jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Anuroop Sriram | Sihoon Choi | Xiaohan Yu | Brabson, Logan M. | Abhishek Das | Zachary W. Ulissi | Matt Uyttendaele | Andrew J. Medford | David S. Sholl
Source: arXiv (Cornell University) (preprint, 2023-11-01)
Field: Chemistry — Inorganic Chemistry

The Open DAC 2023 Dataset and Challenges for Sorbent Discovery in Direct Air Capture New methods for carbon dioxide removal are urgently needed to combat global climate change. Direct air capture (DAC) is an emerging technology to capture carbon dioxide directly from ambient air. Metal-organic frameworks (MOFs) have been widely studied as potentially customizable adsorbents for DAC. However, discovering promising MOF sorbents for DAC is challenging because of the vast chemical space to explore and the need to understand materials as functions of humidity and temperature. We explore a computational approach benefiting from recent innovations in machine learning (ML) and present a dataset named Open DAC 2023 (ODAC23) consisting of more than 38M density functional theory (DFT) calculations on more than 8,400 MOF materials containing adsorbed $CO_2$ and/or $H_2O$. ODAC23 is by far the largest dataset of MOF adsorption calculations at the DFT level of accuracy currently available. In addition to probing properties of adsorbed molecules, the dataset is a rich source of information on structural relaxation of MOFs, which will be useful in many contexts beyond specific applications for DAC. A large number of MOFs with promising properties for DAC are identified directly in ODAC23. We also trained state-of-the-art ML models on this dataset to approximate calculations at the DFT level. This open-source dataset and our initial ML models will provide an important baseline for future efforts to identify MOFs for a wide range of applications, including DAC. Sorbent ; Adsorption ; Metal-organic framework ; Computer science ; Open source ; Density functional theory ; Materials science ; Co2 removal ; Carbon dioxide ; Nanotechnology ; Environmental science ; Chemistry ; Physical chemistry ; Computational chemistry Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies ; Machine Learning in Materials Science
Authors: Abraham Rojas Zuniga | Sam Bakhtiari | Ke Wang | Victor M. Calo | Mariano Iannuzzi
Source: Journal of The Electrochemical Society (article, 2023-05-22)
Field: Materials Science — Metals and Alloys

Revisiting the Copson Curve Using Data Science This work applies machine learning to holistically interrogate the influence of metallurgical factors, such as chemical composition, heat treatment, and mechanical properties, on the stress corrosion cracking resistance of corrosion-resistant alloys. Particularly, we explored the effect of nickel in reducing the stress corrosion cracking susceptibility in boiling magnesium chloride, arguably a controversial topic since Copson’s 1959 seminal publication. This paper offers insights into the synergies of nickel with other alloying elements that ultimately impact the resistance to stress corrosion cracking. Furthermore, a more detailed description of statistical patterns in the so-called Copson curve is provided. Stress corrosion cracking ; Corrosion ; Metallurgy ; Boiling ; Cracking ; Magnesium ; Materials science ; Nickel ; Stress (linguistics) ; Environmental stress cracking ; Work (physics) ; Composite material ; Thermodynamics ; Physics Hydrogen embrittlement and corrosion behaviors in metals ; Machine Learning in Materials Science ; Non-Destructive Testing Techniques
Authors: P. Shahbaz | Sumit Sharma | S. Ajori
Source: Physica Scripta (article, 2023-10-20)
Field: Materials Science — Materials Chemistry

Machine learning approach on the prediction of mechanical characteristics of pristine, boron doped and nitrogen doped graphene Abstract Machine Learning (ML), a subset of Artificial Intelligence has been widely applied in various domains, but it has only just begun to be employed in the field of engineering. In the present investigation, various ML algorithms and artificial neural network (ANN) structures are used for the first time to predict the mechanical properties of pristine, boron-doped, and nitrogen-doped graphene while also taking into account the effects of various types of vacancy defects. Fracture strain, Ultimate Tensile Strength (UTS), and Young’s modulus are all predicted. ML technique reduces the computational cost and time required to find out mechanical properties of these materials. The training dataset for the ML models is developed using Molecular Dynamics (MD) simulations. It was shown that defects and doping both had an adverse effect on mechanical characteristics. While ANN, LASSO, and LASSO Lars have all performed quite well at predicting these features, pipeline polynomial regression has performed best across all datasets. New insights on the research of mechanical characteristics utilizing cutting-edge computational techniques are provided by the discoveries in this research. Materials science ; Graphene ; Artificial neural network ; Doping ; Artificial intelligence ; Machine learning ; Ultimate tensile strength ; Boron ; Enhanced Data Rates for GSM Evolution ; Fracture (geology) ; Computer science ; Pipeline (software) ; Algorithm ; Nanotechnology ; Composite material ; Physics ; Optoelectronics Graphene research and applications ; Machine Learning in Materials Science ; Boron and Carbon Nanomaterials Research
Authors: Dominik Dold | Derek Aranguren van Egmond
Source: arXiv (Cornell University) (preprint, 2023-04-11)
Field: Materials Science — Materials Chemistry

Differentiable graph-structured models for inverse design of lattice materials Architected materials possessing physico-chemical properties adaptable to disparate environmental conditions embody a disruptive new domain of materials science. Fueled by advances in digital design and fabrication, materials shaped into lattice topologies enable a degree of property customization not afforded to bulk materials. A promising venue for inspiration toward their design is in the irregular micro-architectures of nature. However, the immense design variability unlocked by such irregularity is challenging to probe analytically. Here, we propose a new computational approach using graph-based representation for regular and irregular lattice materials. Our method uses differentiable message passing algorithms to calculate mechanical properties, therefore allowing automatic differentiation with surrogate derivatives to adjust both geometric structure and local attributes of individual lattice elements to achieve inversely designed materials with desired properties. We further introduce a graph neural network surrogate model for structural analysis at scale. The methodology is generalizable to any system representable as heterogeneous graphs. Differentiable function ; Computer science ; Theoretical computer science ; Lattice (music) ; Graph ; Personalization ; Inverse ; Algorithm ; Mathematics ; Geometry Machine Learning in Materials Science ; Topology Optimization in Engineering
Authors: Sukriti Manna | Henry Chan | Avishek Ghosh | Tamoghna Chakraborti | Subramanian K. R. S. Sankaranarayanan
Source: ChemRxiv (preprint, 2023-04-21)
Field: Materials Science — Materials Chemistry

Understanding and Control of Zener Pinning via Phase Field and Ensemble Learning Zener pinning refers to the dispersion of fine particles which influences grain size distribution \textit{via} movement of grain boundaries in a polycrystalline material. Grain size distribution in polycrystals has a significant impact on their properties including physical, chemical, mechanical, and optical to name a few. We explore the use of Phase-field modeling and machine-learning techniques to understand and improve the control of grain size distribution \textit{via} Zener pinning in polycrystalline materials. We develop a machine learning model that determines the relative importance of various parameters to exercise microstructure control \textit{via} Zener pinning. Our workflow combines high-throughput phase-field simulations and machine learning to address the computational bottlenecks associated with large-scale simulations as well as identify features necessary for microstructure control in polycrystals. A random forest (RF) regression model was developed to predict grain sizes based on five Phase-field model parameters, achieving an average prediction error of 0.72 nm for the training data and 1.44 nm for the test data. The importance of the input parameters is analyzed using the SHapley Additive exPlanations (SHAP) approach which reveals that diffusivity, volume fraction, and particle diameter are the most important parameters in determining the final grain size. These findings will allow us to select the best second-phase particles, optimize grain size distributions and thus design microstructures with the desired properties. Zener diode ; Grain size ; Materials science ; Zener pinning ; Phase (matter) ; Microstructure ; Field (mathematics) ; Condensed matter physics ; Statistical physics ; Machine learning ; Artificial intelligence ; Algorithm ; Computer science ; Physics ; Superconductivity ; Mathematics ; Flux pinning ; Composite material ; Voltage Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; Theoretical and Computational Physics
Authors: Wanqing Chen | M.J. Abedin | Titon Barua | Meysam Sharifzadeh Mirshekarloo | Sally El Meragawi | Mainak Majumder
Source: Small (article, 2023-08-30)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Customized Production of Holey Graphene Oxides via a Continuous Flow Process Continuous flow manufacturing is an innovative technology mainly applied in the chemical and pharmaceutical industries that is progressively being adapted to the manufacturing of nanomaterials to overcome the challenge of reproducing a product with consistent characteristics at a large scale. Here, a flow photochemical system is designed and prototyped for the synthesis of holey graphene oxides (hGOs). Compared to existing methods for the synthesis of hGO, the process is fast, highly scalable, and controllable. Through a combination of rigorous data analysis using machine learning algorithms on transmission electron microscope images and systematic studies of process parameters, it is demonstrated that characteristics of the produced hGO (i.e., porosity and pore size) are remarkably reproducible to the extent that it can be predicted by empirical models of processing-property correlations. Depending on the tailored nanopore structures, the synthesized hGOs out-performed GO in a range of applications that can benefit from the nanoporous two-dimensional (2D) sheets such as in supercapacitors, gas adsorption, and nanofiltration membranes. These results are significant in offering new perspectives on the low-cost industrialization of 2D nanomaterials. Materials science ; Nanoporous ; Graphene ; Nanomaterials ; Nanotechnology ; Nanopore ; Porosity ; Process engineering ; Scalability ; Process (computing) ; Computer science ; Composite material Supercapacitor Materials and Fabrication ; Graphene research and applications ; Solar-Powered Water Purification Methods
Authors: Ziyong Chen | Vivian Wing‐Wah Yam
Source: Journal of the American Chemical Society (article, 2023-10-24)
Field: Materials Science — Materials Chemistry

