factor governing particle size. Experimental validation under unexplored conditions in the database showed excellent consistency between the predicted and actual values, highlighting the robustness and reliability of the model. Overall, this study establishes an ML-assisted closed-loop strategy for the controllable synthesis of ZIF-8 with tunable particle sizes, underscoring the transformative potential of artificial intelligence in accelerating data-driven materials synthesis. Particle size ; Robustness (evolution) ; Categorical variable ; Boosting (machine learning) ; Particle (ecology) ; Nanoparticle ; Process (computing) ; Consistency (knowledge bases) Metal-Organic Frameworks: Synthesis and Applications ; Advanced Nanomaterials in Catalysis ; Multicomponent Synthesis of Heterocycles
Authors: Shion Hasegawa | Souki Wakabayashi | Toshiki Sawada | Takeshi Serizawa
Source: ACS Applied Polymer Materials (article, 2026-01-14)
Field: Chemistry — Spectroscopy

Machine Learning-Assisted Classification and Identification of Water-Soluble Polymers Using Protein-Based Fluorescent Sensors Water-soluble polymers (WSPs) are widely used in diverse industrial, biomedical, and consumer applications; however, their release into the environment has raised growing concerns regarding their potential ecological impacts. The development of effective methods for detecting and identifying WSPs in aqueous systems is therefore of increasing importance. In this study, we developed a series of protein-based fluorescent sensors by conjugating a microenvironment-responsive fluorophore (N-(1-anilinonaphthyl-4)maleimide, ANM) into proteins through a linker-mediated conjugation reaction. The resulting sensors exhibited unique fluorescence responses in the presence of various synthetic and biorelated WSPs. These responses varied depending on the protein species and the ANM conjugation rate, reflecting distinct modes of interaction between the sensors and WSPs. The application of multivariate statistical analyses to the obtained fluorescence spectra enabled the precise and reproducible identification of multiple WSPs. Remarkably, nearly all sensors achieved 100% classification accuracy in two cross-validations, confirming their high potential in WSP identification. Moreover, the combination of multiple sensors further enhanced the robustness and reliability of classification, demonstrating complementary sensing behaviors among different proteins. This study provides the first proof of concept for employing naturally derived proteins as molecular sensors for WSP classification and identification. Fluorescence ; Fluorophore ; Robustness (evolution) ; Identification (biology) ; Polymer Molecular Sensors and Ion Detection ; Luminescence and Fluorescent Materials ; Analytical Chemistry and Sensors
Authors: Mertcan Kaya | Jean-Luc Göttsche | Christoph Klahn
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

A Machine Learning-Assisted Multi-Criteria Decision Making Framework for Chemical Reactor Channel Geometry Selection Dimensioning ; Probabilistic logic ; Process (computing) ; Selection (genetic algorithm) ; Surrogate model ; Channel (broadcasting) ; Realization (probability) ; Key (lock) ; Degrees of freedom (physics and chemistry) ; Chemical process Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Kevin Maik Jablonka
Source: Open MIND (preprint, 2026-02-18)
Field: Materials Science — Materials Chemistry

Clever Materials: When Models Identify Good Materials for the Wrong Reasons Machine learning can accelerate materials discovery. Models perform impressively on many benchmarks. However, strong benchmark performance does not imply that a model learned chemistry. I test a concrete alternative hypothesis: that property prediction can be driven by bibliographic confounding. Across five tasks spanning MOFs (thermal and solvent stability), perovskite solar cells (efficiency), batteries (capacity), and TADF emitters (emission wavelength), models trained on standard chemical descriptors predict author, journal, and publication year well above chance. When these predicted metadata ("bibliographic fingerprints") are used as the sole input to a second model, performance is sometimes competitive with conventional descriptor-based predictors. These results show that many datasets do not rule out non-chemical explanations of success. Progress requires routine falsification tests (e.g., group/time splits and metadata ablations), datasets designed to resist spurious correlations, and explicit separation of two goals: predictive utility versus evidence of chemical understanding. Spurious relationship ; Metadata ; Benchmark (surveying) ; Property (philosophy) ; Predictive modelling ; Test (biology) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Big Data and Digital Economy
Authors: Susan R. Atlas
Source: Open MIND (preprint, 2026-03-05)
Field: Materials Science — Materials Chemistry

