jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Daiki Kido | Masao Kimura | Kiyotaka ASAKURA
Source: Vacuum and Surface Science (article, 2023-07-09)
Field: Chemistry — Analytical Chemistry

The Challenge of EXAFS Analysis Independent from the Experience of a Scientist — Development of Constrained Thorough Search Method — EXAFS is powerful to obtain the structure information around the X-ray absorbing atom at the atomic size level. EXAFS is usually analyzed by the curve fitting method, one of the least square methods to optimize structure parameters. However, the curve fitting method demands the experience of scientists when they analyze the EXAFS of materials with complex structures. In particular, the following two items make the analysis difficult especially for beginners ; 1. the dependence on the initial selection of the fitting parameters, 2. fitting errors caused by their correlation. We developed a new method, called as Constrained Thorough Search (CTS) method to solve these problems. The CTS method searches the whole parameter space under constrained conditions to extract the structure candidates. We describe the details of the CTS method with their results. We also applied the machine learning method to CTS results which allows us to conduct the analysis more objectively. Extended X-ray absorption fine structure ; Computer science ; Curve fitting ; Atom (system on chip) ; Space (punctuation) ; Algorithm ; Mathematical optimization ; Applied mathematics ; Mathematics ; Machine learning ; Physics ; Optics Spectroscopy and Chemometric Analyses ; X-ray Spectroscopy and Fluorescence Analysis ; X-ray Diffraction in Crystallography
Authors: Patxi Fernandez-Zelaia | Sébastien Dryepondt | Amirkoushyar Ziabari | Michael Kirka
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Materials Chemistry

Self-Supervised Learning of Spatiotemporal Thermal Signatures in Additive Manufacturing Using Reduced Order Physics Models and Transformers Transformer ; Thermal ; Artificial intelligence ; Computer science ; Machine learning ; Physics ; Engineering ; Electrical engineering ; Thermodynamics Machine Learning in Materials Science
Authors: Shuyao Lin | Luis Casillas‐Trujillo | Ferenc Tasnádi | Lars Hultman | P.H. Mayrhofer | Davide G. Sangiovanni | Nikola Koutná
Source: arXiv (Cornell University) (preprint, 2023-09-02)
Field: Materials Science — Materials Chemistry

Machine-learning potentials for nanoscale simulations of deformation and fracture: example of TiB$_2$ ceramic Machine-learning interatomic potentials (MLIPs) offer a powerful avenue for simulations beyond length and timescales of ab initio methods. Their development for investigation of mechanical properties and fracture, however, is far from trivial since extended defects -- governing plasticity and crack nucleation in most materials -- are too large to be included in the training set. Using TiB$_2$ as a model ceramic material, we propose a strategy for fitting MLIPs suitable to simulate mechanical response of monocrystals until fracture. Our MLIP accurately reproduces ab initio stresses and failure mechanisms during room-temperature uniaxial tensile deformation of TiB$_2$ at the atomic scale ($\approx{10}^3$ atoms). More realistic tensile tests (low strain rate, Poisson's contraction) at the nanoscale ($\approx{10}^4$--10$^6$ atoms) require MLIP up-fitting, i.e. learning from additional ab initio configurations. Consequently, we elucidate trends in theoretical strength, toughness, and crack initiation patterns under different loading directions. To identify useful environments for further up-fitting, i.e., making the MLIP applicable to a wider spectrum of simulations, we asses transferability to other deformation conditions and phases not explicitly trained on. Materials science ; Ultimate tensile strength ; Ceramic ; Ab initio ; Nucleation ; Fracture toughness ; Deformation (meteorology) ; Nanoscopic scale ; Ab initio quantum chemistry methods ; Transferability ; Plasticity ; Fracture (geology) ; Interatomic potential ; Atomic units ; Composite material ; Condensed matter physics ; Molecular dynamics ; Nanotechnology ; Thermodynamics ; Computational chemistry ; Machine learning ; Computer science ; Physics ; Chemistry Machine Learning in Materials Science ; Microstructure and mechanical properties ; Advanced Materials Characterization Techniques
Authors: Yong Liu | Christopher E. Carlton | Qi Zhang
Source: Proceedings - International Symposium for Testing and Failure Analysis (article, 2023-11-08)
Field: Materials Science — Surfaces, Coatings and Films