Encoding Hole–Particle Information in the Multi-Channel MolOrbImage for Machine-Learned Excited-State Energies of Large Photofunctional Materials We present a novel class of one-electron multi-channel molecular orbital images (MolOrbImages) designed for the prediction of excited-state energetics in conjunction with the state-of-the-art VGG-type machine-learning architecture. By representing hole and particle states in the excitation process as channels of MolOrbImages, the revised VGG model achieves excellent prediction accuracy for both low-lying singlet and triplet states, with mean absolute errors (MAEs) of <0.08 and <0.1 eV for QM9 molecules and large photofunctional materials with up to 560 atoms, respectively. Remarkably, the model demonstrates exceptional performance (MAE < 1 kcal/mol) for the T<sub>1</sub> state of QM9 molecules, making it a non-system-specific model that approaches chemical accuracy. The general rules attained, for instance, the improved performance with well-defined MO energies and the reduced overfitting concern via the inclusion of physically insightful hole-particle information, provide invaluable guidelines for the further design of orbital-based descriptors targeting molecular excited states. Chemistry ; Excited state ; Particle (ecology) ; Encoding (memory) ; State (computer science) ; Channel (broadcasting) ; Atomic physics ; Artificial intelligence ; Computer network ; Algorithm Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Lukáš Kývala | Andrea Angeletti | Cesare Franchini | Christoph Dellago
Source: The Journal of Physical Chemistry C (article, 2023-12-05)
Field: Materials Science — Materials Chemistry

Diffusion and Coalescence of Phosphorene Monovacancies Studied Using High-Dimensional Neural Network Potentials The properties of two-dimensional materials are strongly affected by defects that are often present in considerable numbers. In this study, we investigate the diffusion and coalescence of monovacancies in phosphorene using molecular dynamics (MD) simulations accelerated by high-dimensional neural network potentials. Trained and validated with reference data obtained with density functional theory (DFT), such surrogate models provide the accuracy of DFT at a much lower cost, enabling simulations on time scales that far exceed those of first-principles MD. Our microsecond long simulations reveal that monovacancies are highly mobile and move predominantly in the zigzag rather than armchair direction, consistent with the energy barriers of the underlying hopping mechanisms. In further simulations, we find that monovacancies merge into energetically more stable and less mobile divacancies following different routes that may involve metastable intermediates. Phosphorene ; Metastability ; Coalescence (physics) ; Microsecond ; Zigzag ; Merge (version control) ; Physics ; Molecular dynamics ; Statistical physics ; Density functional theory ; Chemical physics ; Computer science ; Biological system ; Biology ; Condensed matter physics ; Quantum mechanics Machine Learning in Materials Science ; 2D Materials and Applications ; MXene and MAX Phase Materials
Authors: Armand Barbot | Riccardo Gatti
Source: Computational Materials Science (article, 2023-09-18)
Field: Materials Science — Materials Chemistry

Unsupervised learning for structure detection in plastically deformed crystals Unsupervised learning ; Computer science ; Colloidal crystal ; Deformation (meteorology) ; Artificial intelligence ; Materials science ; Particle (ecology) ; Crystal structure ; Scale (ratio) ; Biological system ; Algorithm ; Pattern recognition (psychology) ; Statistical physics ; Colloid ; Crystallography ; Physics ; Chemistry ; Geology ; Composite material Machine Learning in Materials Science ; Mineral Processing and Grinding ; Geochemistry and Geologic Mapping
Authors: S. Mondal | Rahul Samanta | Sahadeb Shit | Arindam Biswas | Atul Bandyopadhyay | Rudra Sankar Dhar | Gurudas Mandal
Source: International Journal for Multiscale Computational Engineering (article, 2023-12-26)
Field: Materials Science — Biomaterials

CORROSION PREDICTION OFMAGNESIUM IMPLANT USING MULTISCALE MODELING BASED ON MACHINE LEARNING ALGORITHMS Significant thoughtful research is really necessary to improve the patient outcomes and reduce the social and financial burdens associated with implant failure. The primary focus of the researchers is to minimize the major implant failure due to corrosion attributed to making orthopedic surgery safer and more effective. Hence, a critical review has been done in this present article on the various multiscale modelings based on machine learning algorithms (MLAs) to predict the corrosion behavior of magnesium (Mg) alloy implants. According to the best of the authors' knowledge, all the available multiscale modelings tools, such as artificial neural network (ANN), least absolute shrinkage and selection operator (LASSO) regression model, multiple linear regression and random forest regression (RFR) models, etc., are methodically presented and discussed in detailed here for the prediction of corrosion mechanism. Subsequently, various multiscale model tools and assessment metrics for models have been thoroughly compared and criticized for better understanding and optimizing of the corrosion behavior of implants. The comparison indicates that the RFR model may be the best option, whereas the LASSO regression model and ANNs show inefficient performance for the prediction of corrosion behavior. Apart from the multiscale modeling approach, the authors have also explored the physiology and properties of alloys, bone implant, immune and tissue system, and the corrosion control mechanisms of Mg alloy. Finally, the present review on multiscale modeling approach and assessment metrics models will enhance the knowledge and understanding of the corrosion behavior of Mg alloy for implant application. Corrosion ; Computer science ; Materials science ; Algorithm ; Artificial intelligence ; Machine learning ; Metallurgy Magnesium Alloys: Properties and Applications ; Metal and Thin Film Mechanics ; Orthopaedic implants and arthroplasty
Authors: Alexandra C. Cordova | James N. Dodds | Han-Hsuan D Tsai | Dillon Lloyd | Alina T. Roman‐Hubers | Fred A. Wright | Weihsueh A. Chiu | Thomas J. McDonald | Rui Zhu | Galen Newman | Ivan Rusyn
Source: Environmental Toxicology and Chemistry (article, 2023-08-02)
Field: Chemistry — Analytical Chemistry

Application of Ion Mobility Spectrometry–Mass Spectrometry for Compositional Characterization and Fingerprinting of a Library of Diverse Crude Oil Samples Exposure characterization of crude oils, especially in time-sensitive circumstances such as spills and disasters, is a well-known analytical chemistry challenge. Gas chromatography-mass spectrometry is commonly used for "fingerprinting" and origin tracing in oil spills; however, this method is both time-consuming and lacks the resolving power to separate co-eluting compounds. Recent advances in methodologies to analyze petroleum substances using high-resolution analytical techniques have demonstrated both improved resolving power and higher throughput. One such method, ion mobility spectrometry-mass spectrometry (IMS-MS), is especially promising because it is both rapid and high-throughput, with the ability to discern among highly homologous hydrocarbon molecules. Previous applications of IMS-MS to crude oil analyses included a limited number of samples and did not provide detailed characterization of chemical constituents. We analyzed a diverse library of 195 crude oil samples using IMS-MS and applied a computational workflow to assign molecular formulas to individual features. The oils were from 12 groups based on geographical and geological origins: non-US (1 group), US onshore (3), and US Gulf of Mexico offshore (8). We hypothesized that information acquired through IMS-MS data would provide a more confident grouping and yield additional fingerprint information. Chemical composition data from IMS-MS was used for unsupervised hierarchical clustering, as well as machine learning-based supervised analysis to predict geographic and source rock categories for each sample; the latter also yielded several novel prospective biomarkers for fingerprinting of crude oils. We found that IMS-MS data have complementary advantages for fingerprinting and characterization of diverse crude oils and that proposed polycyclic aromatic hydrocarbon biomarkers can be used for rapid exposure characterization. Environ Toxicol Chem 2023;42:2336-2349. © 2023 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC. Ion-mobility spectrometry ; Mass spectrometry ; Chemistry ; Fingerprint (computing) ; Chemometrics ; Petroleum ; Workflow ; Chromatography ; Computer science ; Database ; Artificial intelligence Petroleum Processing and Analysis ; Mass Spectrometry Techniques and Applications ; Hydrocarbon exploration and reservoir analysis
Authors: Jerome Quenum | Iryna V. Zenyuk | Daniela Ushizima
Source: Journal of Imaging (article, 2023-05-31)
Field: Materials Science — Surfaces, Coatings and Films

Lithium Metal Battery Quality Control via Transformer–CNN Segmentation Lithium metal battery (LMB) has the potential to be the next-generation battery system because of its high theoretical energy density. However, defects known as dendrites are formed by heterogeneous lithium (Li) plating, which hinders the development and utilization of LMBs. Non-destructive techniques to observe the dendrite morphology often use X-ray computed tomography (XCT) to provide cross-sectional views. To retrieve three-dimensional structures inside a battery, image segmentation becomes essential to quantitatively analyze XCT images. This work proposes a new semantic segmentation approach using a transformer-based neural network called TransforCNN that is capable of segmenting out dendrites from XCT data. In addition, we compare the performance of the proposed TransforCNN with three other algorithms, U-Net, Y-Net, and E-Net, consisting of an ensemble network model for XCT analysis. Our results show the advantages of using TransforCNN when evaluating over-segmentation metrics, such as mean intersection over union (mIoU) and mean Dice similarity coefficient (mDSC), as well as through several qualitatively comparative visualizations. Segmentation ; Computer science ; Artificial intelligence ; Artificial neural network ; Transformer ; Battery (electricity) ; Pattern recognition (psychology) ; Voltage ; Electrical engineering Electron and X-Ray Spectroscopy Techniques ; Advanced X-ray and CT Imaging ; Advanced Neural Network Applications
Authors: Honghua Huang | Jianfei Luo | Feng Gan | Philip K. Hopke
Source: Applied Sciences (article, 2023-07-23)
Field: Chemistry — Analytical Chemistry

Two Revised Deep Neural Networks and Their Applications in Quantitative Analysis Based on Near-Infrared Spectroscopy Small data sets make developing calibration models using deep neural networks difficult because it is easy to overfit the system. We developed two deep neural network architectures by revising two existing network architectures: the U-Net and the attention mechanism. The major changes were to use 1D convolutional layers to replace the fully connected layers. We also designed and combined average pooling and maximum pooling in our revised networks, respectively. We applied these revised network architectures to three publicly available data sets and the resulting calibration models can generate acceptable results for general quantitative analysis. It also generated rather good results for data sets that concern calibration transfer. It demonstrates that constructing network architectures by properly revising existing successful network architectures may provide additional choices in the exploration of the application of deep neural network in analytical chemistry. Overfitting ; Pooling ; Computer science ; Artificial neural network ; Artificial intelligence ; Deep neural networks ; Convolutional neural network ; Deep learning ; Calibration ; Data mining ; Machine learning ; Mathematics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies