jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Klaus Weinbauer | Tieu-Long Phan | Peter F. Stadler | Thomas Gärtner | S. Malhotra
Source: ArXiv.org (preprint, 2025-10-10)
Field: Materials Science — Materials Chemistry

Prime Implicant Explanations for Reaction Feasibility Prediction Machine learning models that predict the feasibility of chemical reactions have become central to automated synthesis planning. Despite their predictive success, these models often lack transparency and interpretability. We introduce a novel formulation of prime implicant explanations--also known as minimally sufficient reasons--tailored to this domain, and propose an algorithm for computing such explanations in small-scale reaction prediction tasks. Preliminary experiments demonstrate that our notion of prime implicant explanations conservatively captures the ground truth explanations. That is, such explanations often contain redundant bonds and atoms but consistently capture the molecular attributes that are essential for predicting reaction feasibility. Implicant ; Prime (order theory) ; Transparency (behavior) ; Training set ; Synthetic data Machine Learning in Materials Science ; AI-based Problem Solving and Planning ; Computational Drug Discovery Methods
Authors: A.M. Neves | Willi Großmann | Julián Atílio Puskiel | Jan Warfsmann | Vahid Reza Hosseini | Maximilian Passing | Thomas Carraro | Thomas Klassen | Oliver Niggemann | Julian Jepsen
Source: Energy and AI (article, 2025-11-29)
Field: Materials Science — Materials Chemistry

Kinetic-model identification in metal-hydride reactions using neural network autoencoder surrogate models Solid-state hydrides can reversibly absorb and desorb H 2 under comparatively mild temperature and pressure conditions, making them promising candidates for H 2 storage in renewable energy applications. The underlying gas-solid interactions are complex and involve multiple intermediary steps. Because they occur in series, by fitting experimental data employing several proposed models, it is possible to identify the rate-limiting step of the reaction, driving the development of new catalysts and the design of H 2 -storage systems. The corresponding state-of-the-art method for model identification is the reduced-time method (RTM), which is time-consuming and often yields inconclusive results. To overcome these limitations and to facilitate automatization, this work proposes a framework with 12 unsupervised neural networks (NNs) which are trained using simulated curves from selected kinetic models. These networks are applied to a dataset of 144 experimental kinetic curves of an AB 2 hydride-forming alloy as a blueprint material. Each NN attempts to reconstruct the input data, and the model with the lowest reconstruction loss is selected. The machine learning algorithm achieved a match of 97% and 91% for the absorption/desorption curves compared to the benchmark. Both reactions follow predominantly the Avrami-Erofeyev model with exponents ( n ) between 0.8 and 0.9. The kinetic constants ( k ) derived from the assigned model are used to simulate kinetic curves, showing excellent agreement with experimental data and RTM results. The proposed method provides an advantageous approach that can be applied to most gas-solid or even solid-solid reactions. • Characterization of the kinetic behavior using surrogate machine learning models. • Neural network autoencoder surrogate models reproduce the human analysis. • Time for data analysis reduced by over 99%. Artificial neural network ; Autoencoder ; Identification (biology) ; Pattern recognition (psychology) ; Feature (linguistics) Hydrogen Storage and Materials ; Catalysts for Methane Reforming ; Machine Learning in Materials Science
Authors: Z. A. Liu | Zhimin Zhang | Xin Liu | Mingjia Yao | Xin He | Yuanhui Sun | Xin Chen | Lijun Zhang
Source: ArXiv.org (preprint, 2025-07-01)
Field: Materials Science — Materials Chemistry

Monolayer Two-dimensional Materials Database (ML2DDB) and Applications The discovery of two-dimensional (2D) materials with tailored properties is critical to meet the increasing demands of high-performance applications across flexible electronics, optoelectronics, catalysis, and energy storage. However, current 2D material databases are constrained by limited scale and compositional diversity. In this study, we introduce a scalable active learning workflow that integrates deep neural networks with density functional theory (DFT) calculations to efficiently explore a vast set of candidate structures. These structures are generated through physics-informed elemental substitution strategies, enabling broad and systematic discovery of stable 2D materials. Through six iterative screening cycles, we established the creation of the Monolayer 2D Materials Database (ML2DDB), which contains 242,546 DFT-validated stable structures-an order-of-magnitude increase over the largest known 2D materials databases. In particular, the number of ternary and quaternary compounds showed the most significant increase. Combining this database with a generative diffusion model, we demonstrated effective structure generation under specified chemistry and symmetry constraints. This work accomplished an organically interconnected loop of 2D material data expansion and application, which provides a new paradigm for the discovery of new materials. Workflow ; Scalability ; Monolayer ; Ternary operation ; Set (abstract data type) ; Density functional theory Machine Learning in Materials Science ; 2D Materials and Applications ; Surface Chemistry and Catalysis
Authors: Nima Karimitari | Teerachote Pakornchote | Abdulaziz Alherz | Jacob M. Clary | Cooper Tezak | Sourin Dey | Jianjun Hu | Derek Vigil‐Fowler | Ravishankar Sundararaman | Charles B. Musgrave | Christopher Sutton
Source: ACS Materials Letters (article, 2025-11-04)
Field: Materials Science — Materials Chemistry

