jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Cesar Raul Garcia-Jacas | Harrison Green | Shayne D. Wierbowski | Jacob D. Durrant
Source: Digital Discovery (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Precision fragment addition: domain-specific DeepFrag2 models for smarter lead optimization This study introduces a series of machine-learning models based on DeepFrag, our previously published tool designed to guide small-molecule lead optimization through fragment addition. We demonstrate enhanced accuracy by training new DeepFrag models to predict optimizing fragments with specific sizes and chemical properties. Additionally, we show that DeepFrag accuracy improves when fine-tuned on specific receptor classes. These targeted models should prove valuable for medicinal chemists with predetermined insights into suitable molecular fragment characteristics (such as preferred size ranges, charge states, or aromaticity) or those conducting optimization campaigns against specific drug-target classes with many known ligands. To encourage adoption, we release DeepFrag2 under the open-source MIT license. Interested users can download DeepFrag2 free of charge without registration from https://durrantlab.com/deepfrag2/. Fragment (logic) ; Lead (geology) ; Charge (physics) ; Series (stratigraphy) ; Optimization algorithm ; Range (aeronautics) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: G. H. Majzoobi | Samaneh Pourolajal
Source: Scientific Reports (article, 2026-02-16)
Field: Materials Science — Materials Chemistry

Determination of the parameters of a material constitutive relation using the surrogate model along with dynamic indentation test It is well-known that materials behavior changes with strain rate and temperature. The changes are described by an equation known as material model. The model involves a number of constants which are normally determined by experiment. In this study, the constants of Zerilli-Armstrong model are identified using dynamic indentation test combined with numerical simulation and an optimization technique. The specimens made of a steel alloy are subjected to indentation tests at four different strain rates and four temperatures and the experimental load-depth curve is recorded. The dynamic indentation test is simulated using Ls-dyna hydro code and the numerical load-depth is obtained. Attempts are made to optimize the error between the experimental and the numerical load-depth curves. This is accomplished using Surrogate model. The results are validated using the stress-strain curves obtained from Hopkinson bar tests. The study shows that the method yields acceptable results. The results obtained using artificial neural network and optimization technique, based on a quadratic polynomial, which have been reported in the previous works for Johnson-Cook model are reproduced here for Zerilli-Armstrong model for comparison purposes. Indentation ; Split-Hopkinson pressure bar ; Constitutive equation ; Surrogate model ; Strain rate ; Quadratic equation ; Dynamic testing ; Artificial neural network High-Velocity Impact and Material Behavior ; Metallurgy and Material Forming ; Nonlocal and gradient elasticity in micro/nano structures
Authors: Dhinakaran Veeman | Pradeep Castro P | Muthumari M | Poornima Hulipalled | Veerabhadrappa Algur
Source: Multiscale and Multidisciplinary Modeling Experiments and Design (article, 2026-03-27)
Field: Materials Science — Materials Chemistry

Machine learning as a reliable tool for compressive strength prediction in pa6-cf Machine learning plays a crucial role in recent years in manufacturing systems. Beginning with optimisation to predicting machine failures, machine learning has been widely exploited by the manufacturing sector. However, determining the mechanical properties of fabricated parts is crucial for manufacturing sectors to apply the material in the right areas. Determining the mechanical properties of materials includes costly laboratory setups and a time-consuming process. Hence, there is always a need to look for alternative methods to determine the mechanical properties. In this investigation, Machine learning models, including Linear regression, Decision Tree, Support Vector Regression, AdaBoost, XGBoost and CatBoost, were analysed to predict the compressive strength of additively fabricated specimens. The fused filament fabrication technique has been selected as the additive manufacturing method for fabricating the test samples. Carbon fibre-reinforced nylon filament, widely used for industrial applications, is selected as the raw material to fabricate the specimens. Compression specimens were prepared as per ASTM standards, and an experimental investigation was carried out to obtain the experimental values. The obtained values were split into training and testing data and were used with machine learning models. The models were evaluated based on MSE and R-squared evaluation metrics, and the best-fit model was determined. Optimisation techniques, including Optuna and skOpt, were included for hyperparameter tuning. Results proved that ML models are effective in predicting compressive strength with minimal variations. CatBoost and XGBoost were the most suitable models for predicting the compressive strength of fused-fabricated carbon fibre-reinforced nylon specimens. Compressive strength ; Hyperparameter ; Fused filament fabrication ; Compression (physics) ; Support vector machine ; Machine tool Machine Learning in Materials Science ; Additive Manufacturing and 3D Printing Technologies ; Mechanical Behavior of Composites
Authors: Pandurang Abasaheb Ithape | Sheetal Bhausaheb Rasal
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2026-04-30)
Field: Materials Science — Materials Chemistry