Latent space design of interatomic potentials The advent of neural-network-based deep learning techniques has led to the emergence of increasingly sophisticated numerical interatomic potentials, including graph neural networks and large language-motivated foundation models. Parameterized to reproduce large, precomputed quantum mechanical training datasets for molecules and materials, models can be fine-tuned for greater accuracy on specific problems. Despite notable successes, machine learning (ML) models of potentials still face intrinsic challenges due to the combinatoric complexity of the underlying quantum chemical interactions, the existence of as-yet-undiscovered but potentially relevant bonding motifs absent from training datasets, and the need for post-prediction interpretability analysis. Drawing inspiration from autoencoder methods, we propose a constructive approach to interatomic potential design. In standard autoencoder architectures, a ML model self-organizes numerical training data in an unsupervised manner to discover underlying patterns and construct a compressed representation or latent space embedding model of the data, which is then used for prediction and inference. In the present work, we describe how latent space patterns and associated quantum embeddings can be constructed using first-principles methods based on theorems of density functional theory (DFT) and known, analytic constraints. This enables a parsimonious, physics-based representation of energies and densities, formally coupling the electronic and atomic length scales through the electron density, and linking ground, excited, and charge-transfer states of the interacting atoms. We describe the complete set of latent space components providing the foundation for a recently-proposed ensemble charge-transfer potential, and discuss opportunities for synergy in the design and explainability of contemporary machine-learned interatomic potentials. Autoencoder ; Interpretability ; Isosurface ; Representation (politics) ; Embedding ; Parameterized complexity ; Space (punctuation) ; Constructive ; Quantum Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Quantum many-body systems
Authors: Kun Han | J.-L. Yang | Chenyu Wang | Junfeng Li | Zhijing Zhu | Wenjie Mai | Jinliang Li | Guang Yang | Likun Pan
Source: Nano Energy (article, 2026-01-16)
Field: Materials Science — Materials Chemistry

MEMFNet: Toward a knowledge-guided paradigm for interpretable electrochemical performance prediction Interpretability ; Architecture ; Electrochemistry ; Cathode ; Genetic programming ; Deep learning ; Battery (electricity) ; Graph ; Network architecture Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Electrocatalysts for Energy Conversion
Authors: Zihao FANG | Tu Zhixin | L.U. Gang | Qingsong YAN | Jinglei Chen | X.U. Liang
Source: Applied Thermal Engineering (article, 2026-03-20)
Field: Materials Science — Materials Chemistry

Physics-informed neural networks for thermal transfer with latent heat during solid-liquid phase change of alloys Phase change ; Latent heat ; Heat transfer ; Artificial neural network ; Thermal ; Phase-change material Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Thermal properties of materials
Authors: İlker Demiroğlu | Tuğbey Kocabaş
Source: Eskişehir Technical University Journal of Science and Technology A - Applied Sciences and Engineering (article, 2026-03-27)
Field: Materials Science — Materials Chemistry

GAUSSIAN APPROXIMATION POTENTIALS FOR FUNCTIONALIZED Pt–Cu NANOPARTICLES Bimetallic Pt–Cu nanoparticles are promising catalysts for oxidation and hydrogenation reactions due to their tunable electronic and geometric properties. However, first-principles simulations of realistic nanoparticle sizes remain computationally prohibitive. In this study, Gaussian Approximation Potential (GAP) models were developed for Pt–Cu nanoparticles functionalized with a single O2 or CO molecule, achieving near-DFT accuracy in energies and forces while drastically reducing computational cost. The training dataset, derived from ab initio molecular dynamics (AIMD) trajectories at 300–1000 K, spans various morphologies (pure, core–shell, Janus, and ordered alloys) and particle sizes (38–260 atoms), capturing both thermal and structural fluctuations representative of realistic catalytic conditions. The resulting GAP models successfully reproduce DFT-level energetics and atomic forces with root-mean-square errors below 0.4 meV atom-1 for energies and 70 meV Å-1 for forces, without overfitting to any specific morphology. AIMD simulations reveal that alloying Pt with Cu enhances thermal and mechanical stability, with core–shell and Janus configurations maintaining ordered atomic coordination up to 1000 K. Radial distribution function (RDF) analysis confirms that short-range order persists at elevated temperatures, ensuring structural integrity under reactive conditions. These results demonstrate that machine-learning-based interatomic potentials provide a robust and transferable framework for exploring adsorption-driven restructuring, morphology evolution, and catalytic stability of Pt–Cu nanoparticles beyond the accessible limits of conventional DFT. Nanoparticle ; Molecular dynamics ; Ab initio ; Gaussian ; Thermal ; Overfitting ; Particle (ecology) ; Interatomic potential ; Catalysis Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Advanced Chemical Physics Studies
Authors: OTÁVIO AUGUSTO SANTOS PORTO | Lucas Ferreira | Anamaria de Oliveira Cardoso
Source: The Journal of Engineering and Exact Sciences (article, 2026-01-06)
Field: Materials Science — Materials Chemistry

