jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ricardo Urrego-Ortiz | Stefan T. Bromley | Federico Calle-Vallejo
Source: Materials Today Energy (article, 2025-12-27)
Field: Materials Science — Materials Chemistry

Acetylene-linked triaryl 2D covalent organic frameworks as electrocatalysts for hydrogen evolution Light main-group elements are so abundant in the Earth’s crust that materials based on them are generally cost-effective and suitable for mass production. A catalyst made of these elements for the hydrogen evolution reaction (HER) may help in triggering the hydrogen economy. In this context, metal-free 2D covalent organic frameworks (COFs) are promising for electrocatalytic applications in view of their remarkable structural, electronic and compositional versatility. Here, we use density functional theory calculations to build phase diagrams, activity plots and fit machine learning models to assess the HER performance of a class of 2D COFs based on acetylene-linked triaryl centers. We have screened over 40 2D COFs with a range of nodal centers and aryl ring substituents. We find that the HER activity of our 2D COFs can be described in terms of the oxidation state of the substituents on the aryl rings and the ionization energy or molecular weight of the most stable isotope of the triaryl nodal centers. Indeed, we find that materials with B and C nodal centers are more active for the HER than those containing CH and N centers. In turn, varying the aryl ring substituents we predict that the -BH 2 substituent could lead to 2D-COFs with remarkably high HER activities. • We study 2D COFs built from triaryl units and diacetylenic linkers for the HER • The triaryl units can have different nodal centers and peripheral substituents • The degree of hydrogenation of the 2D COFs under HER conditions is ascertained • HER activity depends on substituent oxidation state and center ionization energy • The most active 2D COFs for the HER have B centers and -BH 2 substituents Substituent ; Aryl ; Covalent bond ; Ring (chemistry) ; Density functional theory ; Catalysis ; Oxidation state ; Ionization energy Covalent Organic Framework Applications ; Electrocatalysts for Energy Conversion ; CO2 Reduction Techniques and Catalysts
Authors: Meng-Qi Cheng | Weidong Luo | Hongbin Sun
Source: arXiv (Cornell University) (preprint, 2025-07-13)
Field: Materials Science — Materials Chemistry

On-the-fly machine learning-augmented constrained AIMD to design new routes from glassy carbon to quenchable amorphous diamond with low pressure and temperature Recent advances in machine learning have enabled large-scale atomic simulations with first-principles accuracy, allowing precise modeling of disordered materials such as glassy carbon (GC). However, conventional ab initio molecular dynamics (AIMD) cannot effectively capture anisotropic stress effects, which are believed to play a key role in the transformation of GC into amorphous diamond under extreme conditions. In this work, we present an on-the-fly machine learning-augmented constrained AIMD (ML-augmented CAIMD) approach by modifying VASP 6.3.2. Our simulations not only reproduce major experimental features of GC but also provide restrictive synthesis conditions and microscopic insights. We show that GC exhibits unexpectedly high plasticity, with its compressive and shear strengths enhanced by large strains. Under pressure, increasing annealing temperature promotes the formation of quenchable amorphous diamond via enhanced sp3 preservation, but this trend reverses above 2900 K due to thermal graphitization. Under non-hydrostatic compression, GC transforms into a superhard structure sustaining large stress differences, which sharply increase when confining pressure exceeds 40 GPa. Finally, severe rotational shear at 30 GPa induces sp3 fractions up to 80 percent at 300 to 1000 K. A hardened amorphous carbon retaining 64 percent sp3 content is achieved by decompression at 300 K, marking the lowest pressure-temperature route ever predicted. Our ML-augmented CAIMD provides a general framework for modeling structural transformations in disordered materials under anisotropic stresses. Amorphous solid ; Amorphous carbon ; Diamond ; Anisotropy ; Shear (geology) ; Carbon fibers ; Annealing (glass) ; Molecular dynamics Diamond and Carbon-based Materials Research ; Adhesion, Friction, and Surface Interactions
Authors: Ryan J. Miller (4220986) | Alexander E. Dashuta (22477148) | Brayden Rudisill (22477151) | David Van Vranken (11939183) | Pierre Baldi (57573)
Source: Figshare (article, 2025-10-22)
Field: Materials Science — Materials Chemistry

