jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Aqshat Seth | Rutvij Pankaj Kulkarni | Gopalakrishnan Sai Gautam
Source: arXiv (Cornell University) (preprint, 2024-09-10)
Field: Materials Science — Materials Chemistry

Investigating Ionic Diffusivity in Amorphous Solid Electrolytes using Machine Learned Interatomic Potentials Investigating Li$^+$ transport within the amorphous lithium phosphorous oxynitride (LiPON) framework, especially across a Li||LiPON interface, has proven challenging due to its amorphous nature and varying stoichiometry, necessitating large supercells and long timescales for computational models. Notably, machine learned interatomic potentials (MLIPs) can combine the computational speed of classical force fields with the accuracy of density functional theory (DFT), making them the ideal tool for modelling such amorphous materials. Thus, in this work, we train and validate the neural equivariant Interatomic potential (NequIP) framework on a comprehensive DFT-based dataset consisting of 13,454 chemically relevant structures to describe LiPON. With an optimized training (validation) energy and force mean absolute errors of 5.5 (6.1) meV/atom and 13.6 (13.2) meV/Å, respectively, we employ the trained potential in model Li-transport in both bulk LiPON and across a Li||LiPON interface. Amorphous LiPON structures generated by the optimized potential do resemble those generated by ab initio molecular dynamics, with N being incorporated on non-bridging apical and bridging sites. Subsequent analysis of Li$^+$ diffusivity in the bulk LiPON structures indicates broad agreement with computational and experimental literature so far. Further, we investigate the anisotropy in Li$^+$ transport across the Li(110)||LiPON interface, where we observe Li-transport across the interface to be one order-of-magnitude slower than Li-motion within the bulk Li and LiPON phases. Nevertheless, we note that this anisotropy of Li-transport across the interface is minor and do not expect it to cause any significant impedance buildup. Finally, our work highlights the efficiency of MLIPs in enabling high-fidelity modelling of complex non-crystalline systems over large length and time scales. Ionic bonding ; Thermal diffusivity ; Amorphous solid ; Electrolyte ; Fast ion conductor ; Chemical physics ; Materials science ; Interatomic potential ; Thermodynamics ; Ion ; Chemistry ; Statistical physics ; Computational chemistry ; Physical chemistry ; Physics ; Crystallography ; Molecular dynamics ; Organic chemistry Machine Learning in Materials Science ; Zeolite Catalysis and Synthesis ; Solid-state spectroscopy and crystallography
Authors: Samuel P. Gleason | Matthew R. Carbone | Deyu Lu | Jim Ciston
Source: arXiv (Cornell University) (preprint, 2024-12-03)
Field: Materials Science — Materials Chemistry

CuXASNet: Rapid and Accurate Prediction of Copper L-edge X-Ray Absorption Spectra Using Machine Learning In this work, we have developed CuXASNet, a dense neural network that predicts simulated Cu L-edge X-ray absorption spectra (XAS) from atomic structures. Featurization of the Cu local environment is performed using a component of M3GNet, a graph neural network developed for predicting the potential energy surface. CuXASNet is trained on simulated spectra from FEFF9 at the multiple scattering level of theory, and can predict the L3 and L2 edges for Cu sites to quantitative accuracy. To validate our approach, we compare 14 experimental spectra extracted from the literature with the predictions of CuXASNet. The agreement of CuXASNet with experiments is shown by an average MAE of 0.125 and an average Spearman's correlation coefficient of 0.891, which is comparable to FEFF9's values of 0.131 and 0.898 for the same metrics. As such, CuXASNet can rapidly generate a large number of L-edge XAS spectra at the same accuracy as FEFF9 simulations. This can be used as a drop-in replacement for multiple scattering codes for fast screening of candidate atomic structure models of a measured system. This model establishes a general framework for Cu XAS prediction, and can be extended to more computationally expensive levels of theory and to other transition metal L-edges. Copper ; Enhanced Data Rates for GSM Evolution ; Absorption (acoustics) ; Spectral line ; Absorption edge ; X-ray ; Absorption spectroscopy ; Analytical Chemistry (journal) ; Materials science ; Artificial intelligence ; Computer science ; Chemistry ; Optics ; Physics ; Metallurgy ; Optoelectronics ; Environmental chemistry ; Astronomy Machine Learning in Materials Science ; Advanced X-ray and CT Imaging ; X-ray Diffraction in Crystallography
Source: Global NEST Journal (article, 2024-07-10)
Field: Materials Science — Polymers and Plastics