“Full” Automatic TEM from Imaging to Metrology Abstract TEM automation is dedicated to providing high-volume, fast and precise TEM data to enable semiconductor manufacturers to develop and control fabrication processes. It automates TEM operation and measurement procedures, and minimizes the requirements of user training. Traditionally, recipes are required for each specific structure, and Automatic TEM Imaging and Metrology had individual recipes for each structure, respectively and separately. TEM professionals review structures and TEM images and manually assign imaging and metrology recipes one by one. After metrology, it is tedious to manually check mis-measurement of hundreds of TEM images and Critical Dimensions (CDs). In this study, An Intelligent TEM automation process was developed by machine learning process that could “fully” automatically conduct TEM Analysis from Imaging to Metrology starting right after TEM holder insertion without human intervention. It is not limited to automatically prepared TEM samples and also works for manually prepared samples. TEM Auto Metrology is carried out automatically right after Auto Imaging in the background. After that, Auto capturing of mis-measurements can be carried out automatically to catch problematic metrology images and CDs as an invaluable complement. As a result, the whole workflow was streamlined, with better efficiency and accuracy achieved. Metrology ; Automation ; Workflow ; Computer science ; Process (computing) ; Artificial intelligence ; Engineering drawing ; Engineering ; Mechanical engineering ; Optics Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications
Authors: Yunhui Jang | Seul Lee | Sung Soo Ahn
Source: arXiv (Cornell University) (preprint, 2023-12-04)
Field: Materials Science — Materials Chemistry

A Simple and Scalable Representation for Graph Generation Recently, there has been a surge of interest in employing neural networks for graph generation, a fundamental statistical learning problem with critical applications like molecule design and community analysis. However, most approaches encounter significant limitations when generating large-scale graphs. This is due to their requirement to output the full adjacency matrices whose size grows quadratically with the number of nodes. In response to this challenge, we introduce a new, simple, and scalable graph representation named gap encoded edge list (GEEL) that has a small representation size that aligns with the number of edges. In addition, GEEL significantly reduces the vocabulary size by incorporating the gap encoding and bandwidth restriction schemes. GEEL can be autoregressively generated with the incorporation of node positional encoding, and we further extend GEEL to deal with attributed graphs by designing a new grammar. Our findings reveal that the adoption of this compact representation not only enhances scalability but also bolsters performance by simplifying the graph generation process. We conduct a comprehensive evaluation across ten non-attributed and two molecular graph generation tasks, demonstrating the effectiveness of GEEL. Computer science ; Scalability ; Adjacency matrix ; Theoretical computer science ; Graph Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Text Analysis Techniques
Authors: Guan Yu Soh | Zahra Montazeri
Source: arXiv (Cornell University) (preprint, 2023-11-07)
Field: Materials Science — Polymers and Plastics

Neural Appearance Model for Cloth Rendering The realistic rendering of woven and knitted fabrics has posed significant challenges throughout many years. Previously, fiber-based micro-appearance models have achieved considerable success in attaining high levels of realism. However, rendering such models remains complex due to the intricate internal scatterings of hundreds of fibers within a yarn, requiring vast amounts of memory and time to render. In this paper, we introduce a new framework to capture aggregated appearance by tracing many light paths through the underlying fiber geometry. We then employ lightweight neural networks to accurately model the aggregated BSDF, which allows for the precise modeling of a diverse array of materials while offering substantial improvements in speed and reductions in memory. Furthermore, we introduce a novel importance sampling scheme to further speed up the rate of convergence. We validate the efficacy and versatility of our framework through comparisons with preceding fiber-based shading models as well as the most recent yarn-based model. Rendering (computer graphics) ; Yarn ; Computer science ; Adaptability ; Ray tracing (physics) ; Tracing ; Artificial neural network ; Artificial intelligence ; Computer graphics (images) ; Engineering ; Mechanical engineering Textile materials and evaluations ; Computer Graphics and Visualization Techniques ; 3D Shape Modeling and Analysis
Authors: Mike Entwistle | Zeno Schätzle | Paolo Andrea Erdman | Jan Hermann | Frank Noé
Source: Nature Communications (article, 2023-01-17)
Field: Materials Science — Materials Chemistry