Application of Physics-Informed Neural Networks (PINNs) in Predicting Voltametric Profiles: Hyperparameter Impact and Training Optimization Physics-Informed Neural Networks (PINNs) offer a promising approach for predicting voltametric profiles, overcoming traditional numerical limitations by embedding physical laws directly into the machine learning framework. This study systematically evaluates the influence of key hyperparameters (network depth and width), learning rate, and training duration on the performance of PINNs applied to a one-dimensional cyclic voltammetry system governed by Fick’s diffusion law. We implemented a comprehensive experimental design involving the variation of individual hyperparameters and the assessment of convergence, error minimization, and physical consistency. Results demonstrate that optimal PINN configuration, consisting of three hidden layers, a learning rate of 0.0001, and 600 training epochs, achieves a mean squared error below 10?5 and reproduces voltametric profiles virtually indistinguishable from theoretical predictions. The analysis highlights the pronounced sensitivity of PINN models to learning rate selection and training duration, while also revealing that increased network depth does not necessarily improve outcomes, contrary to common neural network assumptions. Results showed the integration of governing equations within the loss function to enhance physical fidelity and computational efficiency, positioning PINNs as robust tools for electrochemical modeling. These findings establish practical guidelines for PINN deployment in chemical engineering, with implications for battery research, sensor development, and catalytic systems. Future work should target the extension to more complex geometries, multi-species diffusion, and experimental validation. Hyperparameter ; Artificial neural network ; Key (lock) ; Hyperparameter optimization ; Mean squared error ; Sensitivity (control systems) ; Supervised learning ; Fidelity ; Function (biology) Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Advanced battery technologies research
Authors: Leonardo G. J. M. Voltarelli | Natalia Osiecka | Marcin Piwowarczyk | Ewa Ewa Juszynska-Galazka | Rafael S. Zola | Matjaz Perc | Haroldo V. Ribeiro
Source: arXiv (Cornell University) (article, 2026-03-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Interpretable liquid crystal phase classification via two-by-two ordinal patterns Liquid crystal textures encode rich structural information, yet mapping these images to mesophase identity remains challenging because visually similar patterns can arise from distinct structures. Here we present a simple, interpretable representation that maps textures to a 75-dimensional frequency vector of two-by-two ordinal patterns, grouped into eleven symmetry-based types to characterize a large-scale dataset spanning seven mesophases. Combined with a simple machine learning classifier, this lightweight representation yields near-perfect phase recognition, including the difficult distinction between smectic A and smectic B mesophases. Our approach generalizes to unseen compounds and accurately distinguishes between phase identity and material origin. Unlike deep learning methods, each ordinal pattern is readily interpretable, and model explanations augmented with network visualizations of pattern interactions reveal the specific types and pairwise dependencies that drive each mesophase decision, providing compact, physically meaningful summaries of texture determinants. These results establish two-by-two ordinal patterns as an interpretable and scalable tool for liquid crystal image analysis, with potential applications to other complex patterned systems in materials science. Mesophase ; Pairwise comparison ; Representation (politics) ; Pattern recognition (psychology) ; Texture (cosmology) ; Identity (music) ; Image (mathematics) ; Simple (philosophy) Liquid Crystal Research Advancements ; Advanced Materials and Mechanics ; Material Dynamics and Properties
Authors: Jordi Buils | Carles Bo | David Balcells
Source: ChemRxiv (article, 2026-04-08)
Field: Materials Science — Materials Chemistry

POMs-GibbsNet: A Deep Neural Network for the Prediction of Thermodynamic Parameters of Polyoxometalates Polyoxometalates (POMs) are versatile metal–oxo clusters with remarkable structural diversity and applications spanning catalysis, energy storage, and materials science. However, accurately predicting their thermodynamic properties remains computationally demanding due to the large system sizes and the cost of density functional theory (DFT) frequency calculations required to obtain Gibbs free energies. In this work, we present POMs-GibbsNet, a deep neural network designed to predict the thermal free energy correction (G−E) of polyoxometalates by combining graph-based molecular representations with DFT-derived descriptors. Molecular structures were encoded using variants of the AABBA autocorrelation graph kernel, which captures atom–atom, bond–bond, and atom–bond correlations together with quantum-derived features including QTAIM descriptors. POMs-GibbsNet was trained on a dataset of 1625 POM structures spanning 19 chemical systems. A multilinear regression model based on global molecular descriptors was first evaluated as a baseline, achieving R2 values of 0.84–0.92 but with relatively large prediction errors (RMSE 9 kcal mol−1). In contrast, POMs-GibbsNet captures nonlinear structure–property relationships and significantly improves predictive accuracy. External validation using POMSimulator reaction networks comprising over 9×104 reactions shows excellent agreement between predicted and DFT reaction energies (R2 > 0.99), with errors typically within a few kcal mol−1. Notably, simplified atom–atom autocorrelation descriptors already provide robust performance while reducing model complexity. These results demonstrate that POMs-GibbsNet enables efficient estimation of thermodynamic corrections in POM systems, providing a powerful framework for accelerating the computational exploration of polyoxometalate speciation and self-assembly across large chemical spaces. Multilinear map ; Artificial neural network ; Polyoxometalate ; Autocorrelation ; Gibbs free energy ; Energy (signal processing) ; Molecular dynamics Polyoxometalates: Synthesis and Applications ; Machine Learning in Materials Science ; Catalysis and Oxidation Reactions
Authors: Anton Bochkarev | Yury Lysogorskiy | Aparna P. A. Subramanyam | Ralf Drautz | Danny Perez
Source: Open MIND (preprint, 2026-02-26)
Field: Materials Science — Materials Chemistry