Exploring the novel environmentally friendly dye degradation mechanism of NiO-Polypyrrole polymer nanocomposites in degrading the organic pollutants under sunlight &lt;p style="text-align:justify"&gt;&lt;span style="font-size:11pt"&gt;&lt;span style="line-height:115%"&gt;&lt;span style="font-family:Calibri,sans-serif"&gt;&lt;span lang="en-AE" style="font-size:12.0pt"&gt;&lt;span style="line-height:115%"&gt;&lt;span style="font-family:&amp;quot;Times New Roman&amp;quot;,serif"&gt;According to estimates, around 15-50% of textile dyes fail to adhere to the fabric during the dyeing process, resulting in their discharge into wastewater. This wastewater is often used for agricultural irrigation in impoverished nations which harms plant germination and development. In this study, polymer composites of Polypyrrole (PPy) and nickel oxide (NiO) nanocomposites were synthesized via the hydrothermal method with different concentrations (250 mg, 500 mg, and 75 0mg) and their composite samples were employed as catalysts to degrade the anionic-brilliant blue and the cationic-crystal violet dye under 120 mins of sunlight irradiation. The process of degrading BB and CV dyes utilising nano catalysis was analysed using a pseudo-first-order kinetics model. The findings indicate that the degradation of the anionic dye is more efficient than that of the cationic dye under exposure to sunlight. Hence, the presence of NiO NPs and the NiO-PPy nanocomposite significantly impact their photocatalytic activity in degrading BB and CV dyes, achieving an impressive efficiency of around 96.99% and 89.96% respectively. Moreover, its stability has also been studied for over five cycles.&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/p&gt; Polypyrrole ; Degradation (telecommunications) ; Environmentally friendly ; Pollutant ; Nanocomposite ; Polymer ; Non-blocking I/O ; Sunlight ; Chemical engineering ; Conductive polymer ; Mechanism (biology) ; Chemistry ; Materials science ; Environmental chemistry ; Nanotechnology ; Organic chemistry ; Catalysis ; Polymerization ; Computer science Conducting polymers and applications ; Water Quality Monitoring and Analysis ; Pigment Synthesis and Properties
Authors: Yan Zini | Jingya Wang | Yao Yang | Xiaoqin Ceng
Source: Scientia Sinica Technologica (article, 2024-11-12)
Field: Materials Science — Biomaterials

New principles and methods for designing corrosion-resistant magnesium alloys <p indent="0mm">Magnesium alloys are the lightest structural materials, renowned for their low density, high specific strength, excellent thermal conductivity, superior damping and shock absorption properties, electromagnetic shielding capabilities, and good biocompatibility. These characteristics make them ideal for applications in diverse fields, including aerospace, automotive manufacturing, energy production, biomedical engineering, and consumer electronics. Beyond improving energy efficiency and lowering operating costs, the using magnesium alloys can considerably reduce greenhouse gas emissions and conserve energy resources, solidifying their status as “green engineering materials of the 21st century”. This study examines the principles of magnesium alloy corrosion and the factors influencing it, explores design strategies for corrosion-resistant magnesium alloys, and investigates emerging paradigms aligned with the growing trend of artificial intelligence-assisted material design. This study also highlights prospects for developing high-strength, high-ductility, and corrosion-resistant magnesium alloys. Psychology Magnesium Alloys: Properties and Applications ; Material Properties and Applications ; Corrosion Behavior and Inhibition
Authors: Joseph Edgecomb | Dan Thien Nguyen | Shuai Tan | Vijayakumar Murugesan | Grant E. Johnson | Venkateshkumar Prabhakaran
Source: Angewandte Chemie (article, 2024-06-14)
Field: Chemistry — Electrochemistry