Δ-Learning of High-Fidelity Electronic Structure Using Graph Neural Networks with Modified Node-Level Features In this work, we present a Δ-learning approach for predicting the eigenvalues calculated with the hybrid functional HSE06 (ϵnkHSE) for a set of metal and nitrogen doped graphene catalysts (MNCs) from Perdew-Burke-Ernzerhof (PBE) inputs. The model presented here incorporates electronic scalar features along with structural information in a graph neural network (GNN). In particular, the PBE eigenvalues for different bands and k-points and orbital-resolved projectors are combined with the applied potential as node-level features along with structural information within the Atomistic Line Graph Neural Network (ALIGNN) architecture. These features enable flexibility for systems with electrified interfaces, such as in electrocatalysts and achieves mean absolute error (MAE) of less than 0.1 eV. The machine learning model reported here achieves a strong generalization to left-out adsorbates (MAE = 0.074 eV) and leave-one-chemical-space-out (MAE = 0.08 eV) and completely left-out metals (MAE = 0.072 eV), confirming the robustness of the machine learning (ML) model in predicting ϵnkHSE. Artificial neural network ; Eigenvalues and eigenvectors ; Robustness (evolution) ; Graph ; Electronic structure ; Molecular graph ; Generalization Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; CO2 Reduction Techniques and Catalysts
Authors: И. А. Рубцов | Ivan Dudakov | Yuri Kuratov | Vadim Korolev
Source: ArXiv.org (preprint, 2025-11-05)
Field: Materials Science — Materials Chemistry

Enhancing composition-based materials property prediction by cross-modal knowledge transfer Crystal graph neural networks are widely applicable in modeling experimentally synthesized compounds and hypothetical materials with unknown synthesizability. In contrast, structure-agnostic predictive algorithms allow exploring previously inaccessible domains of chemical space. Here we present a universal approach for enhancing composition-based materials property prediction by means of cross-modal knowledge transfer. Two formulations are proposed: implicit transfer involves pretraining chemical language models on multimodal embeddings, whereas explicit transfer suggests generating crystal structures and implementing structure-aware predictors. The proposed approaches were benchmarked on LLM4Mat-Bench and MatBench tasks, achieving state-of-the-art performance in 25 out of 32 cases. In addition, we demonstrated how another modeling aspect of chemical language models - interpretability - benefits from applying a game-theoretic approach, which is able to incorporate high-order feature interactions. Interpretability ; Property (philosophy) ; Feature (linguistics) ; Knowledge transfer ; Artificial neural network ; Graph ; Transfer of learning Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Crystallography and molecular interactions
Authors: Sen Lu (662866) | Zhiguo Wang (219272) | Zhikai Gao (16892091) | Tiren Peng (21072501) | Pei Song (2930583) | Zepeng Jia (15399426) | Yuhang Zhou (1391788) | Hong Cui (209000) | Weizhi Tian (21072504) | Rong Feng (9161534) | Lingxia Jin (2004163) | Hongkuan Yuan (1919947)
Source: Figshare (article, 2025-04-10)
Field: Materials Science — Materials Chemistry