Exploring the extremes: atomic basis for multi-elemental materials science under complex thermodynamic conditions Modern materials science has historically been founded on combining restricted subsets of the periodic table, favoring high-purity, few-element systems. However, the demands of an emerging circular economy, together with the need to understand materials behavior under planetary and industrial extremes, increasingly require mastering Mendeleev materials - chemically and structurally complex systems that span large portions of the periodic table. In these regimes, current universal machine-learning interatomic potentials often fail, largely due to systematic gaps in traditional training datasets that heavily emphasize low-energy, near-equilibrium structures. We address this limitation by introducing a chemistry-agnostic, information-entropy-maximization protocol for data generation. By decoupling structural sampling from thermodynamic bias, our approach provides a robust physical prior for atomic interactions across the entire periodic table, including regimes far from equilibrium and under extreme conditions. Training a Graph Atomic Cluster Expansion (GRACE) model on the resulting statistically maximized entropy (SMAX) dataset yields markedly improved robustness across a range of stringent benchmarks. These include large-strain phase transformations in tin, defect evolution in tungsten-based alloys, and catalytic reaction barrier prediction. More broadly, our approach establishes a scalable and principled methodology for navigating the vast chemical and configurational space relevant to future materials design. It enables a paradigm of discovery by simulation in which unbiased sampling protocols autonomously resolve emergent structures in multi-elemental mixtures-such as systems containing the nine most abundant elements in the Earth's crust-without reliance on a priori chemical assumptions. Scalability ; Chemical space ; A priori and a posteriori ; Robustness (evolution) ; Decoupling (probability) ; Complex system ; Entropy (arrow of time) ; Physical system Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Inorganic Chemistry and Materials
Authors: Marcela Elisabeth Penoff | Facundo I. Altuna | Luis A. Miccio
Source: Applied Sciences (article, 2026-03-06)
Field: Materials Science — Materials Chemistry

Cloud Point Temperature of Thermoresponsive Systems: A Predictive Approach in Data Scarcity Conditions In this study, we employ machine learning techniques to improve materials in data scarcity conditions. In particular, we focus on the prediction of the cloud point temperatures of polymer–water systems with thermoresponsive behavior. We compare a model trained directly on the available data with a model based on representations learned through an encoder–decoder model, in turn pre-trained on a larger dataset to generate molecular fingerprints. Our results demonstrate that the embedding-based model significantly outperforms the direct model in predicting the cloud point temperature under the data limitations imposed by rigorous curation. This approach highlights the potential of domain-informed representation learning to tackle complex materials science problems with limited data. Scarcity ; Cloud computing ; Representation (politics) ; Focus (optics) ; Point cloud ; Point (geometry) Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Phase Equilibria and Thermodynamics
Authors: Yue Li | Tao Yang | Dongmei Fu
Source: Process Safety and Environmental Protection (article, 2026-03-16)
Field: Materials Science — Materials Chemistry

DMCorr: A Deep learning-based Multi-source data fusion model for Corrosion rate prediction of metallic materials Corrosion ; Fusion ; Metal ; Sensor fusion ; Data modeling ; Metal coating Machine Learning in Materials Science ; Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Bing Zeng | Dehui Zheng | Yi Jin | Tingting Hou | Minglin Liu | Zihang Yang | Maruf S.M. Ul Haque | Yingcong Liang | Shahid Ali | Yue Zhang | Muhammad Siddique Ahmad | Kamel Meguellati
Source: Journal of Catalysis (article, 2026-04-09)
Field: Materials Science — Materials Chemistry

Neuromorphic allostery in a porous metallated framework: adaptive Lewis-acid catalysis for the synthesis of key pharmaceuticals Neuromorphic engineering ; Catalysis ; Key (lock) ; Porosity ; Heterogeneous catalysis Polyoxometalates: Synthesis and Applications ; Mesoporous Materials and Catalysis ; Covalent Organic Framework Applications