Electrochemical Imaging of Precisely‐Defined Redox and Reactive Interfaces Abstract Understanding the diverse electrochemical reactions occurring at electrode‐electrolyte interfaces (EEIs) is a critical challenge to developing more efficient energy conversion and storage technologies. Establishing a predictive molecular‐level understanding of solid electrolyte interphases (SEIs) is challenging due to the presence of multiple intertwined chemical and electrochemical processes occurring at battery electrodes. Similarly, chemical conversions in reactive electrochemical systems are often influenced by the heterogeneous distribution of active sites, surface defects, and catalyst particle sizes. In this mini review, we highlight an emerging field of interfacial science that isolates the impact of specific chemical species by preparing precisely‐defined EEIs and visualizing the reactivity of their individual components using single‐entity characterization techniques. We highlight the broad applicability and versatility of these methods, along with current state‐of‐the‐art instrumentation and future opportunities for these approaches to address key scientific challenges related to batteries, chemical separations, and fuel cells. We establish that controlled preparation of well‐defined electrodes combined with single entity characterization will be crucial to filling key knowledge gaps and advancing the theories used to describe and predict chemical and physical processes occurring at EEIs and accelerating new materials discovery for energy applications. Characterization (materials science) ; Nanotechnology ; Electrochemistry ; Electrolyte ; Electrochemical energy conversion ; Battery (electricity) ; Electrode ; Materials science ; Redox ; Chemical energy ; Computer science ; Biochemical engineering ; Chemistry ; Physics ; Organic chemistry ; Engineering Electrochemical Analysis and Applications ; Advanced battery technologies research ; Conducting polymers and applications
Authors: Huanxin Li
Source:  (preprint, 2024-08-02)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Quantum-Dots-in-Carbon functional materials for Energy Storage The 2023 Nobel Prize in Chemistry was awarded to American-French-Tunisian chemist Moungi Bawendi, American chemist Louis Brus, and Russian physicist Alexei Ekimov for their groundbreaking work on the ”discovery and synthesis of quantum dots.” Quantum dots have been widely applied in the areas of Electronics, Biomedical Imaging, Solar Cells, and Chemical Sensors. However, research on the application of quantum dots in electrochemical energy storage remains relatively limited. In the context of ”net-zero” goals, Quantum-Dots-in-Carbon functional materials hold broad application prospects in energy storage areas. Here, we have reviewed and forecasted the trajectory of quantum dots, with a special focus on our team’s contributions in the field of Quantum-Dots-in-Carbon functional Materials. Our efforts have provided an overview and analysis of this area, paving the way for future research and indicating directions for further exploration. Quantum dot ; Context (archaeology) ; Nanotechnology ; Engineering physics ; Physics ; Materials science ; History Supercapacitor Materials and Fabrication ; Advancements in Battery Materials ; Conducting polymers and applications
Authors: Thomas Leiner | David Holec
Source: arXiv (Cornell University) (preprint, 2024-12-16)
Field: Materials Science — Materials Chemistry

Revealing trends in catalytic activity of adatoms for hydrogen adsorption on carbon: a case study of graphene and carbon nanotube The increasing demand for sustainable energy solutions necessitates advancements in hydrogen storage technologies. This study investigates the hydrogen adsorption characteristics of graphene and a (8,0) carbon nanotube (CNT) decorated with adatoms of various elements. Using molecular dynamics (MD) simulations and the universal interatomic potential 'PreFerred Potential' (PFP) implemented in the Matlantis framework, we explore the hydrogen storage capabilities of these doped carbon structures at 77K. We analyze the adsorption efficiency based on the position of adatoms (top, bridge, and hollow sites) and find that the group II elements, such as calcium and strontium, exhibit significant hydrogen uptake. Additionally, light elements like lithium and sodium demonstrate enhanced gravimetric hydrogen storage due to their low atomic mass. Our findings provide insights into the potential of doped graphene and CNTs for efficient hydrogen storage applications. Graphene ; Carbon nanotube ; Catalysis ; Hydrogen ; Carbon fibers ; Adsorption ; Nanotechnology ; Materials science ; Carbon nanotube quantum dot ; Chemical physics ; Chemical engineering ; Nanotube ; Chemistry ; Physical chemistry ; Composite material ; Organic chemistry ; Composite number ; Engineering Graphene research and applications ; Boron and Carbon Nanomaterials Research
Authors: Shihang Xu | S. N. G. Chu | Rami Mrad | Z. Q. Zhang | Zhelin Li | Runxian Jiao | Yuanping Chen
Source: arXiv (Cornell University) (preprint, 2024-12-24)
Field: Materials Science — Materials Chemistry

