jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Wandria Dallas
Source: Critical Care Nursing Clinics of North America (article, 2023-02-10)
Field: Materials Science — Materials Chemistry

Neurocritical Care Nursing Management of Stroke ENCODE ; Neurointensive care ; Representation (politics) ; Creativity ; Medicine ; Graph ; Topology (electrical circuits) ; Selection (genetic algorithm) ; Artificial intelligence ; Computer science ; Data science ; Theoretical computer science ; Intensive care medicine ; Psychology ; Engineering Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Rough Sets and Fuzzy Logic
Authors: J. D. Langcaster | D. S. Balint | M. R. Wenman
Source: arXiv (Cornell University) (preprint, 2023-03-15)
Field: Materials Science — Metals and Alloys

Adapting U-Net for linear elastic stress estimation in polycrystal Zr microstructures A variant of the U-Net convolutional neural network architecture is proposed to estimate linear elastic compatibility stresses in a-Zr (hcp) polycrystalline grain structures. Training data was generated using VGrain software with a regularity alpha of 0.73 and uniform random orientation for the grain structures and ABAQUS to evaluate the stress welds using the finite element method. The initial dataset contains 200 samples with 20 held from training for validation. The network gives speedups of around 200x to 6000x using a CPU or GPU, with signifcant memory savings, compared to finite element analysis with a modest reduction in accuracy of up to 10%. Network performance is not correlated with grain structure regularity or texture, showing generalisation of the network beyond the training set to arbitrary Zr crystal structures. Performance when trained with 200 and 400 samples was measured, finding an improvement in accuracy of approximately 10% when the size of the dataset was doubled. Convolutional neural network ; Finite element method ; Crystallite ; Artificial neural network ; Microstructure ; Linear elasticity ; Texture (cosmology) ; Computer science ; Algorithm ; Materials science ; Structural engineering ; Artificial intelligence ; Composite material ; Engineering ; Image (mathematics) ; Metallurgy Hydrogen embrittlement and corrosion behaviors in metals ; Nuclear Materials and Properties ; Machine Learning in Materials Science
Authors: Cefe López
Source: Advanced Materials (article, 2023-06-01)
Field: Materials Science — Materials Chemistry

Artificial Intelligence and Advanced Materials (Adv. Mater. 23/2023) Artificial Intelligence The search for new advanced materials has been a slow process, in which testing numerous compositions, morphologies etc. for their properties slows down discovery. High-throughput screening methods can greatly benefit from incorporating artificial intelligence, notably machine learning, techniques. These two routes combine in a cycle of materials science and technology progress reviewed by Cefe López in article number 2208683. Materials science ; Nanotechnology ; Process (computing) ; Artificial intelligence ; Throughput ; Computer science Machine Learning in Materials Science ; Additive Manufacturing and 3D Printing Technologies ; Advanced ceramic materials synthesis
Authors: Ri He | Hongyu Wu | Xuejian Qin | Xue‐Jiao Chen | Zhicheng Zhong
Source: arXiv (Cornell University) (preprint, 2023-09-28)
Field: Materials Science — Materials Chemistry

Pressure-induced one-dimensional oxygen ion diffusion channel in lead-apatite Recently, Lee et al. claimed that the experimental observation of room-temperature ambient-pressure superconductivity in a Cu-doped lead-apatite (Pb10-xCux(PO4)6O). The study revealed the Cu doping induces a chemical pressure, resulting in a structural contraction of one-dimensional Cu-O-Cu atomic column. This unique structure promotes a one-dimensional electronic conduction channel along the c-axis mediated by the O atoms, which may be related to superconductivity. These O atoms occupy 1/4 of the equivalent positions along the c-axis and exhibit a low diffusion activation energy of 0.8 eV, indicating the possibility of diffusion between these equivalent positions. Here, using machine-learning based deep potential, we predict the pressure-induced fast diffusion of 1/4-occupied O atoms along the one-dimensional channel in Pb10(PO4)6O at 500 K, while the frameworks of Pb triangles and PO4 tetrahedrons remain stable. The calculation results also indicate Cu doping can provide appropriate effective chemical pressure, indicating the one-dimensional ion diffusion channel may appear in Pb9Cu(PO4)6O, even at ambient pressure. Our finding shows that the one-dimensional ions diffusion channel may be an important factor to affects the fabrication and electrical measurement of doped lead-apatite. Apatite ; Doping ; Diffusion ; Ion ; Ambient pressure ; Analytical Chemistry (journal) ; Chemistry ; Superconductivity ; Materials science ; Mineralogy ; Condensed matter physics ; Thermodynamics ; Optoelectronics ; Physics Advanced Thermoelectric Materials and Devices ; Advanced Condensed Matter Physics ; X-ray Diffraction in Crystallography
Authors: Kazuki SEKINE | Atsuo HIRANO | Akiyuki Takahashi | Tomohisa Kumagai | Akiyoshi Nomoto
Source: Keisan Rikigaku Koenkai koen ronbunshu/Keisan Rikigaku Kouenkai kouen rombunshuu (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Study on diffusion of self-interstitial atoms and its activation energies in &lt;i&gt;α&lt;/i&gt;-iron using ANN potential Neutron irradiation enhances the formation of vacancies and Self Interstitial Atoms (SIAs), which cause the material embrittlement as a consequence of the interaction with dislocations. The vacancies and SIAs spontaneously form their clusters due to their self-diffusion. In this study, we calculated the activation energies and the minimum energy paths of five different diffusion mechanisms (Event 1 ~ 5) in α-iron by means of the Nudged Elastic Band (NEB) method with an Artificial Neural Network (ANN) interatomic potential. The result of the NEB analysis suggests that the activation energy of the Event 1 mechanism is the lowest and is 0.34 eV whereas an EAM potential gives the activation energy as 0.31 eV. The ANN potential can also reproduce well the activation energies and the minimum energy paths of the other mechanisms as well as the first principles calculations. The reaction pathway analysis also tells us that the Event 2 ~ 5 mechanisms can be reproduced by the multiple reptations of the Event 1 mechanism. Animal science ; Biology Fusion materials and technologies ; Nuclear Materials and Properties ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Quan Dong Nguyen | Emma Lejeune
Source: arXiv (Cornell University) (preprint, 2023-08-30)
Field: Materials Science — Materials Chemistry

Segmenting mechanically heterogeneous domains via unsupervised learning From biological organs to soft robotics, highly deformable materials are essential components of natural and engineered systems. These highly deformable materials can have heterogeneous material properties, and can experience heterogeneous deformations with or without underlying material heterogeneity. Many recent works have established that computational modeling approaches are well suited for understanding and predicting the consequences of material heterogeneity and for interpreting observed heterogeneous strain fields. In particular, there has been significant work towards developing inverse analysis approaches that can convert observed kinematic quantities (e.g., displacement, strain) to material properties and mechanical state. Despite the success of these approaches, they are not necessarily generalizable and often rely on tight control and knowledge of boundary conditions. Here, we will build on the recent advances (and ubiquity) of machine learning approaches to explore alternative approaches to detect patterns in heterogeneous material properties and mechanical behavior. Specifically, we will explore unsupervised learning approaches to clustering and ensemble clutering to identify heterogeneous regions. Overall, we find that these approaches are effective, yet limited in their abilities. Through this initial exploration (where all data and code is published alongside this manuscript), we set the stage for future studies that more specifically adapt these methods to mechanical data. Computer science ; Artificial intelligence ; Cluster analysis ; Unsupervised learning ; Machine learning ; Set (abstract data type) Machine Learning in Materials Science ; Robot Manipulation and Learning ; Soft Robotics and Applications
Authors: Yen‐Ju Wu | Yibin Xu
Source: Micromachines (article, 2023-01-11)
Field: Materials Science — Materials Chemistry

Data-Driven Design of Transparent Thermal Insulating Nanoscale Layered Oxides Predicting the interfacial thermal resistance (ITR) for various material systems is a time-consuming process. In this study, we applied our previously proposed ITR machine learning models to discover the material systems that satisfy both high transparency and low thermal conductivity. The selected material system of TiO2/SiO2 shows a high ITR of 26.56 m2K/GW, which is in good agreement with the predicted value. The nanoscale layered TiO2/SiO2 thin films synthesized by sputtering exhibits ultralow thermal conductivity (0.21 W/mK) and high transparency (>90%, 380−800 nm). The reduction of the thermal conductivity is achieved by the high density of the interfaces with a high ITR rather than the change of the intrinsic thermal conductivity. The thermal conductivity of TiO2 is observed to be 1.56 W/mK with the film thickness in the range of 5−50 nm. Furthermore, the strong substrate dependence is confirmed as the thermal conductivity of the nanoscale layered TiO2/SiO2 thin films on quartz glass is three times lower than that on Si. The proposed TiO2/SiO2 composites have higher transparency and robustness, good adaptivity to electronics, and lower cost than the current transparent thermal insulating materials such as aerogels and polypropylene. The good agreement of the experimental ITR with the prediction and the low thermal conductivity of the layered thin films promise this strategy has great potential for accelerating the development of transparent thermal insulators. Thermal conductivity ; Materials science ; Nanoscopic scale ; Thin film ; Composite material ; Sputtering ; Thermal ; Thermal resistance ; Nanotechnology ; Thermodynamics Thermal properties of materials ; Advanced Thermoelectric Materials and Devices ; Aerogels and thermal insulation
Authors: Veera Sundararaghavan | Megna Shah | Jeff Simmons
Source: arXiv (Cornell University) (preprint, 2023-01-11)
Field: Materials Science — Materials Chemistry

Towards Microstructural State Variables in Materials Systems The vast combination of material properties seen in nature are achieved by the complexity of the material microstructure. Advanced characterization and physics based simulation techniques have led to generation of extremely large microstructural datasets. There is a need for machine learning techniques that can manage data complexity by capturing the maximal amount of information about the microstructure using the least number of variables. This paper aims to formulate dimensionality and state variable estimation techniques focused on reducing microstructural image data. It is shown that local dimensionality estimation based on nearest neighbors tend to give consistent dimension estimates for natural images for all p-Minkowski distances. However, it is found that dimensionality estimates have a systematic error for low-bit depth microstructural images. The use of Manhattan distance to alleviate this issue is demonstrated. It is also shown that stacked autoencoders can reconstruct the generator space of high dimensional microstructural data and provide a sparse set of state variables to fully describe the variability in material microstructures. Curse of dimensionality ; Microstructure ; Variable (mathematics) ; Dimension (graph theory) ; State variable ; Generator (circuit theory) ; Computer science ; Characterization (materials science) ; Set (abstract data type) ; Minkowski space ; Algorithm ; Artificial intelligence ; Materials science ; Mathematics ; Power (physics) ; Physics ; Nanotechnology ; Geometry Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications
Authors: Shreerup Goswami | Samuel A. Käser | Raymond J. Bemish | Markus Meuwly
Source: arXiv (Cornell University) (preprint, 2023-09-10)
Field: Materials Science — Materials Chemistry

On the Effect of Aleatoric and Epistemic Errors on the Learnability and Quality of NN-based Potential Energy Surfaces The effect of noise in the input data for learning potential energy surfaces (PESs) based on neural networks for chemical applications is assessed. Noise in energies and forces can result from aleatoric and epistemic errors in the quantum chemical reference calculations. Statistical (aleatoric) noise arises for example due to the need to set convergence thresholds in the self consistent field (SCF) iterations whereas systematic (epistemic) noise is due to, {\it inter alia}, particular choices of basis sets in the calculations. The two molecules considered here as proxies are H$_{2}$CO and HONO which are examples for single- and multi-reference problems, respectively, for geometries around the minimum energy structure. For H$_2$CO it is found that adding noise to energies with magnitudes representative of single-point calculations does not deteriorate the quality of the final PESs whereas increasing the noise level commensurate with electronic structure calculations for more complicated, e.g. metal-containing, systems is expected to have a more notable effect. However, the effect of noise on the forces is more noticeable. On the other hand, for HONO which requires a multi-reference treatment, a clear correlation between model quality and the degree of multi-reference character as measured by the $T_1$ amplitude is found. It is concluded that for chemically "simple" cases the effect of aleatoric and epistemic noise is manageable without evident deterioration of the trained model - although the quality of the forces is important. However, considerably more care needs to be exercised for situations in which multi-reference effects are present. Noise (video) ; Quality (philosophy) ; Energy (signal processing) ; Statistical physics ; Set (abstract data type) ; Computer science ; Mathematics ; Physics ; Artificial intelligence ; Quantum mechanics ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Topic Modeling
Authors: Simon Axelrod | Eugene I. Shakhnovich | Rafael Gómez‐Bombarelli
Source: arXiv (Cornell University) (preprint, 2023-02-22)
Field: Materials Science — Materials Chemistry

Mapping the space of photoswitchable ligands and photodruggable proteins with computational modeling Light-activated drugs are a promising way to localize biological activity and minimize side effects. However, their development is complicated by the numerous photophysical and biological properties that must be simultaneously optimized. To accelerate the design of photoactive drugs, we describe a procedure that combines ligand-protein docking with chemical property prediction based on machine learning (ML). We apply this procedure to 58 proteins and 9,000 photo-drug candidates based on azobenzene cis-trans isomerism. We find that most proteins display a preference for trans isomers over cis, and that the binding affinities of nominally active/inactive pairs are in fact highly correlated. These findings have significant value for photopharmacology research, and reinforce the need for virtual screening to identify compounds with rare desirable properties. Further, we combine our procedure with quantum chemical validation to identify promising candidates for the photoactive inhibition of PARP1, an enzyme that is over-expressed in cancer cells. The top compounds are predicted to have long-lived active forms, differential bioactivity, and absorption in the near-infrared therapeutic window. Azobenzene ; Virtual screening ; Docking (animal) ; Combinatorial chemistry ; Ligand (biochemistry) ; Affinities ; Chemistry ; Active site ; Drug discovery ; Computational biology ; Enzyme ; Molecule ; Stereochemistry ; Biology ; Biochemistry ; Organic chemistry Photochromic and Fluorescence Chemistry ; Click Chemistry and Applications ; Chemical Synthesis and Analysis
Authors: X. L. Lei | Weike Ye | Joseph H. Montoya | Tim Mueller | Linda Hung | Jens Hummelshoej
Source: arXiv (Cornell University) (preprint, 2023-10-28)
Field: Materials Science — Materials Chemistry

The Role of Reference Points in Machine-Learned Atomistic Simulation Models This paper introduces the Chemical Environment Modeling Theory (CEMT), a novel, generalized framework designed to overcome the limitations inherent in traditional atom-centered Machine Learning Force Field (MLFF) models, widely used in atomistic simulations of chemical systems. CEMT demonstrated enhanced flexibility and adaptability by allowing reference points to exist anywhere within the modeled domain and thus, enabling the study of various model architectures. Utilizing Gaussian Multipole (GMP) featurization functions, several models with different reference point sets, including finite difference grid-centered and bond-centered models, were tested to analyze the variance in capabilities intrinsic to models built on distinct reference points. The results underscore the potential of non-atom-centered reference points in force training, revealing variations in prediction accuracy, inference speed and learning efficiency. Finally, a unique connection between CEMT and real-space orbital-free finite element Density Functional Theory (FE-DFT) is established, and the implications include the enhancement of data efficiency and robustness. It allows the leveraging of spatially-resolved energy densities and charge densities from FE-DFT calculations, as well as serving as a pivotal step towards integrating known quantum-mechanical laws into the architecture of ML models. Computer science ; Robustness (evolution) ; Grid ; Density functional theory ; Algorithm ; Force field (fiction) ; Statistical physics ; Theoretical computer science ; Artificial intelligence ; Physics ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Block Copolymer Self-Assembly
Authors: Santiago Zapata-Boada | Francisco J. Heredia | Klever Mera
Source:  (article, 2023-11-05)
Field: Chemistry — Analytical Chemistry