Mechanism-Aware DeepLearning for Polar Reaction Prediction Accurately predicting chemical reactions is essential for driving innovation in synthetic chemistry, with broad applications in medicine, manufacturing, and agriculture. Yet reaction prediction remains a complex problem that is both time-consuming and resource-intensive for chemists to solve. Deep learning offers an appealing solution by enabling high-throughput prediction, but most existing models are trained on the US Patent Office data set and treat reactions as recipes or overall transformationsmapping reactants directly to products with limited mechanistic insight. To address this, we introduce PMechRP (Polar Mechanistic Reaction Predictor), trained on the PMechDB data set of polar elementary steps that capture electron flow and mechanistic detail. To broaden coverage and improve generalization, we augment PMechDB with combinatorially generated reactions and train models spanning transformer, graph, and two-stage Siamese architectures. In addition to reaction prediction models, we also develop ArrowFinder, a new model that directly predicts arrow-pushing mechanisms for a set of reactants and products. Our best-performing approach is a hybrid pipeline that combines an ensemble of Chemformer models with a two-stage Siamese framework, leveraging the accuracy of transformers while filtering away “alchemical” products using the two-step network and generating mechanistic annotations using ArrowFinder. This approach achieves strong predictive accuracy while also providing interpretable predictions. We evaluate performance across multiple benchmarks: PMechDB test splits, a curated USPTO subset from the Open Reaction Database, and a human benchmark of mechanistic pathways from an intermediate-level textbook. Deep learning ; Benchmark (surveying) ; Pipeline (software) ; Set (abstract data type) ; Training set ; Test set ; Data set ; Labeled data Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Debarati Dey | SEVDA RZAYEVA | Sevinj Guluzade | Vusala Nabi Jafarova
Source: TURKISH JOURNAL OF PHYSICS (article, 2025-12-19)
Field: Materials Science — Materials Chemistry

First-principles and deep learning frameworks to predict the electronic, magnetic and electrical properties of V-doped SiC nanotube Nowadays, with the miniaturization of nanoindustry, we are increasingly interested in doping effects for nanotube systems. In this study, based on density functional theory (DFT) and local spin density approximation (LDA) methods within Hubbard U corrections, the electronic, magnetic and electrical properties of single-wall silicon carbide nanotubes doped with vanadium were theoretically studied. Although the undoped SiC system is nonmagnetic, the V-doped SiC nanotube induces magnetism and the total magnetic moment of this magnetic material is equal to ~1 µB. Density of states calculations indicated that the magnetization of SiC:V single-wall nanotubes mainly comes from the 2p orbitals of carbon atoms and 3d orbitals of the V dopant. Total energy calculations of the ferromagnetic and antiferromagnetic phases of V-doped SiCNT systems revealed that the ferromagnetic phase was more stable.In this study, based on density functional theory (DFT) and local spin density approximation (LDA) methods within Hubbard U corrections, the electronic and magnetic properties of single wall silicon carbide nanotubes doped with vanadium were theoretically studied. These properties were simulated for cases in which single or double silicon atoms of the SiC nanotube were replaced with V atoms. Using deep learning (DL) algorithms is beneficial for predicting quantum-confined electronic structures; however, first-principles simulation methods are more accurate. The ML-based regression model shows the accuracy and prediction model for quantum-confined nanotubes. Among the various neural network algorithms, trilayered and medium neural network algorithms provide more accuracy and a lower error rate for these molecular nanotubes. A comparison between the ML-based approach and the DFT-based procedure reveals the similarity and accuracy of the proposed algorithm. We have used Quantum ATK version 20.0 for the DFT simulation whereas MATLAB is used to predict the best fitted model. Nanotube ; Density functional theory ; Antiferromagnetism ; Ferromagnetism ; Magnetism ; Spintronics ; Magnetic moment ; Magnetization ; Carbon nanotube Boron and Carbon Nanomaterials Research ; Machine Learning in Materials Science ; Carbon Nanotubes in Composites
Authors: Mithun S. Ullal | Virgil Popescu | RAMONA BIRAU | Costel Ionașcu | Genu Alexandru Cãruntu | DUMITRU DOREL D. CHIRIȚESCU | Stefan Margaritescu
Source: Industria Textila (article, 2025-12-22)
Field: Materials Science — Polymers and Plastics

AI-enabled robotic sorting for circular textile waste management:A scalable solution for India’s recycling sector The global textile industry faces a critical inflexion point as circular economy mandates intensify and waste volumes soar beyond 100 million tonnes annually. Central to realising circularity is the efficiency and fidelity of textile waste sorting, a longstanding bottleneck dominated by manual, low-throughput, and error-prone methods. This paper investigates the deployment of an AI-enabled robotic sorting system integrating hyperspectral imaging (HSI) and deep learning algorithms within the context of India’s fragmented textile recycling ecosystem. We demonstrate that spectral imaging combined with convolutional neural networks (CNNs) achieves over 95% classification accuracy across heterogeneous, post-consumer Indian textile waste streams, including multi-fibre blends that typically confound manual sorters. Drawing from industrial benchmarks such as Sweden’s SipTex and U.S.-based Refiberd, we design a prototype that integrates conveyor automation, real-time classification, and robotic actuation. Comparative analysis reveals that the AI system achieves throughput rates exceeding 1,000 garments per hour, representing a 20× gain over manual processes while reducing misclassification rates by more than 60%. A techno-economic model suggests payback periods under four years when scaled to medium-sized facilities, with significant reductions in labour dependency and waste-to-landfill ratios. Our findings have strong implications for policy and industry: AI sorting systems not only align with India’s National Textile Policy and MITRA initiatives but also represent an enabling infrastructure for chemical recycling, extended producer responsibility, and traceable material flows. By bridging technological innovation with operational scalability, this study advances the industrial feasibility of circular textiles in the Global South. Keywords: AI sorting, hyperspectral imaging, textile recycling, circular economy, robotic automation, India Bottleneck ; Hyperspectral imaging ; Circular economy ; Context (archaeology) ; Convolutional neural network ; Textile ; Scalability ; Textile industry ; Sorting ; Robot Textile materials and evaluations ; Dyeing and Modifying Textile Fibers ; Industrial Vision Systems and Defect Detection
Authors: D.A. Lgotina | R.A. Sukhachev | A.A. Chubarova | D.A. Sergeicheva | L.N. Stepanova | Yu.G. Malinovsky | P.V. Prudnikov | A.V. Lavrenov
Source: Теоретические основы химической технологии / Theoretical Foundations of Chemical Engineering (article, 2025-01-01)
Field: Chemistry — Organic Chemistry

Methods for Modeling the Process of High-Temperature Conversion of Natural Gas This review article describes modern methods for modeling the kinetics of high-temperature oxygen-free conversion of methane to acetylene. Published studies on both fundamental aspects of the reaction mechanism and applied methods of computer modeling are analyzed. Particular attention is paid to modern computational methods, such as numerical methods for solving systems of differential equations, quantum chemical methods, and machine learning methods. The influence of key process parameters on the yield of the target product and the selectivity of the reaction is considered. It is shown that the combination of traditional methods of chemical kinetics with modern computational technologies offers opportunities for optimizing laboratory and, in the future, industrial processes for obtaining acetylene from natural gas. Process (computing) ; Chemical process ; Yield (engineering) ; Methane ; Natural gas ; Acetylene ; Mathematical model ; Chemical industry ; Process simulation Synthesis and Characterization of Pyrroles ; Catalysis and Oxidation Reactions ; Process Optimization and Integration
Authors: Eric Warnemunde Vertina | Emily Sutherland | Sajal Chakroborty | N. Aaron Deskins | Oren Mangoubi
Source:  (article, 2025-09-07)
Field: Materials Science — Materials Chemistry

Machine-Learning-Enabled High-Throughput MXene Property Prediction MXenes, an important class of 2-D solid materials, are challenging to synthesize in the laboratory, and numerical models for simulating MXene properties, such as density functional theory (DFT), remain computationally expensive. We develop a pipeline that performs high-throughput MXene property predictions with greater accuracy than machine learning methods relying on only easily obtainable standard atomic features and with computationally cheaper inputs than current deep learning methods that require inputs derived from DFT computations. Our pipeline consists of three steps. The first step is a novel rules-based algorithm that generates a large set of unique MXene crystal graph structures as.cif files. The second step includes trained Graph Neural Network (GNN) models that take as input these generated MXene.cif files and outputs predicted target properties for the each MXene. Our GNN models achieve the following test set mean absolute errors and percent errors for models trained ten times on DFT-generated target properties training data, using different random seeds for each train/test splitting: band gap 0.062±0.031 (eV) and 4.5±1.9% median percent error, bulk modulus 22.0±2.1 (N/m) and 16.7±3.6% mean percent error, d-band center 0.103±0.18 (eV) and 8.2±2.7% mean percent error, density of states (at Fermi level) 0.78±0.12 and 10.8±2.7% mean percent error, heat of formation 0.052±0.007 (eV/atom) and 3.1±0.9% median percent error, binding energy 0.295±0.048 (eV) and 7.6±1.5% mean percent error, and work function 0.196±0.018 (eV) and 4.4±0.8% mean percent error. For classification target properties, we obtain the following accuracy and F1 scores: dynamically stable (T/F) 83.8% accuracy and 0.877 F1 score, magnetic (T/F) 94.3% accuracy and 0.859 F1 scores. Finally, we predict these target properties for more than 13 million unique MXenes using with the best performing model for each target property, performing property predictions orders of magnitude faster than DFT methods and providing MXenes researchers additional insight into the vast MXenes property space. To our knowledge, this is the largest publicly available MXene properties dataset to date. Pipeline (software) ; Artificial neural network ; Graph ; Test set ; Mean field theory ; Cluster analysis ; Property (philosophy) ; Mean-shift MXene and MAX Phase Materials ; Machine Learning in Materials Science ; 2D Materials and Applications
Authors: Daixin Liu | Xun Chen | Meili Ren | Yongxiang Ren | Mulusew W. Yaltaye | Jian‐An Huang | Rong Tian | Liyuan Liang
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Single-Molecule Resolution of Oligopeptides in Anti-Aging Cosmetics Combined with Nanopore Readouts and Deep Learning Model Cosmetics ; Nanopore ; Oligopeptide ; Nanotechnology ; Resolution (logic) ; Materials science ; Biophysics ; Chemistry ; Computer science ; Artificial intelligence ; Biology ; Biochemistry ; Organic chemistry ; Peptide Machine Learning in Materials Science ; Advanced biosensing and bioanalysis techniques ; Nanopore and Nanochannel Transport Studies
Authors: Nikhil Singh (9684160) | Dibyajyoti Ghosh (1664176)
Source: Figshare (article, 2025-08-26)
Field: Materials Science — Materials Chemistry

Hot CarrierRelaxation in Halide Double Perovskites:Insights from <i>Ab Initio</i> Models and Structure-InformedMachine Learning The hot carrier (HC) cooling at an ultrafast timescale represents a critical limitation in achieving high-efficiency photon-to-electron conversion. The comprehensive materials screening seeks to identify candidates that inherently suppress hot carrier relaxation, thereby extending carrier lifetimes. Here, we combine time-dependent density functional theory and nonadiabatic molecular dynamics to investigate the hot electron (HE) dynamics in experimentally synthesized Cs<sub>2</sub>AgInX<sub>6</sub> (X = Br and Cl). These halide double perovskites exhibit significantly extended HE lifetimes compared to conventional metal halide perovskites, primarily due to the presence of discrete electronic states at the conduction band edge and weakened. The well-trained machine learning models, including artificial neural networks (ANN) and mutual information (MI), efficiently capture the complex relationships among composition, structural dynamics, and excited-state properties while drastically reducing computational expense. The ANN models that are trained using only 10% of data enable accurate interpolation of dynamic electronic properties such as energy gaps near the conduction band edge and nonadiabatic coupling. The high accuracies of predicted properties ensure the ability of these ANN models to capture transient changes in electronic structure over extended timescales. The ANN-generated interpolated data enhance the reliability of MI analyses that uncover subtle, time-resolved correlations between structural dynamics and HE relaxation. These insights pin down the strategic design approaches to realize stable materials for HE-based optoelectronic applications. Interpolation (computer graphics) ; Halide ; Relaxation (psychology) ; Transient (computer programming) ; Artificial neural network ; Electronic structure ; Density functional theory ; Ultrashort pulse ; Enhanced Data Rates for GSM Evolution Machine Learning in Materials Science ; Perovskite Materials and Applications ; Heusler alloys: electronic and magnetic properties
Authors: Elohan Veillon | Astrid Klipfel | Adlane Sayede | Zied Bouraoui
Source: ArXiv.org (article, 2025-12-31)
Field: Materials Science — Materials Chemistry

A Chemically Grounded Evaluation Framework for Generative Models in Materials Discovery Generative models hold great promise for accelerating materials discovery, but their evaluation often overlooks the chemical validity and stability requirements crucial to real-world applications. Density Functional Theory (DFT) simulations are the gold standard for evaluating such properties but are computationally intensive and inaccessible to non-experts. We propose a chemically grounded, user-friendly evaluation framework that integrates DFT-based stability analysis with commonly used machine learning (ML) metrics. Through systematic experiments using both perturbative and generative methods, we demonstrate that conventional ML metrics can misrepresent chemical feasibility. To address this, we propose new insights on robust metrics and highlight the importance of simulation-informed evaluation for developing reliable generative models in materials science. Generative grammar ; Stability (learning theory) ; Generative model ; Generative Design ; Basis (linear algebra) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Elohan Veillon | Astrid Klipfel | Adlane Sayede | Zied Bouraoui
Source: SPIRE - Sciences Po Institutional REpository (preprint, 2025-12-31)
Field: Materials Science — Materials Chemistry

A Chemically Grounded Evaluation Framework for Generative Models in Materials Discovery Generative models hold great promise for accelerating materials discovery, but their evaluation often overlooks the chemical validity and stability requirements crucial to real-world applications. Density Functional Theory (DFT) simulations are the gold standard for evaluating such properties but are computationally intensive and inaccessible to non-experts. We propose a chemically grounded, user-friendly evaluation framework that integrates DFT-based stability analysis with commonly used machine learning (ML) metrics. Through systematic experiments using both perturbative and generative methods, we demonstrate that conventional ML metrics can misrepresent chemical feasibility. To address this, we propose new insights on robust metrics and highlight the importance of simulation-informed evaluation for developing reliable generative models in materials science. Generative grammar ; Stability (learning theory) ; Generative model ; Generative Design ; Basis (linear algebra) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Yixiang Li | Yujun Liang | Xiang He | Yu‐ting Yin | Xiaobo Sun | Chun Li | Yang Baomin | Jinping Wang
Source: Microchemical Journal (article, 2025-12-06)
Field: Materials Science — Materials Chemistry

Fluorescence-based sensor array using nitrogen-doped carbon dots: Machine learning-assisted discrimination of dihydroxybenzene isomers and their ternary blends Ternary operation ; Sensor array ; Artificial neural network ; Principal component analysis ; Electronic tongue ; Binary number ; Fluorescence ; Binary classification ; Carbon fibers Carbon and Quantum Dots Applications ; Electrochemical sensors and biosensors ; Water Quality Monitoring and Analysis
Authors: Md Rahatul Islam Udoy | Jack Hutchins | Shamiul Alam | Catherine D. Schuman | Ahmedullah Aziz
Source: IEEE Journal on Exploratory Solid-State Computational Devices and Circuits (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Integrating Atomistic Insights With Circuit Simulations via Transformer-Driven Symbolic Regression This paper introduces a framework that establishes a cohesive link between first principles-based simulations and circuit-level analyses using a machine learning-based compact modeling platform. Starting with atomistic simulations, the framework examines the microscopic details of material behavior, forming the foundation for later stages. The generated datasets, with molecular insights, are processed using machine learning algorithms to identify complex patterns and relationships. As these machine-learning models develop, they become tools for predicting behaviors beyond the reach of conventional modeling and simulation methods. Applied to circuit simulation, the framework improves understanding of electrical interactions, enhancing accuracy and speeding up design automation. As a proof of concept, we perform first principles-based simulations of the Graphene Nanoribbon Field Effect Transistor (GNRFET), an exploratory device, and create a symbolic-regression-based machine learning model that can readily be integrated into advanced circuit simulation. This framework presents a template offering a unified approach that synergizes the strengths of first principles-based simulations and circuit-level design tools. Field (mathematics) ; Circuit design ; Transistor ; Integrated circuit ; Foundation (evidence) ; Electronic circuit ; Complex system ; Proof of concept Machine Learning in Materials Science ; Protein Structure and Dynamics ; Fuel Cells and Related Materials