Electronic excited states in deep variational Monte Carlo Obtaining accurate ground and low-lying excited states of electronic systems is crucial in a multitude of important applications. One ab initio method for solving the Schrödinger equation that scales favorably for large systems is variational quantum Monte Carlo (QMC). The recently introduced deep QMC approach uses ansatzes represented by deep neural networks and generates nearly exact ground-state solutions for molecules containing up to a few dozen electrons, with the potential to scale to much larger systems where other highly accurate methods are not feasible. In this paper, we extend one such ansatz (PauliNet) to compute electronic excited states. We demonstrate our method on various small atoms and molecules and consistently achieve high accuracy for low-lying states. To highlight the method's potential, we compute the first excited state of the much larger benzene molecule, as well as the conical intersection of ethylene, with PauliNet matching results of more expensive high-level methods. Ansatz ; Wave function ; Variational Monte Carlo ; Excited state ; Quantum Monte Carlo ; Physics ; Statistical physics ; Electronic structure ; Ground state ; Monte Carlo method ; Quantum mechanics ; Mathematics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Quantum many-body systems
Authors: Annie M. Westerlund | Bente Barge | Lewis Mervin | Samuel Genheden
Source: ChemRxiv (preprint, 2023-05-28)
Field: Materials Science — Materials Chemistry

Data-driven approaches for identifying hyperparameters in multi-step retrosynthesis Multi-step retrosynthesis problem can be solved by a search algorithm, such as Monte Carlo tree search (MCTS). The performance of multistep retrosynthesis, as measured by a trade-off in search time and route solvability, therefore depends on the hyperparameters of the search algorithm. In this paper, we demonstrated the effect of three MCTS hyperparameters (number of iterations, tree depth, and tree width) on metrics such as Linear integrated speed-accuracy score (LISAS) and Inverse efficiency score which consider both route solvability and search time. This exploration was conducted by employing three data-driven approaches, namely a systematic grid search, Bayesian optimization over an ensemble of molecules to obtain static MCTS hyperparameters, and a machine learning approach to dynamically predict optimal MCTS hyperparameters given an input target molecule. With the obtained results on the internal dataset, we demonstrated that it is possible to identify a hyperparameter sets which outperform the current AiZynthFinder default setting and appeared optimal across a variety of target input molecules, both on proprietary and public datasets. The settings identified with the in-house dataset reached a solvability of 93% and median search time of 151s for the in-house dataset, and a 74% solvability and 114s for the ChEMBL dataset. These numbers can be compared to the current default settings which solved 85% and 73% during a median time of 110s and 84s, for in-house and ChEMBL, respectively. Hyperparameter ; Hyperparameter optimization ; Computer science ; Tree (set theory) ; chEMBL ; Machine learning ; Artificial intelligence ; Search tree ; Search algorithm ; Data mining ; Algorithm ; Mathematics ; Support vector machine Machine Learning in Materials Science ; Mass Spectrometry Techniques and Applications ; Computational Drug Discovery Methods
Authors: Weihao Tang | Wenfeng Hu | Chuang Li | Jinjing Wu | Hong Liu | Chao Wang | Xiaochuan Luo | Rongnian Tang
Source: Research Square (preprint, 2023-11-13)
Field: Chemistry — Analytical Chemistry

Improving the performance of a spectral model to estimate total nitrogen content with small soil samples sizes Abstract The application of near-infrared spectrum spectroscopy (NIRS) for rapid quan titative analysis of soil total nitrogen (STN) is of great significance to recycling nitrogen in the ecosystem and crops growth. However, collecting thousands of soil samples and chemical annotation are impracticable, more importantly a deviation from NIRS advantages of rapid, costless and nondestructive. To improve the estimation performance and reduce uncertainty of the model under small sample sizes, new solutions from soil spectral data augmentation and model fusion were investigated. Specifically, 123 Latosols samples were collected, and decomposed by particle sizes to generate more data from multiple scales. Subsequently, based on the augmented data, multi-modal fusion methods were implemented. The results showed that the proposed method increased the scale of spectral data, mined more STN-related spectral information, improved estimation accuracy, and reduced uncertainty. The fusion model based on augmented data yielded optimal estimated results (root mean square error (RMSE) = 0.082g.kg−1) R2 = 0.779, and a ratio of performance to interquartile distance (RP IQ) = 3.428) on the validation set. In addition, we explored the impact of different model fusion strategies and proposed soil data augmentation method for estimating. From a 10-folds validation, the R2 cv of the weighted fusion model with using augmented data was increased by 0.285, and RP IQ was 1.015 improved than model constructed from conventional machine learning (ML) technique and pristine limited data (R2 cv = 0.432, RP IQ = 2.294). Therefore, in the scenario of using NIRS to build a rapid and accurate STN predictive models, the proposed method shows great potential in improving model reliability under small sample sizes. Mean squared error ; Fusion ; Sensor fusion ; Soil science ; Computer science ; Mathematics ; Statistics ; Environmental science ; Artificial intelligence Spectroscopy and Chemometric Analyses ; Metabolomics and Mass Spectrometry Studies ; Soil Geostatistics and Mapping
Authors: T.S Kumanan | A. Sofi
Source: SSRN Electronic Journal (article, 2023-01-01)
Field: Materials Science — Biomaterials

Progression of Bio-Modified Adobes with Derivatives of Cassava Periderm and Clay Brick Waste by Experimental and Probabilistic Prediction Models Brick ; Probabilistic logic ; Environmental science ; Waste management ; Biochemical engineering ; Engineering ; Mathematics ; Civil engineering ; Statistics Advanced Cellulose Research Studies ; Lignin and Wood Chemistry ; Dyeing and Modifying Textile Fibers
Authors: Eslam S. El-Mokadem | Christian Heumann | Fabian Suhrke | Kathrin Meindl
Source: Opto-Electronics Review (article, 2023-05-08)
Field: Materials Science — Surfaces, Coatings and Films

A probabilistic approach for approximation of optical and opto-electronic properties of an opto-semiconductor wafer under consideration of measuring inaccuracy and model uncertainty This paper presents a probabilistic machine learning approach to approximate wavelength values for unmeasured positions on an opto-semiconductor wafer after epitaxy. Insufficient information about optical and opto-electronic properties may lead to undetected specification violations and, consequently, to yield loss or may cause product quality issues. Collection of information is restricted because physical measuring points are expensive and in practice samples are only drawn from 120 specific positions. The purpose of the study is to reduce the risk of uncertainties caused by sampling and measuring inaccuracy and provide reliable approximations. Therefore, a Gaussian process regression is proposed which can determine a point estimation considering measuring inaccuracy and further quantify estimation uncertainty. For evaluation, the proposed method is compared with radial basis function interpolation using wavelength measurement data of 6-inch InGaN wafers. Approximations of these models are evaluated with the root mean square error. Gaussian process regression with radial basis function kernel reaches a root mean square error of 0.814 nm averaged over all wafers. A slight improvement to 0.798 nm could be achieved by using a more complex kernel combination. However, this also leads to a seven times higher computational time. The method further provides probabilistic intervals based on means and dispersions for approximated positions. Semiconductor ; Wafer ; Probabilistic logic ; Electronic engineering ; Semiconductor device ; Computer science ; Materials science ; Optoelectronics ; Statistical physics ; Reliability engineering ; Physics ; Engineering ; Nanotechnology ; Artificial intelligence Optical Coatings and Gratings
Authors: Daniel A. Heller | Mijin Kim | Chen Chen | YuHuang Wang | Ming Zheng | Declan Gwynne
Source: ECS Meeting Abstracts (article, 2023-08-28)
Field: Materials Science — Materials Chemistry

(Invited) Carbon Nanotube Photoluminescence for Cancer Research and Diagnosis New technologies are needed for the early detection of cancer and the identification of therapeutic vulnerabilities in cancers. Our laboratory develops optical nanosensor technologies using carbon nanotubes, including sensors that identify disease fingerprints in serum, diagnostic implants to facilitate longitudinal detection of cancer biomarkers, and assays for cancer drug development. These methods employ the photoluminescence of single-walled carbon nanotube (SWCNTs) and covalent sp3 quantum well defects on SWCNTs, aka organic color centers (OCCs), that introduce new chemical sensitivities. To introduce new detection technologies using OCC-modified SWCNTs, we developed new supramolecular functionalization methods, as well as new types of optical instrumentation to detect changes in near-infrared emission within biological and clinical samples. We harnessed OCC-modified SWCNTs for the detection of metabolic changes in live cells and tissues, disease biomarkers in situ via implants, and overall disease states, aided by machine learning processes. Carbon nanotube ; Nanosensor ; Nanotechnology ; Photoluminescence ; Materials science ; Cancer detection ; Biosensor ; Cancer ; Computer science ; Optoelectronics ; Medicine ; Internal medicine Carbon Nanotubes in Composites
Authors: Mahmood Mahmoodi‐Eshkaftaki | Mehdi Mahbod | Hamid Reza Ghenaatian
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Chemistry — Analytical Chemistry

Non-Destructive Detection of Biomass Characteristics: Integrating Hyperspectral Imaging and Neural Network Hyperspectral imaging ; Biomass (ecology) ; Artificial neural network ; Pattern recognition (psychology) ; Artificial intelligence ; Remote sensing ; Computer science ; Environmental science ; Geology ; Biology ; Ecology Spectroscopy and Chemometric Analyses
Authors: Duncan Taylor | Caitlin McDonald | Ayesha Khan | Richard Leibbrandt | Adrian Linacre | Russell S. A. Brinkworth
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Materials Chemistry

Towards a Machine-Learning Driven ‘Smart’ PCR Thermocycler Computer science ; Artificial intelligence ; Machine learning Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Thermography and Photoacoustic Techniques
Authors: Sanan H. Khan | Afsar Husain | Mohd Danish | Abdel‐Hamid I. Mourad
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Materials Chemistry

Predicting Projectile Residual Velocities Using an Advanced Artificial Neural Network Model Residual ; Projectile ; Artificial neural network ; Computer science ; Artificial intelligence ; Physics ; Algorithm High-Velocity Impact and Material Behavior ; Electromagnetic Launch and Propulsion Technology ; Advanced Measurement and Detection Methods
Authors: Önder Eyecioğlu | Korhan Kayışlı | Murat Beken
Source: Electric Power Components and Systems (article, 2023-12-13)
Field: Materials Science — Materials Chemistry

Strain Energy Prediction of Single Wall Carbon Nanotubes Using General Regression Neural Network and Adaptive Neuro – Fuzzy Inference System Single Wall Carbon Nanotubes (SWCNTs) play crucial roles in the field of nanotechnology research and applications. Due to their quantum mechanical nature and the intricate structure of SWCNTs, performing direct or indirect experimental processes can be exceedingly challenging. Simulation methods like Tight-Binding Molecular Dynamics (TBMD) can serve as viable alternatives to experimentation. However, it's worth noting that these methods often demand extensive computational runtime. To address this computational time challenge, artificial intelligence algorithms such as the General Regression Neural Network (GRNN) and the Adaptive Neuro Fuzzy Interface System (ANFIS) have been proposed in this study. These models aim to calculate the energetic properties of SWCNTs more efficiently, offering practical and quicker predictive methods with reduced computational workloads. The study's findings demonstrate a strong correlation between the predicted energy values of SWCNTs using GRNN and ANFIS models and the results obtained through TBMD simulations. Consequently, it is believed that these models can be suitable and effective approaches for computing the energetic properties of SWCNTs. Adaptive neuro fuzzy inference system ; Artificial neural network ; Computer science ; Neuro-fuzzy ; Computational model ; Artificial intelligence ; Inference ; Computational intelligence ; Interface (matter) ; Fuzzy logic ; Machine learning ; Fuzzy control system ; Parallel computing Carbon Nanotubes in Composites ; Fuel Cells and Related Materials ; Machine Learning in Materials Science
Authors: Richard Overstreet | Ethan King
Source: OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (article, 2023-01-01)
Field: Chemistry — Inorganic Chemistry

QC-GN2OMS2 The QC-GN2OMS2 neural network was designed to predict mass spectra better than currently available approaches. Fundamentally the neural net is designed to input the structure of a molecule and collision energy value then output a spectra datafile for the end user. This software is able to make predictions orders of magnitude faster (milliseconds) than QC methods and with 50-60% greater accuracy than rules approaches. Computer science Inorganic Chemistry and Materials ; Intermetallics and Advanced Alloy Properties ; Microwave Dielectric Ceramics Synthesis