AI-Driven Analysis of Thin Films for Solar Cells and Sensor Applications Thin-film materials are fundamental to modern photovoltaic and sensing technologies due to their unique physical characteristics, efficient material utilization, and adaptability to flexible and lightweight substrates. This study provides a comprehensive analysis of the structural, optical, electrical, and mechanical properties of thin films and examines their influence on the performance of solar cells and sensor devices. In contrast to bulk materials, thin films exhibit enhanced surface-related phenomena, tunable properties, and stronger interaction with surrounding environments, resulting in improved efficiency and sensitivity. A significant challenge in thin-film research lies in understanding the complex interdependence between fabrication conditions, material composition, and device performance. Traditional experimental approaches are often time-intensive and costly. To overcome these limitations, this work incorporates Artificial Intelligence (AI) techniques— including machine learning and neural network models—to analyze material behavior, predict outcomes, and optimize fabrication processes. The study further explores the application of thin films in solar energy conversion and sensing technologies. AI-driven approaches improve prediction accuracy, reduce experimental efforts, and enhance overall device performance. The integration of AI with thin-film technology presents a promising pathway toward the development of efficient, intelligent, and sustainable systems. Fabrication ; Thin film ; Photovoltaic system ; Adaptability ; Thin film solar cell ; Artificial neural network ; Energy harvesting ; Solar energy Machine Learning in Materials Science ; Advanced Sensor and Energy Harvesting Materials ; Thin-Film Transistor Technologies
Authors: Pengju Zhang | Hao Pan | Chen Chen | Yiming Jing | Ding Liu
Source: Crystals (article, 2026-01-13)
Field: Materials Science — Materials Chemistry

CNN–BiLSTM–Attention-Based Hybrid-Driven Modeling for Diameter Prediction of Czochralski Silicon Single Crystals High-precision prediction of the crystal diameter during the growth of electronic-grade silicon single crystals is a critical step for the fabrication of high-quality single crystals. However, the process features high-temperature operation, strong nonlinearities, significant time-delay dynamics, and external disturbances, which limit the accuracy of conventional mechanism-based models. In this study, mechanism-based models denote physics-informed heat-transfer and geometric models that relate heater power and pulling rate to diameter evolution. To address this challenge, this paper proposes a hybrid deep learning model combining a convolutional neural network (CNN), a bidirectional long short-term memory network (BiLSTM), and self-attention to improve diameter prediction during the shoulder-formation and constant-diameter stages. The proposed model leverages the CNN to extract localized spatial features from multi-source sensor data, employs the BiLSTM to capture temporal dependencies inherent to the crystal growth process, and utilizes the self-attention mechanism to dynamically highlight critical feature information, thereby substantially enhancing the model’s capacity to represent complex industrial operating conditions. Experiments on operational production data collected from an industrial Czochralski (Cz) furnace, model TDR-180, demonstrate improved prediction accuracy and robustness over mechanism-based and single data-driven baselines, supporting practical process control and production optimization. Robustness (evolution) ; Silicon ; Process (computing) ; Fabrication ; Artificial neural network ; Feature (linguistics) ; Convolutional neural network ; Process control Machine Learning in Materials Science ; Solidification and crystal growth phenomena ; Silicon and Solar Cell Technologies
Authors: Peng Liu | Donghai Mei
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Chemistry — Inorganic Chemistry

Energy-entropy compensation: What is missing in theoretical quantifications of hydrogenation and dehydrogenation catalyzed by zeolite s Ethylbenzene ; Metadynamics ; Dehydrogenation ; Catalysis ; Ab initio ; Hydrogen ; Molecular dynamics ; Potential energy Zeolite Catalysis and Synthesis ; Machine Learning in Materials Science ; Catalysis and Oxidation Reactions
Authors: Xiaoying Zhang | Ning Liu | Qian Guo | Guo-Yong Shi | Yue Wang
Source: The Journal of Chemical Physics (article, 2026-02-17)
Field: Materials Science — Materials Chemistry

Beyond Boltzmann transport: Green–Kubo prediction of lattice thermal conductivity with machine-learned potentials We investigate the microscopic origin of the ultralow lattice thermal conductivity in Rb2ZnTe by combining anharmonic lattice dynamics based on first-principles density functional theory with the Boltzmann transport equation (BTE). Full cubic and quartic anharmonicity is included, accounting for phonon renormalization and both three-phonon and four-phonon scattering processes. In addition, due to the unusually strong coherence effects in Rb2ZnTe, we consider off-diagonal contributions beyond the conventional BTE framework. The calculated phonon transport approaches the Ioffe-Regel limit, indicating the breakdown of the quasiparticle picture. To obtain an accurate thermal conductivity, we employ a machine-learning potential and perform Green-Kubo molecular dynamics simulations using GPUMD. At 300 K, the computed thermal conductivity increases by 38% compared to previous estimates, yet remains close to the amorphous limit, highlighting Rb2ZnTe as a promising thermoelectric material. The methodology presented here provides a robust framework for understanding lattice thermal transport in strongly anharmonic crystals and guiding the rational design of low-thermal-conductivity materials. Anharmonicity ; Thermal conductivity ; Phonon ; Boltzmann equation ; Quasiparticle ; Lattice (music) ; Renormalization ; Scattering ; Quartic function Thermal properties of materials ; Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science
Authors: Muhamad Shirwan Abdullah Sani
Source:  (article, 2026-02-25)
Field: Materials Science — Polymers and Plastics

Smart Rubber Manufacturing: Integrating Thermoplastic Elastomers and AI-Driven Biotechnological Modifications Smart rubber manufacturing is undergoing rapid transformation through the convergence of advanced thermoplastic elastomers (TPEs) and artificial intelligence (AI)-enabled biotechnological modification strategies. This study explores an integrated framework that combines the tunable mechanical and thermal properties of TPE systems with data-driven optimization and bio-assisted material enhancement. Emphasis is placed on AI-supported predictive modeling for formulation design, process control, and performance forecasting, alongside enzyme-mediated and microbial modification techniques aimed at improving sustainability, recyclability, and functional adaptability. The proposed approach demonstrates how machine learning algorithms can accelerate material discovery, optimize crosslink density, enhance interfacial compatibility, and reduce waste during production. Furthermore, biotechnology-based surface and structural modifications are examined for their potential to impart self-healing, antimicrobial, and environmentally responsive characteristics. By synthesizing advances in polymer science, computational intelligence, and industrial biotechnology, this research highlights a pathway toward intelligent, sustainable, and high-performance rubber systems suitable for automotive, biomedical, and smart device applications. The findings underscore the importance of interdisciplinary integration in achieving next-generation manufacturing efficiency and eco-conscious material innovation. Thermoplastic elastomer ; Natural rubber ; Elastomer ; Process (computing) ; Surface modification ; Thermoplastic Polymer Nanocomposites and Properties ; Polymer composites and self-healing ; Hydrogels: synthesis, properties, applications
Authors: Zihan Dong | Yinglin Guan | Minru Wen | Le Huang
Source: Applied Physics Letters (article, 2026-01-12)
Field: Materials Science — Materials Chemistry

Unveiling bonding heterogeneity-driven anharmonicity and ultralow lattice thermal conductivity in NbSe2Br2: A machine learning accelerated discovery Transition metal chalcogenide halide (TM–Ch–X) compounds with significant heterogeneity in their chemical bonding have immense potential for thermoelectric applications. Their mixed ionic–covalent bonding nature, combined with intrinsic low lattice symmetry, provides a favorable platform for achieving strong lattice anharmonicity and ultralow lattice thermal conductivity. In this work, we developed a temperature-included crystal graph convolutional neural network to accurately predict mode-resolved Grüneisen parameters, a key descriptor of lattice anharmonicity. Using this approach, two-dimensional NbSe2Br2 is identified as a thermoelectric candidate with strong anharmonicity and ultralow lattice thermal conductivity. First-principles results reveal that the strong anharmonic lattice dynamics originate from its weak and heterogeneous chemical bonding, further leading to ultralow lattice thermal conductivity. NbSe2Br2 also exhibits favorable electronic transport behavior, resulting in a maximum ZT of 1.63. Our work provides a theoretical understanding of the origin of low lattice thermal conductivity in TM–Ch–X compounds with bonding heterogeneity and should encourage further exploration of potential thermoelectric materials. Anharmonicity ; Thermoelectric effect ; Lattice (music) ; Thermoelectric materials ; Thermal conductivity ; Crystal structure Advanced Thermoelectric Materials and Devices ; 2D Materials and Applications ; Machine Learning in Materials Science
Authors: Ouarda El Hani | Khalid Digua | Aziz Amine
Source: Microchimica Acta (article, 2026-03-21)
Field: Chemistry — Analytical Chemistry

Generic strategy for high-performance bio-based imprinted membranes free of non-specific adsorption: machine learning-assisted sensing Membrane ; Feature (linguistics) ; Noise (video) ; Work (physics) Analytical chemistry methods development ; Adsorption and biosorption for pollutant removal ; Membrane Separation Technologies
Authors: Junwei Zhu | Jun Cheng
Source: APL Computational Physics (article, 2026-04-01)
Field: Materials Science — Materials Chemistry

How can machine learning facilitate computational electrochemistry Electrochemistry plays a central role in modern sustainable energy technologies, yet its computational modeling has long been constrained by the trade-off between the efficiency of classical force fields and the accuracy of ab initio methods. This limitation is particularly critical given the scarcity of experimental “ground truth” data for buried interfaces. This Perspective charts the transformative impact of machine learning on overcoming these long-standing spatiotemporal barriers. We begin with an overview of foundational methodologies, ranging from continuum models to ab initio molecular dynamics, to contextualize the unique physical requirements of the charged electrode–electrolyte interface. The discussion then focuses on the evolution of machine learning potentials, tracing their development from short-range local descriptors to advanced architectures capable of capturing long-range electrostatic interactions. A critical analysis is provided on the central challenge: accurately modeling the distinct dielectric responses of metallic conductors vs ionic insulators and the emergence of hybrid frameworks as a promising solution. Finally, we offer an outlook on the future of computational electrochemistry, arguing that the next frontier involves the synergistic integration of machine learning with multiscale modeling to bridge the gap between microscopic mechanisms and macroscopic device performance. Computational model ; Transformative learning ; Ab initio ; Field (mathematics) ; Bottleneck ; Computational learning theory ; Perspective (graphical) Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Electrocatalysts for Energy Conversion
Authors: Dipali Nayak | C. Alison Da Silva | Om Patel | Cristina H. Amon
Source: Applied Physics Letters (article, 2026-02-09)
Field: Materials Science — Materials Chemistry

Phonon transport in lithiated graphene–copper heterostructures: Intertwined thermal and electrochemical properties Effective thermal management is crucial for maintaining the efficiency and reliability of lithium-ion batteries (LIBs), which requires a comprehensive understanding of the intertwined phonon thermal transport and electrochemical processes occurring in the battery electrode materials. This work leverages density functional theory and machine-learning interatomic potentials to investigate and compare the phonon thermal transport and electrochemical properties of lithiated graphene monolayers and graphene–copper (Cu) heterostructures. The graphene–Cu heterostructure exhibits significantly reduced thermal conductivity compared to the graphene monolayer, originating from decreased phonon group velocities, shortened phonon lifetimes, increased anharmonicity as well as scattering phase space due to interlayer coupling. However, the diffusion energy barrier of Li ions over the graphene monolayer (0.3 eV) drops to 0.24 eV on the graphene top surface of the heterostructure and the Cu bottom surface has a substantially lower barrier of 0.04 eV, indicating enhanced electrochemical performance facilitated by strengthened adsorption energies. Additionally, lithiation further suppresses the thermal conductivity of the graphene monolayers and the graphene–Cu heterostructures with the top surface of the lithiated heterostructure depicts higher thermal conductivity than the bottom surface. These results reveal a fundamental trade-off between electrochemical enhancement and phonon-mediated heat transport, providing atomistic insights and design guidelines for optimizing graphene-based anodes and current–collector interfaces in next-generation LIBs. Graphene ; Phonon ; Thermal conductivity ; Heterojunction ; Phonon scattering ; Monolayer ; Density functional theory ; Surface phonon Thermal properties of materials ; Advancements in Battery Materials ; Thermal Expansion and Ionic Conductivity
Authors: Huiju Lee | Zhi Li | Jiangang He | Yi Xia
Source: ArXiv.org (article, 2026-01-14)
Field: Materials Science — Materials Chemistry

Data-Driven Exploration and Insights into Temperature-Dependent Phonons in Inorganic Materials Phonons, quantized vibrations of the atomic lattice, are fundamental to understanding thermal transport, structural stability, and phase behavior in crystalline solids. Despite advances in computational materials science, most predictions of vibrational properties in large materials databases rely on the harmonic approximation and overlook crucial temperature-dependent anharmonic effects. Here, we present a scalable computational framework that combines machine learning interatomic potentials, anharmonic lattice dynamics, and high-throughput calculations to investigate temperature-dependent phonons across thousands of materials. By fine-tuning the universal M3GNet interatomic potential using high-quality phonon data, we improve phonon prediction accuracy by a factor of four while preserving computational efficiency. Integrating this refined model into a high-throughput implementation of the stochastic self-consistent harmonic approximation, we compute temperature-dependent phonons for 4,669 inorganic compounds. Our analysis identifies systematic elemental and structural trends governing anharmonic phonon renormalization, with particularly strong manifestations in alkali metals, perovskite-derived frameworks, and related systems. Machine learning models trained on this dataset identify key atomic-scale features driving strong anharmonicity, including weak bonding, large atomic radii, and specific coordination motifs. First-principles validation confirms that anharmonic effects can dramatically alter lattice thermal conductivity by factors of two to four in some materials. This work establishes a robust and efficient data-driven approach for predicting finite-temperature phonon behavior, offering new pathways for the design and discovery of materials with tailored thermal and vibrational properties. Anharmonicity ; Phonon ; Thermal conductivity ; Interatomic potential ; Lattice (music) ; Molecular vibration ; Ab initio Thermal Expansion and Ionic Conductivity ; Machine Learning in Materials Science ; Thermal properties of materials
Authors: Rui Shu | Sijie Liu | Huilin Jia | Lei Zhao | Ibrahim A. Darwish | Jianlong Wang | Daohong Zhang
Source: Food Research International (article, 2026-02-11)
Field: Materials Science — Materials Chemistry

Machine-learning-guided risk stratification of bacterial contamination in food powered by a dual-mode lateral flow biosensor using low-crystallinity nanozyme labels Prussian blue ; Biosensor ; Salmonella ; Colloidal gold ; Contamination ; Analyte ; Bacteria Advanced Nanomaterials in Catalysis ; Biosensors and Analytical Detection ; Nanoplatforms for cancer theranostics
Authors: Kapil Dev Mahato
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Predicting Debye Temperature and Thermal Conductivity of Transition Metals Using Machine Learning Ensemble Models Debye model ; Ensemble learning ; Lattice (music) ; Thermal conductivity ; Tree (set theory) ; Decision tree ; Transition metal ; Debye Machine Learning in Materials Science ; Thermal properties of materials ; Nuclear Materials and Properties