jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Swagata Roy | Johannes P. Dürholt | Thomas S. Asche | Federico Zipoli | Rafael Gómez‐Bombarelli
Source: Nature Communications (article, 2024-07-17)
Field: Materials Science — Materials Chemistry

Learning a reactive potential for silica-water through uncertainty attribution The reactivity of silicates in aqueous solution is relevant to various chemistries ranging from silicate minerals in geology, to the C-S-H phase in cement, nanoporous zeolite catalysts, or highly porous precipitated silica. While simulations of chemical reactions can provide insight at the molecular level, balancing accuracy and scale in reactive simulations in the condensed phase is a challenge. Here, we demonstrate how a machine-learning reactive interatomic potential trained on PaiNN architecture can accurately capture silicate-water reactivity. The model was trained on a dataset comprising 400,000 energies and forces of molecular clusters at the ωB97X-D3/def2-TZVP level. To ensure the robustness of the model, we introduce a general active learning strategy based on the attribution of the model uncertainty, that automatically isolates uncertain regions of bulk simulations to be calculated as small-sized clusters. The potential reproduces static and dynamic properties of liquid water and solid crystalline silicates, despite having been trained exclusively on cluster data. Furthermore, we utilize enhanced sampling simulations to recover the self-ionization reactivity of water accurately, and the acidity of silicate oligomers, and lastly study the silicate dimerization reaction in a water solution at neutral conditions and find that the reaction occurs through a flanking mechanism. Silicate ; Reactivity (psychology) ; Nanoporous ; Robustness (evolution) ; Molecular dynamics ; Chemical physics ; Aqueous solution ; Catalysis ; Nanoscopic scale ; Materials science ; Chemistry ; Nanotechnology ; Computational chemistry ; Physical chemistry ; Organic chemistry Machine Learning in Materials Science ; Topic Modeling ; Zeolite Catalysis and Synthesis
Authors: Kangming Li | Kamal Choudhary | Brian DeCost | Michael T. Greenwood | Jason Hattrick‐Simpers
Source: Journal of Materials Chemistry A (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Efficient first principles based modeling <i>via</i> machine learning: from simple representations to high entropy materials Generalization performance of machine learning models: (upper panel) generalization from small ordered to large disordered structures (SQS); (lower panel) generalization from low-order to high-order systems. Computer science ; Generalization ; Density functional theory ; Entropy (arrow of time) ; Simple (philosophy) ; Machine learning ; Theoretical computer science ; Artificial intelligence ; Mathematics ; Computational chemistry ; Chemistry Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; High Entropy Alloys Studies
Authors: Samia | Muhammad Hasnain Jameel | Musfira Arain | Iftikhar Hussain | Muhammad Bilal Hanif | Shalu Atri | Mohd Zul Hilmi Mayzan | Haitao Dai
Source: Nano Materials Science (review, 2024-07-08)
Field: Materials Science — Materials Chemistry

A review on catalyst convergence: Unleashing the potential of MXenes for CO2 electrochemical reduction into high-value liquid product The electrochemical reduction reaction of carbon dioxide (CO2-ERR) holds tremendous potential as a key approach for achieving carbon neutrality by harnessing renewable resources. However, the current state of CO2-ERR encounters challenges in terms of efficiency and selectivity. Overcoming these obstacles requires the development of a robust electrocatalyst capable of enhancing process efficiency and improving selectivity towards desired products. In recent years, 2D materials have garnered significant attention as efficient catalysts. Among them, MXene stands out of high interest due to unique multilayered structure and presence of surface functional moieties. The MXene material offers high electrical conductivity, versatile surface chemistry, and tunable interface designs. This comprehensive review explores the utilization of MXene-based catalysts for CO2-ERR into valuable products. It covers fundamental aspects of electrochemical conversion, including CO2 adsorption on MXene Ti3C2Tx, the mechanism of CO2-ERR on MXene (Mo2CS2) single-atom catalysts, applications, synthesis methods of MXene production, and future prospects. Additionally, the review highlights the significance of modern artificial intelligence techniques, particularly machine learning, in screening and activating CO2, making it a pioneering scientific endeavor. MXenes ; Electrochemistry ; Reduction (mathematics) ; Product (mathematics) ; Catalysis ; Convergence (economics) ; Value (mathematics) ; Materials science ; Chemical engineering ; Chemistry ; Computer science ; Nanotechnology ; Mathematics ; Engineering ; Electrode ; Organic chemistry ; Economics ; Physical chemistry MXene and MAX Phase Materials ; Advanced Photocatalysis Techniques ; CO2 Reduction Techniques and Catalysts
Authors: Zhiqiang Zhao | Min Yi | Wanlin Guo | Zhuhua Zhang
Source: Physical review. B./Physical review. B (article, 2024-11-25)
Field: Materials Science — Materials Chemistry

General-purpose neural network potential for Ti-Al-Nb alloys towards large-scale molecular dynamics with <i>ab initio</i> accuracy High Nb-containing TiAl alloys exhibit exceptional high-temperature strength and room-temperature ductility, making them widely used in hot-section components of automotive and aerospace engines. However, the lack of accurate interatomic interaction potential for large-scale modeling severely hampers a comprehensive understanding of the failure mechanism of Ti-Al-Nb alloys and the development of strategies to enhance the mechanical properties. Here, we develop a general-purpose machine-learned potential (MLP) for the Ti-Al-Nb ternary system by combining the neural evolution potential framework with an active learning scheme. The developed MLP, trained on extensive first-principles datasets, demonstrates remarkable accuracy in predicting various lattice and defect properties as well as high-temperature characteristics such as thermal expansion and melting point for TiAl systems. Notably, this potential can effectively describe the key effect of Nb doping on stacking fault energies and formation energies. Of practical importance is that our MLP enables large-scale molecular dynamics simulations involving tens of millions of atoms with ab initio accuracy, achieving an outstanding balance between computational speed and accuracy. These results pave the way for elucidating micromechanical behaviors and failure mechanism TiAl lamellar structures and developing high-performance TiAl alloys towards applications at elevated temperatures. Ab initio ; Molecular dynamics ; Scale (ratio) ; Artificial neural network ; Materials science ; Atomic units ; Dynamics (music) ; Ab initio quantum chemistry methods ; Statistical physics ; Computational chemistry ; Computer science ; Artificial intelligence ; Physics ; Chemistry ; Molecule ; Quantum mechanics Machine Learning in Materials Science ; Titanium Alloys Microstructure and Properties ; Intermetallics and Advanced Alloy Properties
Authors: Sarath Menon | Yury Lysogorskiy | A. Knoll | Niklas Leimeroth | Marvin Poul | Minaam Qamar | Jan Janßen | Matous Mrovec | Jochen Rohrer | Karsten Albe | Jörg Behler | Ralf Drautz | Jörg Neugebauer
Source: npj Computational Materials (article, 2024-11-17)
Field: Materials Science — Materials Chemistry

From electrons to phase diagrams with machine learning potentials using pyiron based automated workflows Abstract We present a comprehensive and user-friendly framework built upon the integrated development environment (IDE), enabling researchers to perform the entire Machine Learning Potential (MLP) development cycle consisting of (i) creating systematic DFT databases, (ii) fitting the Density Functional Theory (DFT) data to empirical potentials or MLPs, and (iii) validating the potentials in a largely automatic approach. The power and performance of this framework are demonstrated for three conceptually very different classes of interatomic potentials: an empirical potential (embedded atom method - EAM), neural networks (high-dimensional neural network potentials - HDNNP) and expansions in basis sets (atomic cluster expansion - ACE). As an advanced example for validation and application, we show the computation of a binary composition-temperature phase diagram for Al-Li, a technologically important lightweight alloy system with applications in the aerospace industry. Workflow ; Computer science ; Phase (matter) ; Phase diagram ; Electron ; Artificial intelligence ; Physics ; Nuclear physics ; Database Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Materials Characterization Techniques
Authors: Muyu Lu | Fengyu Gao | Yiran Tan | Honghong Yi | Gui Yang | Yan Xu | Ya Wang | Yuansong Zhou | Xiaolong Tang | Linjiang Chen
Source: ACS Applied Materials & Interfaces (article, 2024-01-12)
Field: Materials Science — Materials Chemistry

Knowledge-Driven Experimental Discovery of Ce-Based Metal Oxide Composites for Selective Catalytic Reduction of NO<sub><i>x</i></sub> with NH<sub>3</sub> through Interpretable Machine Learning Mining the scientific literature, combined with data-driven methods, may assist in the identification of optimized catalysts. In this paper, we employed interpretable machine learning to discover ternary metal oxides capable of selective catalytic reduction of nitrogen oxides with ammonia (NH<sub>3</sub>-SCR). Specifically, we devised a machine learning framework utilizing extreme gradient boosting (XGB), identified for its optimal performance, and SHapley Additive exPlanations (SHAP) to evaluate a curated database of 5654 distinct metal oxide composite catalytic systems containing cerium (Ce) element, with records of catalyst composition and preparation and reaction conditions. By virtual screening, this framework precisely pinpointed a CeO<sub>2</sub>-MoO<sub>3</sub>-Fe<sub>2</sub>O<sub>3</sub> catalyst with superior NO<sub><i>x</i></sub> conversion, N<sub>2</sub> selectivity, and resistance to H<sub>2</sub>O and SO<sub>2</sub>, as confirmed by empirical evaluations. Subsequent characterization affirmed its favorable structural, chemical bulk properties and reaction mechanism. Demonstrating the efficacy of combining knowledge-driven techniques with experimental validation and analysis, our strategy charts a course for analogous catalyst discoveries. Catalysis ; Materials science ; Ternary operation ; Selective catalytic reduction ; Oxide ; Cerium oxide ; Metal ; NOx ; Characterization (materials science) ; Selectivity ; Computer science ; Machine learning ; Nanotechnology ; Organic chemistry ; Chemistry Catalytic Processes in Materials Science ; Ammonia Synthesis and Nitrogen Reduction ; Advanced Photocatalysis Techniques
Authors: Yuquan Wu | Da Lei | Jiawen Li | Ying Luo | Yuwan Du | Shi Zhang | Baiyi Zu | Yuhong Su | Xincun Dou
Source: Small (article, 2024-04-10)
Field: Materials Science — Materials Chemistry

Controlled Synthesis of Preferential Facet‐Exposed Fe‐MOFs for Ultrasensitive Detection of Peroxides Exposing different facets on metal-organic frameworks (MOFs) is highly desirable to enhance the performance for various applications, however, exploiting a concise and effective approach to achieve facet-controlled synthesis of MOFs remains challenging. Here, by modulating the ratio of metal precursors to ligands, the facet-engineered iron-based MOFs (Fe-MOFs) exhibits enhanced catalytic activity for Fenton reaction are explored, and the mechanism of facet-dependent performance is revealed in detail. Fully exposed (101) and (100) facets on spindle-shaped Fe-MOFs enable rapid oxidation of colorless o-phenylenediamine (OPD) to colored products, thereby establishing a dual-mode platform for the detection of hydrogen peroxide (H<sub>2</sub>O<sub>2</sub>) and triacetone triperoxide (TATP). Thus, a detection limit as low as 2.06 nm is achieved, and robust selectivity against a wide range of common substances (>16 types) is obtained, which is further improved by incorporating a deep learning architecture with an SE-VGG16 network model, enabling precise differentiation of oxidizing agents from captured images. The present strategy is expected will shine light on both the rational synthesis of nanomaterials with modulated morphologies and the exploitation of high-performance trace chemical sensors. Oxidizing agent ; Facet (psychology) ; Metal-organic framework ; Nanotechnology ; Selectivity ; Nanomaterials ; Materials science ; Rational design ; Catalysis ; Hydrogen peroxide ; Combinatorial chemistry ; Chemistry ; Organic chemistry Advanced Nanomaterials in Catalysis ; Metal-Organic Frameworks: Synthesis and Applications ; Gas Sensing Nanomaterials and Sensors
Authors: Yuhui Li | Guolin Wan | Yongqian Zhu | Jingyu Yang | Yanfang Zhang | Jinbo Pan | Shixuan Du
Source: Nature Communications (article, 2024-11-04)
Field: Materials Science — Materials Chemistry

High-throughput screening and machine learning classification of van der Waals dielectrics for 2D nanoelectronics Van der Waals (vdW) dielectrics are promising for enhancing the performance of nanoscale field-effect transistors (FETs) based on two-dimensional (2D) semiconductors due to their clean interfaces. Ideal vdW dielectrics for 2D FETs require high dielectric constants and proper band alignment with 2D semiconductors. However, high-quality dielectrics remain scarce. Here, we employed a topology-scale algorithm to screen vdW materials consisting of zero-dimensional (0D), one-dimensional (1D), and 2D motifs from Materials Project database. High-throughput first-principles calculations yielded bandgaps and dielectric properties of 189 0D, 81 1D and 252 2D vdW materials. Among which, 9 highly promising dielectric candidates are suitable for MoS<sub>2</sub>-based FETs. Element prevalence analysis indicates that materials containing strongly electronegative anions and heavy cations are more likely to be promising dielectrics. Moreover, we developed a high-accuracy two-step machine learning (ML) classifier for screening dielectrics. Implementing active learning framework, we successfully identified 49 additional promising vdW dielectrics. This work provides a rich candidate list of vdW dielectrics along with a high-accuracy ML screening model, facilitating future development of 2D FETs. Nanoelectronics ; Throughput ; Dielectric ; van der Waals force ; Computer science ; High-throughput screening ; Nanotechnology ; Optoelectronics ; Physics ; Materials science ; Bioinformatics ; Biology ; Quantum mechanics ; Telecommunications ; Wireless ; Molecule Graphene research and applications ; Advancements in Semiconductor Devices and Circuit Design ; Semiconductor materials and devices
Authors: Wakana Hamada | Mafumi Hishida | Ryuto Sugiura | Haruka Tobita | Hiroaki Imai | Yasuhiko Igarashi | Yuya Oaki
Source: Journal of Materials Chemistry A (article, 2024-01-01)
Field: Materials Science — Polymers and Plastics

Efficient design and synthesis of an amorphous conjugated polymer network for a metal-free electrocatalyst of hydrogen evolution reaction A performance predictor for metal-free electrocatalyst of hydrogen evolution reaction was constructed by machine learning on small data. Based on the prediction, an amorphous conjugated polymer network was designed to achieve high catalytic activity. Electrocatalyst ; Conjugated system ; Amorphous solid ; Catalysis ; Polymer ; Materials science ; Hydrogen ; Metal ; Chemical engineering ; Nanotechnology ; Chemistry ; Electrochemistry ; Organic chemistry ; Electrode ; Physical chemistry ; Engineering ; Metallurgy ; Composite material Conducting polymers and applications ; Electrocatalysts for Energy Conversion ; Fuel Cells and Related Materials
Authors: Gaurav Deshmukh | Noah J. Wichrowski | Nikolaos Evangelou | Pushkar Ghanekar | Siddharth Deshpande | Ioannis G. Kevrekidis | Jeffrey Greeley
Source: npj Computational Materials (article, 2024-05-30)
Field: Materials Science — Materials Chemistry

Active learning of ternary alloy structures and energies Abstract Machine learning models with uncertainty quantification have recently emerged as attractive tools to accelerate the navigation of catalyst design spaces in a data-efficient manner. Here, we combine active learning with a dropout graph convolutional network (dGCN) as a surrogate model to explore the complex materials space of high-entropy alloys (HEAs). We train the dGCN on the formation energies of disordered binary alloy structures in the Pd-Pt-Sn ternary alloy system and improve predictions on ternary structures by performing reduced optimization of the formation free energy, the target property that determines HEA stability, over ensembles of ternary structures constructed based on two coordinate systems: (a) a physics-informed ternary composition space, and (b) data-driven coordinates discovered by the Diffusion Maps manifold learning scheme. Both reduced optimization techniques improve predictions of the formation free energy in the ternary alloy space with a significantly reduced number of DFT calculations compared to a high-fidelity model. The physics-based scheme converges to the target property in a manner akin to a depth-first strategy, whereas the data-driven scheme appears more akin to a breadth-first approach. Both sampling schemes, coupled with our acquisition function, successfully exploit a database of DFT-calculated binary alloy structures and energies, augmented with a relatively small number of ternary alloy calculations, to identify stable ternary HEA compositions and structures. This generalized framework can be extended to incorporate more complex bulk and surface structural motifs, and the results demonstrate that significant dimensionality reduction is possible in thermodynamic sampling problems when suitable active learning schemes are employed. Ternary operation ; Curse of dimensionality ; Materials science ; Statistical physics ; Computer science ; Chemical space ; Algorithm ; Physics ; Chemistry ; Machine learning Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Electrocatalysts for Energy Conversion
Authors: Mgcini Keith Phuthi | Archie Mingze Yao | Simon Batzner | Albert Musaelian | Pin-Wen Guan | Boris Kozinsky | Ekin D. Cubuk | Venkatasubramanian Viswanathan
Source: ACS Omega (article, 2024-02-21)
Field: Materials Science — Materials Chemistry

Accurate Surface and Finite-Temperature Bulk Properties of Lithium Metal at Large Scales Using Machine Learning Interaction Potentials The properties of lithium metal are key parameters in the design of lithium-ion and lithium-metal batteries. They are difficult to probe experimentally due to the high reactivity and low melting point of lithium as well as the microscopic scales at which lithium exists in batteries where it is found to have enhanced strength, with implications for dendrite suppression strategies. Computationally, there is a lack of empirical potentials that are consistently quantitatively accurate across all properties, and ab initio calculations are too costly. In this work, we train a machine learning interaction potential on density functional theory (DFT) data to state-of-the-art accuracy in reproducing experimental and ab initio results across a wide range of simulations at large length and time scales. We accurately predict thermodynamic properties, phonon spectra, temperature dependence of elastic constants, and various surface properties inaccessible using DFT. We establish that there exists a weak Bell-Evans-Polanyi relation correlating the self-adsorption energy and the minimum surface diffusion barrier for high Miller index facets. Lithium (medication) ; Ab initio ; Density functional theory ; Materials science ; Diffusion ; Thermodynamics ; Chemistry ; Chemical physics ; Computational chemistry ; Physics Machine Learning in Materials Science ; Advancements in Battery Materials ; Advanced Battery Materials and Technologies
Authors: Xiaorui Liu | Haiping Yang | Tang Yuanjun | Chao Ye | Hui Jin | Xue Peixuan
Source: Bioresource Technology (article, 2024-05-25)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Deep learning prediction and experimental investigation of specific capacitance of nitrogen-doped porous biochar Biochar ; Porosity ; Capacitance ; Nitrogen ; Materials science ; Doping ; Chemical engineering ; Environmental science ; Chemistry ; Composite material ; Engineering ; Optoelectronics ; Pyrolysis ; Organic chemistry ; Physical chemistry ; Electrode Supercapacitor Materials and Fabrication ; Conducting polymers and applications ; Membrane-based Ion Separation Techniques
Authors: Koyuru Nakayama | Keita Sakakibara
Source: Science and Technology of Advanced Materials (article, 2024-05-08)
Field: Materials Science — Polymers and Plastics

Machine learning strategy to improve impact strength for PP/cellulose composites via selection of biomass fillers Lignocellulosic materials have inherent complexities and natural nanoarchitectures, such as various chemical constituents in wood cell walls, structural factors such as fillers, surface properties, and variations in production. Recently, the development of lignocellulosic filler-reinforced polymer composites has attracted increasing attention due to their potential in various industries, which are recognized for environmental sustainability and impressive mechanical properties. The growing demand for these composites comes with increased complexity regarding their specifications. Conventional trial-and-error methods to achieve desired properties are time-intensive and costly, posing challenges to efficient production. Addressing these issues, our research employs a data-driven approach to streamline the development of lignocellulosic composites. In this study, we developed a machine learning (ML)-assisted prediction model for the impact energy of the lignocellulosic filler-reinforced polypropylene (PP) composites. Firstly, we focused on the influence of natural supramolecular structures in biomass fillers, where the Fourier transform infrared spectra and the specific surface area are used, on the mechanical properties of the PP composites. Subsequently, the effectiveness of the ML model was verified by selecting and preparing promising composites. This model demonstrated sufficient accuracy for predicting the impact energy of the PP composites. In essence, this approach streamlines selecting wood species, saving valuable time. Materials science ; Lignocellulosic biomass ; Composite material ; Filler (materials) ; Biomass (ecology) ; Cellulose ; Papermaking ; Polypropylene ; Natural fiber ; Fiber ; Chemical engineering Natural Fiber Reinforced Composites ; Lignin and Wood Chemistry ; Advanced Cellulose Research Studies