Discovery of 2D Materials via Symmetry-Constrained Diffusion Model Generative model for 2D materials has shown significant promise in accelerating the material discovery process. The stability and performance of these materials are strongly influenced by their underlying symmetry. However, existing generative models for 2D materials often neglect symmetry constraints, which limits both the diversity and quality of the generated structures. Here, we introduce a symmetry-constrained diffusion model (SCDM) that integrates space group symmetry into the generative process. By incorporating Wyckoff positions, the model ensures adherence to symmetry principles, leading to the generation of 2,000 candidate structures. DFT calculations were conducted to evaluate the convex hull energies of these structures after structural relaxation. From the generated samples, 843 materials that met the energy stability criteria (Ehull &lt; 0.6 eV/atom) were identified. Among these, six candidates were selected for further stability analysis, including phonon band structure evaluations and electronic properties investigations, all of which exhibited phonon spectrum stability. To benchmark the performance of SCDM, a symmetry-unconstrained diffusion model was also evaluated via crystal structure prediction model. The results highlight that incorporating symmetry constraints enhances the effectiveness of generated 2D materials, making a contribution to the discovery of 2D materials through generative modeling. Symmetry (geometry) ; Diffusion ; Statistical physics ; Theoretical physics ; Physics ; Mathematics ; Geometry ; Thermodynamics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Zeolite Catalysis and Synthesis
Authors: Eugene Stephane Mananga | Aissata Diop | Paulin JT Dongomale | Fambougouri Diane | Hubertus J. J. van Dam
Source: arXiv (Cornell University) (preprint, 2024-02-22)
Field: Chemistry — Organic Chemistry

Density Functional Theory Calculations of the thermochemistry of the dehydration of 2-propanol Electronic structure theory provides a foundation for understanding chemical transformations and processes in complex chemical environments. Our work is focused on the NWChemEx project that has selected two interrelated science challenges that address the production of advanced biomass-derived fuels and other value-added chemical compounds. One of which is the dehydration of 2-propanol over a zeolite catalyst. Aqueous phase dehydration of 2-propanol was investigated using density functional theory (DFT) calculations. We considered and analyzed the thermochemistry of the dehydration of 2-propanol using NWChem calculations while the NWChemEx code is still under development. Realistically modeling the reaction in this study properly requires simulations using extended atomistic models. We validated our computational models by comparing the predicted outcomes for 2-propanol dehydration with the calculated results from 1-propanol dehydration studies. We used the first-principles DFT calculations to investigate aqueous phase dehydration of 2-propanol, examine the enthalpy of the 2-propanol reaction and computed the energy for geometry optimization for increasingly better basis sets: cc-pVDZ, cc-pVTZ, cc-pVQZ, cc-pV5Z, and cc-pV6Z. The various transition states and minima along the reaction pathway are critical to inform the NWChemEx science challenge calculations. In this work, we established how the accuracy of the calculations depends on the basis sets, and we determined what basis sets are needed to achieve sufficient accurate results. We also calculated the reaction free energy as a function of temperature as thermodynamic parameter. We found that at low temperature the reaction is thermodynamically unfavorable. Nevertheless, dehydrating 2-propanol increases entropy, underscoring the need for high temperatures to facilitate the reaction. Thermochemistry ; Dehydration ; Propanol ; Density functional theory ; Chemistry ; 1-Propanol ; Thermodynamics ; Computational chemistry ; Mathematics ; Physical chemistry ; Physics ; Organic chemistry ; Biochemistry Chemical Thermodynamics and Molecular Structure ; Thermodynamic properties of mixtures ; thermodynamics and calorimetric analyses
Authors: Eva Zurek | Josiah Roberts | Biswas Rijal | Simon Divilov | Jon‐Paul Maria | William G. Fahrenholtz | Douglas E. Wolfe | Donald W. Brenner | Stefano Curtarolo
Source: Research Square (preprint, 2024-01-17)
Field: Materials Science — Materials Chemistry