Predicting Corrosion Rate in Oil Wells Using First Principles and Machine Learning Corrosion in oil wells results from the complex interaction between various factors such as chemical composition, temperature, pressure, and flow rates of the fluids involved, and poses a significant challenge to the oil and gas industry. Accurate prediction of corrosion rates is essential for operational efficiency, proactive maintenance, and risk mitigation. Traditional methods for corrosion rate prediction rely on empirical models, which are often reactive and inaccurate. Machine learning models can provide a more accurate prediction of corrosion rate1, 2. The objective of this study is to develop a hybrid model that leverages the physics-based understanding of corrosion processes (first principles) along with the data-driven capabilities of machine learning. By merging these two approaches, we seek to improve the accuracy and adaptability of corrosion rate predictions to enable timely maintenance, reduce downtime, and enhance safety and operational efficiency. Our methodology started with the collection of historical data on corrosion rates, well operational parameters, pipe parameters, and fluid compositions from a representative set of oil wells (over 1,000 records). We cleaned and transformed the data, handling missing values and outliers appropriately. To integrate first principles, we employed the corrosion model NORSOK3, based on fundamental chemical and physical principles, to calculate theoretical corrosion rates. Then, machine learning models, including Random Forest, Gradient Boosting, and Support Vector Machines, were trained on the same dataset to predict corrosion rates, and the best-performing model was selected using metrics such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). Finally, we combined the predictions from the first principles and machine learning model using an ensemble approach. The resulting hybrid model was then evaluated on a testing dataset. Corrosion ; Computer science ; Petroleum engineering ; Artificial intelligence ; Geology ; Materials science ; Metallurgy Petroleum Processing and Analysis ; Drilling and Well Engineering ; Structural Integrity and Reliability Analysis
Authors: Christoph M. Arndt | Anna‐Lena Sahlberg | Anil K. Patnaik | Frank Beyrau | Joakim Bood | Paul S. Hsu | Thomas Seeger
Source: Applied Optics (article, 2023-02-13)
Field: Chemistry — Spectroscopy

Laser Applications to Chemical, Security, and Environmental Analysis: introduction to the feature issue The eighteenth topical meeting on Laser Applications to Chemical, Security, and Environmental Analysis (LACSEA) was held in Vancouver, Canada from 11-15 July 2022, as part of the Optica Optical Sensors and Sensing Congress in a hybrid format allowing on-site and online attendance. The meeting featured a broad range of distinguished papers focusing on recent advances in laser and optical spectroscopy. A total of 52 contributed and invited papers were presented during the meeting, including topics such as photo-acoustic spectroscopy, imaging, non-linear technologies, frequency combs, remote sensing, environmental monitoring, aerosols, combustion diagnostics, hypersonic flow diagnostics, nuclear diagnostics, fs/ps applications, and machine learning and computational sensing. Laser ; Optics ; Remote sensing ; Computer science ; Photoacoustic spectroscopy ; Spectroscopy ; Environmental science ; Physics ; Geology Spectroscopy and Laser Applications ; Laser Design and Applications ; Atmospheric and Environmental Gas Dynamics
Authors: Nico Unglert | Jesús Carrete | Lívia B. Pártay | Georg K. H. Madsen
Source: arXiv (Cornell University) (preprint, 2023-08-22)
Field: Materials Science — Materials Chemistry

Neural-Network Force Field Backed Nested Sampling: Study of the Silicon p-T Phase Diagram Nested sampling is a promising method for calculating phase diagrams of materials, however, the computational cost limits its applicability if ab-initio accuracy is required. In the present work, we report on the efficient use of a neural-network force field in conjunction with the nested-sampling algorithm. We train our force fields on a recently reported database of silicon structures and demonstrate our approach on the low-pressure region of the silicon pressure-temperature phase diagram between 0 and \SI{16}{GPa}. The simulated phase diagram shows a good agreement with experimental results, closely reproducing the melting line. Furthermore, all of the experimentally stable structures within the investigated pressure range are also observed in our simulations. We point out the importance of the choice of exchange-correlation functional for the training data and show how the meta-GGA r2SCAN plays a pivotal role in achieving accurate thermodynamic behaviour using nested-sampling. We furthermore perform a detailed analysis of the exploration of the potential energy surface and highlight the critical role of a diverse training data set. Phase diagram ; Sampling (signal processing) ; Silicon ; Computer science ; Force field (fiction) ; Artificial neural network ; Field (mathematics) ; Phase (matter) ; Statistical physics ; Range (aeronautics) ; Diagram ; Materials science ; Algorithm ; Artificial intelligence ; Physics ; Mathematics ; Optoelectronics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications