jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Paolo Vincenzo Freiesleben de Blasio | Peter Bjørn Jørgensen | Juan Maria Garcia Lastra | Arghya Bhowmik
Source: ChemRxiv (preprint, 2023-06-13)
Field: Materials Science — Materials Chemistry

Nanosecond MD of battery cathode materials withelectron density description Potentials derived with machine learning algorithms achieve the accuracy of high-fidelity quantum mechanical computations such as density functional theory (DFT), while allowing orders of magnitude lower computational time. In this work, we demonstrate the use of uncertainty aware equivariant graph neural networks for predicting spin-resolved electron densities, forces, and energies of the $Na_3V_2(PO_4)_3$ NASICON structured cathode. Due to the speedup in computational time, we are able to investigate structures of $\sim 300$ atoms for ~200 million timesteps. The ability to model larger systems on the nanosecond length scale with maintaining DFT level accuracy allowed critical insights into the diffusion characteristics of Na-ions, associated electron transfer processes, and dependence of diffusivity on sodium concentration in the structure. Density functional theory ; Nanosecond ; Electron ; Thermal diffusivity ; Speedup ; Cathode ; Ion ; Electron density ; Electron transfer ; Molecular dynamics ; Physics ; Statistical physics ; Computer science ; Materials science ; Chemistry ; Computational chemistry ; Quantum mechanics ; Physical chemistry Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques
Authors: Ha Huu | Soo Young Kim
Source: Preprints.org (review, 2023-08-29)
Field: Chemistry — Inorganic Chemistry

Metal-Organic Frameworks Based Multifunctional Materials for Solar Cells: A Review Developing low-cost and stable materials for converting solar energy into electricity is vital in meeting the world&amp;#039;s energy demand. Metal-organic frameworks (MOFs) have gained attention for solar cells due to their natural porous architectures and tunable chemical structures. This review commences with an exploration of the synthetic methods of MOFs. Moreover, we discuss the various roles of MOFs, including photoanodes and counter electrodes in dye-sensitized solar cells and interfacial layers and charge carriers in perovskite solar cells. Ultimately, burdensome tasks and possible directions for advancing MOFs-based nanomaterials are provided for solar cells. Materials science ; Nanotechnology ; Nanomaterials ; Metal-organic framework ; Perovskite (structure) ; Solar energy ; Energy transformation ; Organic solar cell ; Hybrid solar cell ; Energy conversion efficiency ; Optoelectronics ; Chemical engineering ; Chemistry ; Polymer solar cell ; Engineering ; Electrical engineering Metal-Organic Frameworks: Synthesis and Applications ; Perovskite Materials and Applications ; Advanced Nanomaterials in Catalysis
Authors: Ali Hashemi | Sana Bougueroua | Marie‐Pierre Gaigeot | Evgeny A. Pidko
Source: ChemRxiv (preprint, 2023-09-01)
Field: Materials Science — Materials Chemistry

High-Throughput Reactivity Exploration for Extended Databases of Transition Metal Catalysts A method is introduced for the automated reactivity exploration of extended in silico databases of transition metal catalysts. The proposed workflow is designed to tackle two key challenges for bias-free mechanistic explorations on large databases of catalysts: (1) automated exploration of the chemical space around each catalyst with unique structural and chemical features and (2) automated analysis of the resulting large chemical datasets. To address these challenges we have extended the application of our previously developed ReNeGate method for bias-free reactivity exploration and implemented an automated analysis procedure to identify the classes of reactivity patterns within specific catalyst groups. Our procedure applied to an extended series of representative Mn(I) pincer complexes revealed correlations between structural and reactive features pointing to new channels for catalyst transformation under the reaction conditions. Such an automated high-throughput virtual screening of systematically generated hypothetical catalyst datasets opens new opportunities for the design of high performance catalysts as well as an accelerated method for expert bias-free high-throughput in silico reactivity exploration. Reactivity (psychology) ; Workflow ; Catalysis ; Throughput ; Chemical space ; Computer science ; Key (lock) ; Database ; Virtual screening ; Chemistry ; Computational chemistry ; Molecular dynamics ; Organic chemistry Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Semantic Web and Ontologies
Authors: Michael Craig | Felix Kleuker | Michal Bajdich | Max García‐Melchor
Source: Catalysis Science & Technology (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

FEFOS: a method to derive oxide formation energies from oxidation states Herein we report an interpretable, computationally efficient method to forecast formation energies from oxidation states of binary oxides from unary oxide entries in Materials Project. This new method is envisioned to guide inverse catalyst design. Unary operation ; Inverse ; Oxide ; Binary number ; Catalysis ; Materials science ; Chemical engineering ; Computer science ; Nanotechnology ; Mathematical optimization ; Chemistry ; Mathematics ; Metallurgy ; Engineering ; Discrete mathematics ; Arithmetic ; Geometry ; Organic chemistry Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: Chulgi Nathan Hong | Audrey B. Crom | Jeremy I. Feldblyum | Maria R. Lukatskaya
Source: ChemRxiv (preprint, 2023-01-19)
Field: Chemistry — Inorganic Chemistry

Metal–Organic Frameworks for Fast Electrochemical Energy Storage: Mechanisms and Opportunities Electrochemical energy storage devices are typically based on materials of inorganic nature which require high temperature synthesis and frequently feature scarce and/or toxic elements. Organic-based materials on the other hand can provide an attractive alternative, potentially yielding sustainable, safe, and cost-effective energy storage devices based on abundant elements (e.g. C, N, O, S, and H). However, attempts to incorporate organic and coordination compounds so far have led to sub-par cycling stability and charging rates due to insufficient structural and (electro)chemical stability, low electrical conductivity, and reduced performance at industrially relevant device scales. In recent years metal–organic frameworks (MOFs) have gained attention as having the potential to rival or even supersede traditional energy storage materials. Functional properties such as electronic or ionic conductivity can be incorporated into these materials by judicious design of their constituent inorganic and organic building blocks. However, full realization of the potential of MOFs for electrochemical energy storage requires joint expertise from distinct fields. In particular, bridges must be formed between electrochemists and synthetic and material chemists to establish the unified approach necessary to develop MOF-based energy storage devices exhibiting competitive performance. Electrochemical energy storage ; Energy storage ; Metal-organic framework ; Electrochemistry ; Nanotechnology ; Electrochemical energy conversion ; Supercapacitor ; Materials science ; Process engineering ; Chemistry ; Electrode ; Engineering Metal-Organic Frameworks: Synthesis and Applications ; Polyoxometalates: Synthesis and Applications ; Supercapacitor Materials and Fabrication
Authors: Vyshnavi Vennelakanti | Irem Kilic | Gianmarco Terrones | Chenru Duan | Heather J. Kulik
Source: ChemRxiv (preprint, 2023-11-27)
Field: Materials Science — Materials Chemistry

Machine Learning Prediction of the Experimental Transition Temperature of Fe(II) Spin-Crossover Complexes Spin crossover (SCO) complexes are materials that exhibit changes in spin state in response to external stimuli with potential applications in molecular electronics. It is challenging to know a priori how to design ligands to achieve the delicate balance of entropic and enthalpic contributions needed to tailor a transition temperature close to room temperature. We leverage the SCO complexes from the previously curated SCO-95 data set [Vennelakanti et al. J. Chem. Phys. 159, 024120 (2023)] to train three ML models for transition temperature (T1/2) prediction, using graph-based revised autocorrelations as features. We perform feature selection using random forest-ranked recursive feature addition (RF-RFA) to identify the features essential to model transferability. Of the ML models considered, the full feature set random forest (RF) and recursive feature addition RF models perform best, achieving moderate correlation to experimental T1/2 values. We then compare ML T1/2 predictions to those from three previously identified best- performing density functional approximations (DFAs) which accurately predict SCO behavior across SCO-95, finding that the ML models predict T1/2 more accurately than the best-performing DFAs. In addition, we study ML model predictions on the set of 18 SCO complexes for which only estimated T1/2 values are available. Upon excluding outliers from this set, the RF-RFA RF model shows strong correlation to estimated T1/2 values with a Pearson’s r of 0.82. In contrast, DFA-predicted T1/2 values have large errors and show no correlation to estimated T1/2 values over the same set of complexes. Overall, our study demonstrates reasonable performance of ML models in comparison to some of the best-performing DFAs, and we expect ML models to improve further as larger data sets of SCO complexes are curated and become available for model training. Leverage (statistics) ; Random forest ; Outlier ; Correlation ; Computer science ; Feature selection ; Crossover ; Statistical physics ; Algorithm ; Artificial intelligence ; Mathematics ; Physics Machine Learning in Materials Science ; Magnetism in coordination complexes ; CO2 Reduction Techniques and Catalysts
Authors: Viacheslav Shkirskiy | Frédéric Kanoufi
Source: ChemRxiv (preprint, 2023-02-21)
Field: Materials Science — Materials Chemistry

Reflective microscopy for mechanistic insights in corrosion research Reflective microscopy (RM) is a robust, label free optical imaging technique that allows fast operando measurements of structural changes on metal interfaces at nanoscale in a wide field. Based on the analysis of the reflected light, RM can be simply understood as “video camera” to produce optical photographs of studied interfaces and thus, it has been used for many years as a complementary tool for the visual inspection. However, recent developments in the optical models and refining the experimental design provided means for the quantitative conversion of reflected light intensities into the variations in roughness, thickness of surface films, chemical composition etc., all indispensable for the surface sciences. This review highlights recent advances and contemporary challenges in the methodological developments of RM specifically tailored for the corrosion research. Nanoscopic scale ; Materials science ; Microscopy ; Optical microscope ; Surface finish ; Nanotechnology ; Optics ; Surface roughness ; Corrosion ; Field (mathematics) ; Computer science ; Metallurgy ; Physics ; Composite material Anodic Oxide Films and Nanostructures ; Photoacoustic and Ultrasonic Imaging ; Force Microscopy Techniques and Applications
Authors: Alexander Mendoza-Acosta | Suleyka Torres-Romero | Martín Orozco | Manuel Pérez Cota | Ricarda L. Basurto | Luis L. Galaviz
Source: Frontiers in Chemical Engineering (article, 2023-07-20)
Field: Materials Science — Materials Chemistry

Three basic open access software tools for academic analysis of photocatalytic particles There is currently great interest in photocatalytic degradation technologies of pollutants in industrial effluents. This is due to the need to reduce the environmental pollution generated by the textile industry’s high demand of clothing for fast fashion; in addition to severe environmental problems, this also generates social problems. Since the catalysts of this type of processes are usually nanoparticles of metal oxides such as zinc and titanium, it is necessary to promote research into the synthesis and evaluation of photocatalysts. Therefore, this article describes three free basic access tools for the academic analysis of nanoparticles, from experimental design to representation, using the study of kinetics and particle size analysis. After pre-selecting easily accessible software, it was found that RStudio, J-Image, and Vesta are very useful programs for the analysis of nanoparticles in the respective areas of statistical processing, image analysis, and three-dimensional representation. Photocatalysis ; Computer science ; Software ; Representation (politics) ; Process engineering ; Environmental science ; Engineering ; Chemistry ; Catalysis ; Political science Nanoparticles: synthesis and applications ; TiO2 Photocatalysis and Solar Cells
Authors: Liqun Kang | Ruoyu Xu | Jay Hon Cheung Yan | Fernando Cacho‐Nerin | Juila E. Parker | Christopher S. Allen | Feng Ryan Wang
Source: ChemRxiv (preprint, 2023-10-26)
Field: Materials Science — Surfaces, Coatings and Films

Quantification and visualisation statistics of Pt cathode degradation via correlative electron and X-ray imaging Catalytic degradation in proton exchange membrane fuel cells (PEMFCs) has always been a challenging subject. Despite the monumental research on the subject1-6, the complex relationship between the catalyst deactivation mechanisms and electrochemical degradation remains obscure owing to the heterogenous nature of the catalyst structure. Here we report a correlative electron and X-ray imaging method that enables a multi-modal and multi-length-scale quantification and visualisation of Pt degradation mechanisms in the oxygen reduction reaction (ORR) for proton exchange membrane fuel cell (PEMFC). This technique provides statistical information of up to 107 Pt nanoparticle with 2-dimensional (2D) statistical distribution of Pt content and nanoparticle number in length scales from 2 nm to 20 µm. With the accelerated durability test (ADT) under an O2-saturated 0.1 M HClO4 electrolyte between 0.4 and 1.2 V vs. reversible hydrogen electrode (RHE), the statistical results reveal that the carbon supported Pt nanoparticle catalyst experiences 1) 1:1.5 ratio between the type I degradation mechanisms, Pt dissolution and particle detachment, and type II, Ostwald ripening and particle agglomeration; 2) electrocatalytic activity losses of 33.4% and 41.8% for type I and II degradations, respectively; and 3) macro-scale Pt redistribution from Pt dense regions to sparse regions at an average distance of 1.6 ± 0.9 µm. Proton exchange membrane fuel cell ; Catalysis ; Electrochemistry ; Electrolyte ; Dissolution ; Nanoparticle ; Materials science ; Ostwald ripening ; Degradation (telecommunications) ; Chemical engineering ; Chemistry ; Electrode ; Nanotechnology ; Computer science ; Physical chemistry ; Organic chemistry Electron and X-Ray Spectroscopy Techniques ; X-ray Spectroscopy and Fluorescence Analysis ; Advanced Electron Microscopy Techniques and Applications
Authors: K. Srividya | Seelam Pichi Reddy | Kurra Hari Prasad | Naga Sai Rama Krishna Thati | Kilari Snehita | Unnam Sai Pranay | Naga Venkata Sai Ram Yellapragada
Source: Annales de Chimie Science des Matériaux (article, 2023-02-28)
Field: Chemistry — Inorganic Chemistry

Optimization of Process Parameters for Preparation of Lanthanum Hexa-Aluminate Powders Using Combinatorial Approach of Taguchi-GRA and ACO Methods This work focuses on selection of optimal process parameters for the preparation of Lanthanum Hexa-aluminate (LHA) nanoparticles using chemical precipitation and filtration process.Multi response optimization is performed using Taguchi-GRA combinatorial approach using the process parameters such as Temperature (A), Time (B) and Composition (C).The results showed that % composition has the largest effect on hardness, while the Calcination Temperature is the most important factor in ultimate compression strength.In GRA analysis, the combined effect of hardness and ultimate compression strength is considered and the optimum combination is identified (A1B2C2).The percentage of the contribution was most important factor affecting hardness performance (36.58%).Based on the GRA results a regression equation is generated and optimized using ACO technique followed by preparation and characterization of powders.For the powders, prepared FESEM/EDS analysis were done and observed that average grain size of the particle is 85nm. HEXA ; Taguchi methods ; Lanthanum ; Aluminate ; Process (computing) ; Materials science ; Chromatography ; Process engineering ; Computer science ; Engineering ; Chemistry ; Composite material ; Organic chemistry ; Crystallography Pigment Synthesis and Properties ; Thermal and Kinetic Analysis ; X-ray Diffraction in Crystallography
Authors: Kota Tsujikawa | Junji Hyodo | Susumu Fujii | Kazuki Takahashi | Shusuke Kasamatsu | Yoshihiro Yamazaki
Source: Research Square (preprint, 2023-11-21)
Field: Materials Science — Materials Chemistry

Overcoming the H+ concentration-conductivity trade-off with heavily Sc-doped soft perovskite oxides Conductivity ; Electrolyte ; Materials science ; Ionic conductivity ; Perovskite (structure) ; Doping ; Electrochemistry ; Chemical stability ; Proton ; Chemical engineering ; Inorganic chemistry ; Analytical Chemistry (journal) ; Chemistry ; Physical chemistry ; Electrode ; Optoelectronics Advancements in Solid Oxide Fuel Cells ; Electronic and Structural Properties of Oxides ; Magnetic and transport properties of perovskites and related materials
Authors: Yaqi He | Xin Liu | Jie Lei | Liang Ma | Xiaoguang Zhang | Hongchuan Wang | Chunchi Lei | Xiaobo Feng | Yang Cao | Yong Gao
Source: Research Square (preprint, 2023-09-20)
Field: Materials Science — Materials Chemistry

Bioactive VS4-based sonosensitizer for robust chemodynamic, sonodynamic and osteogenic therapy of infected bone defects Abstract Most bone defects caused by bone disease or trauma are accompanied by infection, and there is a high risk of infection spreading and defect expansion. Clinical treatments include local debridement and systemic administration of antibiotics still have a high failure rate. Therefore, it is desired to develop a high efficiency strategy against these two possible consequences. In this study, an ultrasound responsive vanadium tetrasulfide-loaded MXene (VSM) Schottky junction is constructed for rapid methicillin-resistant staphylococcus aureus (MRSA) clearance and bone regeneration. Due to the peroxidase (POD)-like activity of VS 4 and the abundant Schottky junctions, VSM has high electron-hole separation efficiency and a decreased band gap, exhibiting a strong chemodynamic and sonodynamic antibacterial efficiency of 94.03%. Under the stimulation of medical dose ultrasound, the steady release of vanadium element promotes the osteogenic differentiation of human bone marrow mesenchymal stem cells (hBMSCs). The in vivo application of VSM in infected femoral plateau bone defects of rats also has a great therapeutic effect, eliminating MRSA infection, inhibiting inflammation and improving bone regeneration. The present work successfully develops an ultrasound responsive VS 4 -based versatile sonosensitizer for robust effective antibacterial and osteogenic therapy of infected bone defects. Sonodynamic therapy ; In vivo ; Regeneration (biology) ; Materials science ; Bone healing ; Biomedical engineering ; Mesenchymal stem cell ; Cancer research ; Medicine ; Cell biology ; Pathology ; Ultrasound ; Surgery ; Biology ; Radiology MXene and MAX Phase Materials ; Nanoplatforms for cancer theranostics ; Bone Tissue Engineering Materials
Authors: Alessandra Toniato | Alain C. Vaucher | Marzena Maria Lehmann | Torsten Luksch | Philippe Schwaller | Marco Stenta | Teodoro Laino
Source: Chemistry of Materials (article, 2023-10-27)
Field: Materials Science — Materials Chemistry

Fast Customization of Chemical Language Models to Out-of-Distribution Data Sets The world is on the verge of a new industrial revolution, and language models are poised to play a pivotal role in this transformative era. Their ability to offer intelligent insights and forecasts has made them a valuable asset for businesses seeking a competitive advantage. The chemical industry, in particular, can benefit significantly from harnessing their power. Since 2016 already, language models have been applied to tasks such as predicting reaction outcomes or retrosynthetic routes. While such models have demonstrated impressive abilities, the lack of publicly available data sets with universal coverage is often the limiting factor for achieving even higher accuracies. This makes it imperative for organizations to incorporate proprietary data sets into their model training processes to improve their performance. So far, however, these data sets frequently remain untapped as there are no established criteria for model customization. In this work, we report a successful methodology for retraining language models on reaction outcome prediction and single-step retrosynthesis tasks, using proprietary, nonpublic data sets. We report a considerable boost in accuracy by combining patent and proprietary data in a multidomain learning formulation. This exercise, inspired by a real-world use case, enables us to formulate guidelines that can be adopted in different corporate settings to customize chemical language models easily. Computer science ; Personalization ; Retraining ; Asset (computer security) ; Pruning ; Artificial intelligence ; Machine learning ; Data science ; World Wide Web ; Business ; Computer security Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Software Engineering Research
Authors: Debashish Sur | Howie Joress | Jason Hattrick‐Simpers | John R. Scully
Source: arXiv (Cornell University) (preprint, 2023-02-20)
Field: Materials Science — Materials Chemistry

A High Throughput Aqueous Passivation Testing Methodology for Compositionally Complex Alloys using Scanning Droplet Cell Compositionally complex alloy systems containing more than five principal elements allow exploring a wide range of compositions, processing, and structural variables with the hope for identifying unique properties. Such opportunities also apply to designing materials for improved corrosion resistance, regulated by a self-healing passive film. Such a rich landscape in reactivity and protectivity demands the search for high-throughput experimental testing workflows to uncover key metrics, indicative of superior properties. In this communication, one such methodology is demonstrated for evaluating passivation performance of a combinatorial library of Al0.7-x-yCoxCryFe0.15Ni0.15 thin film alloys in deaerated 0.1 mol/L H2SO4(aq), using a scanning droplet cell. Passivation ; Workflow ; Throughput ; Corrosion ; Materials science ; Alloy ; Aqueous solution ; Scanning electron microscope ; Computer science ; Nanotechnology ; Chemical engineering ; Metallurgy ; Composite material ; Chemistry ; Database ; Engineering ; Telecommunications ; Physical chemistry Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion ; Semiconductor materials and devices