Modulationof Hydrogen Evolution Reaction Performanceof MXenes by Doped Transition Metals: Comprehensive Exploration ofHigh-Throughput Computing and Machine Learning Due to the unique properties of MXenes, the doping of transition metals can modulate their catalytic properties and make them potential materials for hydrogen evolution reaction (HER). Nevertheless, the extensive combinatorial space poses a challenge for rapid screening of catalysts. To address this issue, we conducted high-throughput calculations on a series of transition metal atom-doped Ti<sub>3</sub>CNO<sub>2</sub> and Zr<sub>2</sub>HfCNO<sub>2</sub>. Furthermore, the local structure and the corresponding electronic structure changes are analyzed, focusing on their influence on the HER properties. Furthermore, site identification features were introduced to train a multisite prediction model with a final model accuracy of <i>R</i><sup>2</sup> = 0.97 and predicted the trend of hydrogen adsorption Gibbs free energy (ΔG<sub>H*</sub>) across a range of MXenes structures, which were doped with TM atoms. The results show that Nb, Sc, Rh, W, Ti, and V doping resulted in |ΔG<sub>H*</sub>| < 0.2 eV for more than 38 M′<sub>2</sub>M″CNO<sub>2</sub>, respectively, and they are effective dopant atoms for enhancing the catalytic ability of M′<sub>2</sub>M″CNO<sub>2</sub>. This study not only demonstrates the potential of doped TM atoms in enhancing the performance of MXenes HER but also highlights the importance of multisite prediction models in the rapid identification and development of efficient HER catalysts. MXenes ; Doping ; Dopant ; Hydrogen ; Transition metal ; Identification (biology) ; Catalysis MXene and MAX Phase Materials ; Electrocatalysts for Energy Conversion ; Ammonia Synthesis and Nitrogen Reduction
Authors: Gavin Winter | Nathan Walemba | Rafael Gómez‐Bombarelli
Source: ChemRxiv (article, 2025-09-26)
Field: Materials Science — Materials Chemistry

Simulations enabled by machine learning potentials predict sulfide solid electrolyte with covalent network structure and glassy behavior Ionic conductivity ; Fast ion conductor ; Ionic bonding ; Electrolyte ; Conductivity ; Electrochemical window ; Phase (matter) ; Sulfide Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Fuel Cells and Related Materials
Authors: Evelyn Julieth Calderón Flórez
Source: Universidad Industrial de Santander (article, 2025-11-24)
Field: Chemistry — Analytical Chemistry

Predictive models of physico-chemical properties of petroleum using FTIR-ATR and Machine learning methods Esta investigación describe el desarrollo y evaluación de modelos de machine learning líneales y no lineales para determinar propiedades fisicoquímicas de crudos medios y pesados de origen colombiano y ecuatoriano; a partir de los espectros infrarrojos obtenidos por reflectancia total atenuada (FTIR-ATR), empleando el modelo lineal PLS (Partial Linear Squart) como modelo de referencia ya que es ampliamente usado en la literatura para los sistemas de crudos; y como modelos no líneales se usó SVM (Support Vector Machine) y GBR (Gradient Boosting Regressor). El objetivo principal de esta investigación es desarrollar técnicas alternas a las fracciones saturadas, aromáticas, resinas y asfáltenos (SARA), gravedad API, residuo de carbón micro (MCR) y número de acidez total (TAN) que permitan realizar análisis rápidos, amigables con el medio ambiente, no destructivos, eliminando el uso de reactivos y la cantidad de muestra. El enfoque computacional y de aprendizaje automatizado va en camino de aumentar la eficiencia de los procesos y tiempos de obtención de resultados fiables solo a partir de los espectros FTIR-ATR. La metodología empleada en esta investigación demostró que los modelos no lineales se ajustan mejor que el modelo lineal propuesto en las propiedades de interés; obteniendo como coeficientes de correlación de 0.98, 0.95 y 0.95 para las propiedades API, TAN y MCR, respectivamente. Por otro lado, se obtuvieron coeficiente de correlación de 0.89, 0.36, 0.61 y 0.82 para las fracciones SARA, respectivamente. Como resultados del análisis de estos modelos, es evidente que el modelo de regresión GBR se ajustó mejor en la predicción de las propiedades ya que logró predecir mejor, cinco de las siete propiedades. Support vector machine ; Boosting (machine learning) ; Context (archaeology) ; Gradient boosting ; Principal component analysis Petroleum Processing and Analysis ; Spectroscopy and Chemometric Analyses ; Calcium Carbonate Crystallization and Inhibition
Authors: Long-Shu Tang | Shunsuke Muto
Source: Materia Japan (article, 2025-12-31)
Field: Materials Science — Materials Chemistry

Development of a Machine Learning-assisted Operando Measurement System for Catalytic Reactions using the Reaction Science High-voltage Electron Microscope Catalysis ; Electron microscope ; Scanning electron microscope ; Microscope ; System of measurement ; Transmission electron microscopy Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Electron and X-Ray Spectroscopy Techniques
Authors: Divyansh Adha (22801849) | Avinash Anand (6709646) | Arjita Roy (22801852) | Vijayanand Suryakant Moholkar (8859983)
Source: Figshare (article, 2025-12-08)
Field: Materials Science — Materials Chemistry

Valorizationof Polycarbonate Waste by Ultrasound-AssistedMethanolysis: Optimization Using an Artificial Neural Network andKinetic Investigation This study presents an integrated approach combining experimental optimization, machine learning (using ANN-GA), and kinetic modeling to enhance ultrasound-assisted methanolysis of polycarbonate (PC) waste. Central composite design (CCD) under response surface methodology (RSM) was employed to optimize methanolysis reaction parameters of solvent ratio, catalyst loading, temperature, reaction time, and lagging time. The optimized process achieved 98.56% PC conversion under 0.138 g of the KOH catalyst, 3.63:1 THF: methanol ratio, 47.7 °C, 26.8 min reaction time, and 7 min lagging time. Product characterization confirmed the formation of high-purity bisphenol A (BPA) and dimethyl carbonate (DMC). To enhance predictive capability, an artificial neural network optimized with genetic algorithm (ANN-GA) was developed. The model with 12 hidden neurons improved the predictions of RSM-CCD with 99.95% PC conversion and identified the solvent ratio and catalyst dosage as the most influential variables through sensitivity analysis. A kinetic model based on four reversible reaction steps was solved using the 4<sup>th</sup>-order Runge–Kutta method with GA-optimized constants, showing strong agreement with experimental data (<i>R</i><sup>2</sup> > 0.94). The kinetic constants provided mechanistic insight into the role of ultrasound, which enhanced depolymerization by improving mass transfer and reducing activation barriers. This work demonstrates an efficient, sustainable pathway for polycarbonate waste valorization and green synthesis of BPA and DMC. Polycarbonate ; Methanol ; Catalysis ; Artificial neural network ; Depolymerization ; Mass transfer ; Batch reactor ; Dimethyl carbonate Ultrasound and Cavitation Phenomena ; Catalysis for Biomass Conversion ; Polymer Foaming and Composites
Authors: Kausik Roy
Source: ChemRxiv (article, 2025-12-17)
Field: Chemistry — Organic Chemistry

Nickel Outperforms Palladium in Suzuki-Miyaura Cross-Coupling: A Machine Learning Analysis of Five Metal Catalysts Benchmarking ; Catalysis ; Artificial neural network ; Palladium ; Feature (linguistics) ; Chemical reaction engineering Catalytic Cross-Coupling Reactions ; Machine Learning in Materials Science ; Catalytic C–H Functionalization Methods
Authors: Daniel Willimetz | Lukáš Grajciar
Source: arXiv (Cornell University) (preprint, 2025-08-20)
Field: Materials Science — Materials Chemistry

A Simple and Scalable Kernel Density Approach for Reliable Uncertainty Quantification in Atomistic Machine Learning Machine learning models are increasingly used to predict material properties and accelerate atomistic simulations, but the reliability of their predictions depends on the representativeness of the training data. We present a scalable, GPU-accelerated uncertainty quantification framework based on $k$-nearest-neighbor kernel density estimation (KDE) in a PCA-reduced descriptor space. This method efficiently detects sparsely sampled regions in large, high-dimensional datasets and provides a transferable, model-agnostic uncertainty metric without requiring retraining costly model ensembles. The framework is validated across diverse case studies varying in: i) chemistry, ii) prediction models (including foundational neural network), iii) descriptors used for KDE estimation, and iv) properties whose uncertainty is sought. In all cases, the KDE-based score reliably flags extrapolative configurations, correlates well with conventional ensemble-based uncertainties, and highlights regions of reduced prediction trustworthiness. The approach offers a practical route for improving the interpretability, robustness, and deployment readiness of ML models in materials science. Uncertainty quantification ; Metric (unit) ; Reliability (semiconductor) ; Kernel density estimation ; Kernel (algebra) ; Probabilistic logic ; Scalability ; Representativeness heuristic ; Artificial neural network ; Density estimation Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Ion-surface interactions and analysis
Authors: Beatriz Mouriño | Sauradeep Majumdar | Xin Jin | Fergus Mcilwaine | Joren Van Herck | Andres Ortega‐Guerrero | Susana Garcia | Berend Smit
Source: ChemRxiv (preprint, 2025-01-29)
Field: Chemistry — Inorganic Chemistry

Exploring the Chemical Design Space of Metal-Organic Frameworks for Photocatalysis In this work, we introduce a combined DFT and machine learning approach to obtain insights into the chemical design of metal-organic framework (MOF) photocatalysts for hydrogen (HER) and oxygen (OER) evolution reactions. To train our machine learning models, we evaluated a dataset of 314 MOFs using a dedicated DFT workflow that computes a set of five descriptors for both closed and open shell MOFs. Our dataset is composed of a diverse selection of the QMOF database and experimentally reported MOF photocatalysts. In addition, to ensure a balanced dataset, we designed a set of MOFs (CDP–MOF) inspired by insights obtained regarding different types of photocatalytic materials. Our machine-learning approach allowed us to screen the entire QMOF and CDP–MOF databases for promising candidates. Our analysis of the chemical design space shows that we have many materials with a suitable spatial overlap of electron and hole, band gap, band-edge alignment to HER, and charge-carrier effective masses. However, we have identified in the QMOF database only a very small percentage of materials that also have the right band edge alignment to OER. With the CDP–MOF database, we successfully targeted building blocks that potentially have the correct OER band alignment, and indeed obtained a larger percentage of materials that obey this criteria. Among those, a few motifs stood out, such as Au-pyrazolate, Ti clusters and rod-shaped metal nodes, and a particular MOF designed with the Mn4Ca cluster, which mimics the OER center in the photosystem II of photosynthesis. Photocatalysis ; Space (punctuation) ; Metal-organic framework ; Chemical space ; Metal ; Materials science ; Environmental chemistry ; Nanotechnology ; Environmental science ; Chemistry ; Computer science ; Organic chemistry ; Catalysis ; Adsorption Metal-Organic Frameworks: Synthesis and Applications ; Polyoxometalates: Synthesis and Applications ; Pigment Synthesis and Properties
Authors: Yunfei Wang | Daniel Struble | Simant R. Upreti | Zongliang Xie | Ka Hung Chan | Yi Liu | Chenhui Zhu | Paul D. Ashby | Wenjie Xia | Derek L. Patton | Boran Ma | Xiaodan Gu
Source: ChemRxiv (article, 2025-11-17)
Field: Materials Science — Materials Chemistry

Automated and High-Throughput Phase Separation Control for Supramolecular Polymer Blends Enabled by Machine Learning Modular design ; Polymer ; Workflow ; Polymer blend ; Supramolecular chemistry ; Support vector machine ; Phase (matter) ; Coupling (piping) Block Copolymer Self-Assembly ; Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization
Authors: Marco Diani | Marco Papetti | Marcello Colledani
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Black Plastics Identification and Classification Via Middle-Wave Infrared Hyperspectral Imaging Integrated with Machine Learning Hyperspectral imaging ; Sorting ; Dimensionality reduction ; Pattern recognition (psychology) ; Linear discriminant analysis ; Identification (biology) ; Random forest ; Artificial neural network ; Infrared Spectroscopy and Chemometric Analyses ; Microplastics and Plastic Pollution ; Machine Learning in Materials Science
Authors: Stephanos Karafiludis | Jakob Standl | Tom William Ryll | A.J. Schwab | Carsten Prinz | Jakob B. Wolf | Sabine Kruschwitz | Franziska Emmerling | Christoph Völker | Tomasz M. Stawski
Source: ChemRxiv (preprint, 2025-06-16)
Field: Chemistry — Inorganic Chemistry

High-Entropy Phosphate Synthesis: Advancements through Automation and Sequential Learning Optimization Transition metal phosphates (TMPs) are extensively explored for electrochemical and catalytical applications due to their structural versatility and chemical stability. Within this material class, novel high-entropy metal phosphates (HEMPs)—containing multiple transition metals combined into a single-phase structure—are particularly promising, as their compositional complexity can significantly enhance functional properties. However, the discovery of suitable HEMP compositions is hindered by the vast compositional design space and complex or very specific synthesis conditions. Here, we present a data-driven strategy combining automated wet-chemical synthesis with a Sequential Learning App for Materials Discovery (SLAMD) framework (Random Forest regression model) to efficiently explore and optimize HEMP compositions. Using a limited set of initial experiments, we identified near-equimolar multi-metal compositions in a single-phase crystalline solid. The model successfully predicted a novel Co0.3Ni0.3Fe0.2Cd0.1Mn0.1 phosphate octahydrate phase, validated experimentally, demonstrating the effectiveness of the machine learning approach. This work highlights the potential of integrating automated synthesis platforms with data-driven algorithms to accelerate the discovery of high-entropy materials, offering an efficient designing pathway to advanced functional materials. Phosphate ; Automation ; Entropy (arrow of time) ; Computer science ; Nanotechnology ; Chemistry ; Artificial intelligence ; Engineering ; Materials science ; Biochemistry ; Physics ; Thermodynamics ; Mechanical engineering Inorganic Chemistry and Materials ; Chemical Synthesis and Characterization ; Machine Learning in Materials Science