Machine Learned Interatomic Potentials for Ternary Carbides trained on the AFLOW Database Relaxation (psychology) ; Ternary operation ; Tensor (intrinsic definition) ; Density functional theory ; Stoichiometry ; Computer science ; Materials science ; Database ; Statistical physics ; Chemistry ; Computational chemistry ; Physics ; Mathematics ; Physical chemistry ; Geometry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Boron and Carbon Nanomaterials Research
Authors: Ritesh Haldar | Suvendu Panda | Tanmoy Maity | Susmita Sarkar | Arun K. Manna | Jagannath Mondal
Source: Research Square (preprint, 2024-09-23)
Field: Chemistry — Inorganic Chemistry

Diffusion-programmed catalysis in nanoporous material Nanoporous ; Diffusion ; Catalysis ; Materials science ; Chemistry ; Nanotechnology ; Thermodynamics ; Organic chemistry ; Physics Metal-Organic Frameworks: Synthesis and Applications ; Catalytic Processes in Materials Science ; Nanomaterials for catalytic reactions
Authors: Qiye Guan | Kaiyang Wang | Jingjie Yeo | Yongqing Cai
Source: arXiv (Cornell University) (preprint, 2024-12-10)
Field: Chemistry — Electrochemistry

Path Entropy-driven Design of Solid-State Electrolytes The development of high-performance solid-state electrolytes (SSEs) has entered a critical stage, where entropy-driven strategies offer transformative potential for enhancing electrochemical properties. By engineering local environments for conductive ions alongside introducing disorder, these approaches can significantly improve conductivity. However, embracing high-entropy designs does not always guarantee improved performance. Current entropy descriptions oversimplify disorder by accounting solely for host framework configurations, neglecting conductive ion-induced disorder, rendering such descriptions incomplete. Herein, we propose path entropy (Sp) as a descriptor that quantifies diffusion pathway diversity, directly capturing diffusional disorder. Combining Markov state model with transition path theory, we reveal the interplay between diffusion pathway diversity of lithium and microscopic local environments in inorganic thiophosphates. Generalizing this path-informative Sp for high-throughput screening, we demonstrate its broad applicability in identifying and designing high-performance SSEs. Our work establishes a critical link between entropy evolution underlying ion conduction and practical entropy-driven design principles. Electrolyte ; Ion ; Statistical physics ; Solid-state ; Path (computing) ; Entropy (arrow of time) ; Materials science ; Computer science ; Thermodynamics ; Physics ; Engineering physics ; Chemistry ; Physical chemistry Electrochemical Analysis and Applications ; Fuel Cells and Related Materials
Authors: Fahma Riyanti | Fitria Nursari | Bijak Rihandi Ahadito | Laila Hanum | Eliza Eliza | Poedji Loekitowati Hariani
Source:  (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Green Synthesis of Fe3O4 Magnetic Nanoparticle by Moringa oleifera Leaf Extract for Photocatalytic of Conge Red dye Natural water resources can be contaminated if industrial waste is inadequately treated. The uncontrolled release of dangerous organic compounds poses a serious environmental risk. Before wastewater may be discharged into the environment, it must be processed properly. The green synthesis method, Mo Moringa ; Magnetic nanoparticles ; Photocatalysis ; Nanoparticle ; Chemistry ; Materials science ; Nanotechnology ; Organic chemistry ; Food science ; Catalysis Nanoparticles: synthesis and applications ; Moringa oleifera research and applications
Authors: Murat Akkalender | Haydar Yalçın
Source: Sosyal Bilimler Metinleri (article, 2024-04-29)
Field: Materials Science — Materials Chemistry

Patent Analysis in the Realm of Machine Learning in Manufacturing Patent analysis reveals a surge in machine learning for manufacturing since 1980, hinting at its potential beyond traditional applications. This study explores this trend through three key questions: how machine learning use is evolving, what technological areas patents cover, and where these machine learning applications are being developed. The analysis finds machine learning impacting areas like medical devices and quality control across various industries. These findings suggest that machine learning can improve efficiency, ensure quality, and drive innovation, paving the way for future research into specific applications, productivity impacts, and potential challenges. Patent data from Lens.org was visualized employing of BibExcel, Pajek and VOSviewer. Productivity ; Quality (philosophy) ; Computer science ; Realm ; Key (lock) ; Artificial intelligence ; Machine learning ; Manufacturing engineering ; Manufacturing ; Patent analysis ; Industrial engineering ; Data science ; Engineering ; Business ; Marketing ; Computer security Machine Learning in Materials Science ; Intellectual Property and Patents ; Law, AI, and Intellectual Property