jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Nicholas Gao | Stephan Günnemann
Source: arXiv (Cornell University) (preprint, 2022-05-30)
Field: Materials Science — Materials Chemistry

Sampling-free Inference for Ab-Initio Potential Energy Surface Networks Recently, it has been shown that neural networks not only approximate the ground-state wave functions of a single molecular system well but can also generalize to multiple geometries. While such generalization significantly speeds up training, each energy evaluation still requires Monte Carlo integration which limits the evaluation to a few geometries. In this work, we address the inference shortcomings by proposing the Potential learning from ab-initio Networks (PlaNet) framework, in which we simultaneously train a surrogate model in addition to the neural wave function. At inference time, the surrogate avoids expensive Monte-Carlo integration by directly estimating the energy, accelerating the process from hours to milliseconds. In this way, we can accurately model high-resolution multi-dimensional energy surfaces for larger systems that previously were unobtainable via neural wave functions. Finally, we explore an additional inductive bias by introducing physically-motivated restricted neural wave function models. We implement such a function with several additional improvements in the new PESNet++ model. In our experimental evaluation, PlaNet accelerates inference by 7 orders of magnitude for larger molecules like ethanol while preserving accuracy. Compared to previous energy surface networks, PESNet++ reduces energy errors by up to 74%. Inference ; Computer science ; Artificial neural network ; Monte Carlo method ; Energy (signal processing) ; Ab initio ; Importance sampling ; Wave function ; Statistical physics ; Algorithm ; Artificial intelligence ; Physics ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Advanced Chemical Physics Studies
Authors: Xiaoxia Yang | Yisheng Gao | Shuhua Zhang | Zhedong Ge | Yucheng Zhou
Source: JOURNAL OF RENEWABLE MATERIALS (article, 2022-01-01)
Field: Chemistry — Organic Chemistry

Research on Rosewood Micro Image Classification Method Based on Feature Fusion and ELM Rosewood is a kind of high-quality and precious wood in China. The correct identification of rosewood species is of great significance to the import and export trade and species identification of furniture materials. In this paper, micro CT was used to obtain the micro images of cross sections, radial sections and tangential sections of 24 kinds of rosewood, and the data sets were constructed. PCA method was used to reduce the dimension of four features including logical binary pattern, local configuration pattern, rotation invariant LBP, uniform LBP. These four features and one feature not reducing dimension (rotation invariant uniform LBP) was fused with Gray Level CoOccurrence Matrix and Tamura features, respectively, a total of five fused features <i>LBP</i>+<i>GLCM</i>+<i>Tamura</i>, <i>LCP</i> +<i>GLCM</i>+<i>Tamura</i>, <img src="https://www.techscience.com/ueditor/files/jrm1.png" height="20px">+<i>GLCM</i>+<i>Tamura</i>, <img src="https://www.techscience.com/ueditor/files/jrm2.png" height="20px">+<i>GLCM</i>+<i>Tamura</i> and <img src="https://www.techscience.com/ueditor/files/jrm3.png" height="20px"> +<i>GLCM</i>+<i>Tamura</i> were obtained. The five fused features were classified by extreme learning machine and BP neural network. The classification effect of feature <img src="https://www.techscience.com/ueditor/files/jrm1.png" height="20px">+<i>GLCM</i>+<i>Tamura</i> combined with extreme learning machine was the best, and the classification accuracy of cross, radial and tangential sections reached 100%, 97.63% and 94.72%, respectively, which is 0.83%, 2.77% and 5.70% higher than that of BP neural network. The classification running time of ELM is less than 1 s, and the classification efficiency is high. In conclusion, the <img src="https://www.techscience.com/ueditor/files/jrm1.png" height="20px">+<i>GLCM</i>+<i>Tamura</i> method combined with extreme learning machine can be used as a quick and accurate classifier, providing an efficient and feasible classification method of rosewood. Artificial intelligence ; Pattern recognition (psychology) ; Extreme learning machine ; Mathematics ; Computer science ; Artificial neural network Wood and Agarwood Research ; Industrial Vision Systems and Defect Detection ; Spectroscopy and Chemometric Analyses
Authors: Francine Gloerfelt‐Tarp | Jos C. Mieog | Mark J. Bigland | Simon Wheeler | William M. Palmer | Tobias Kretzschmar
Source: Journal of Raman Spectroscopy (article, 2022-01-15)
Field: Chemistry — Analytical Chemistry

Predicting tea tree oil distillate composition using portable spectrometric technology Abstract Commercial tea tree oil (TTO) has to conform chemically to the international standard ISO4730: 2017, which defines acceptable ranges for the 15 main terpenoids therein. Lack of in‐house capacity for the technology typically required for terpenoid quantification (GC‐FID) necessitates that Australian TTO producers outsource the analyses to commercial service providers. This is associated with high cost and slow turnaround times, which have been recognized as limiting factors for TTO process optimization in the areas of quality assurance, distillation and blending. This paper describes the testing of two custom portable Raman devices with different lasers (785 and 1064 nm) for the relative quantification of the 15 main TTO components using model‐based predictions. Initial testing showed that the 1064‐nm device provided superior data for TTO predictions. Machine‐learning algorithms were trained with spectral data from the 1064‐nm device and corresponding GC‐FID data from 214 TTO samples. Hold‐over validation (HV) correlations between actual and predicted values using up to 53 unseen samples showed r 2 at or above 0.92 for 10 and at or above 0.95 for 6 of the 15 specified terpenoids. The highly abundant key TTO quality compound terpinen‐4‐ol showed an HV r 2 of 0.96 with a root mean square error (RMSE) of 0.37%, whereas the lowly abundant key compound 1,8‐cineole showed an HV r 2 of 1.00 with a RMSE of 0.13%. These results indicated a strong potential for Raman spectrometry to provide real‐time product quality data at TTO distillation sites enabling direct feedback control and process optimisations. Mean squared error ; Distillation ; Chemistry ; Mathematics ; Raman spectroscopy ; Tea tree oil ; Terpenoid ; Chromatography ; Essential oil ; Statistics ; Stereochemistry Spectroscopy and Chemometric Analyses ; Essential Oils and Antimicrobial Activity ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Simone Venturi | Tiernan Casey
Source: arXiv (Cornell University) (preprint, 2022-04-27)
Field: Materials Science — Materials Chemistry

SVD Perspectives for Augmenting DeepONet Flexibility and Interpretability Deep operator networks (DeepONets) are powerful architectures for fast and accurate emulation of complex dynamics. As their remarkable generalization capabilities are primarily enabled by their projection-based attribute, we investigate connections with low-rank techniques derived from the singular value decomposition (SVD). We demonstrate that some of the concepts behind proper orthogonal decomposition (POD)-neural networks can improve DeepONet's design and training phases. These ideas lead us to a methodology extension that we name SVD-DeepONet. Moreover, through multiple SVD analyses, we find that DeepONet inherits from its projection-based attribute strong inefficiencies in representing dynamics characterized by symmetries. Inspired by the work on shifted-POD, we develop flexDeepONet, an architecture enhancement that relies on a pre-transformation network for generating a moving reference frame and isolating the rigid components of the dynamics. In this way, the physics can be represented on a latent space free from rotations, translations, and stretches, and an accurate projection can be performed to a low-dimensional basis. In addition to flexibility and interpretability, the proposed perspectives increase DeepONet's generalization capabilities and computational efficiencies. For instance, we show flexDeepONet can accurately surrogate the dynamics of 19 variables in a combustion chemistry application by relying on 95% less trainable parameters than the ones of the vanilla architecture. We argue that DeepONet and SVD-based methods can reciprocally benefit from each other. In particular, the flexibility of the former in leveraging multiple data sources and multifidelity knowledge in the form of both unstructured data and physics-informed constraints has the potential to greatly extend the applicability of methodologies such as POD and PCA. Interpretability ; Singular value decomposition ; Computer science ; Generalization ; Projection (relational algebra) ; Flexibility (engineering) ; Rank (graph theory) ; Artificial intelligence ; Basis (linear algebra) ; Artificial neural network ; Operator (biology) ; Algorithm ; Machine learning ; Theoretical computer science ; Mathematics Machine Learning in Materials Science ; Gaussian Processes and Bayesian Inference ; Model Reduction and Neural Networks
Authors: Zhao Li | Qi Zhang | Chang He | Qinglin Chen | Bing J. Zhang
Source: ACS Omega (article, 2022-09-14)
Field: Chemistry — Inorganic Chemistry

Quantitative Structure–Property Relationship Analysis for the Prediction of Propylene Adsorption Capacity in Pure Silicon Zeolites at Various Pressure Levels This work is devoted to the development of quantitative structure-property relationship (QSPR) models using various regression analyses to predict propylene (C<sub>3</sub>H<sub>6</sub>) adsorption capacity at various pressures in zeolites from a topologically diverse International Zeolite Association database. Based on univariate and multilinear regression analysis, the accessible volume and largest cavity diameter are the most crucial factors determining C<sub>3</sub>H<sub>6</sub> uptake at high and low pressures, respectively. An artificial neural network (ANN) model with five structural descriptors is sufficient to predict C<sub>3</sub>H<sub>6</sub> uptake at high pressures. For combined pressures, the prediction of an ANN model with pore size distribution is pleasing. The isosteric heat of adsorption (<i>Q</i> <sub>st</sub>) has a significant impact on the improvement of the prediction of low-pressure gas adsorption, which finely classifies zeolites into high or low C<sub>3</sub>H<sub>6</sub> adsorbers. The conjunction of high-throughput screening and QSPR models contributes to being able to prescreen the database rapidly and accurately for top performers and perform further detailed and time-consuming computational-intensive molecular simulations on these candidates for other gas adsorption applications. Adsorption ; Quantitative structure–activity relationship ; Multilinear map ; Zeolite ; Molecular descriptor ; Artificial neural network ; Volume (thermodynamics) ; Chemistry ; Biological system ; Materials science ; Thermodynamics ; Computer science ; Physical chemistry ; Organic chemistry ; Mathematics ; Catalysis ; Artificial intelligence ; Stereochemistry Zeolite Catalysis and Synthesis ; Metal-Organic Frameworks: Synthesis and Applications ; Catalysis and Oxidation Reactions
Authors: Paulo De Carvalho Gomes | Alexander Crossman | Emily Massey | Jonathan James Stanley Rickard | Pola Goldberg Oppenheimer
Source: Informatics in Medicine Unlocked (article, 2022-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Real-time validation of Surface-Enhanced Raman Scattering substrates via convolutional neural network algorithm Development of next-generation sensor technologies could be advanced by exploiting surface enhanced Raman spectroscopy (SERS) substrates capable of considerably enhancing signal detection of analytes of interest down to single molecule levels. The widespread implementation of SERS, however, requires overcoming many of the existing and emerging challenges related to the enhancement inconsistency from the different parts of substrates, resulting in inconsistent readings. Here, a custom-designed convolutional neural networks (CNN) algorithm is developed to specifically analyse and validate substrates in real-time, providing a rapid output and identification of SERS active structures on each substrate enabling a consistently high signal enhancement. A computational algorithm has been developed to identify regions of high and consistent SERS activity from acquired optical microscopy images of SERS structures in real-time. The CNN model is tested on the inputted optical images and outputs the predicted consistent SERS-active structures or regions overlaid on top of the input image. The optimised CNN model used is a pre-trained neural network (VGG16) with the last layers fine-tuned. This model achieves an accuracy of 90% in classifying our uniquely developed electrohydrodynamically (EHD) fabricated, highly enhancing, SERS-active structures from optical images, enabling a real-time predictive tool for SERS substrates. The CNN output generates a heatmap, which provides feedback on the regions of high SERS activity, overlaid on the optical image to rapidly facilitate the SERS spectral acquisition measurements. Beyond its application for EHD, the versatility of the developed method renders it easily extendable for implementation with further, current and future, fabrication techniques of SERS substrates. The full script run yields an accuracy of 87.5 ± 12.0% for in-situ measurement acquisition, successfully demonstrating a proof-of-concept of rapidly identifying consistent SERS structures via the real-time CNN processing of optical images. Convolutional neural network ; Computer science ; SIGNAL (programming language) ; Raman scattering ; Surface-enhanced Raman spectroscopy ; Artificial neural network ; Substrate (aquarium) ; Artificial intelligence ; Analyte ; Algorithm ; Materials science ; Raman spectroscopy ; Optics ; Chemistry ; Physics Gold and Silver Nanoparticles Synthesis and Applications ; Biosensors and Analytical Detection ; Machine Learning and ELM
Authors: Isabelle Atheaux | Sergey Makarychev‐Mikhailov | Débora Campos Faria | Jesse Farrell | Bruce A. MacKay
Source:  (article, 2022-05-25)
Field: Chemistry — Analytical Chemistry

Produced Water, X-Ray Fluorescence Spectrometry and Machine Learning: How Can They be Connected? Abstract Fast and reliable field-portable chemical analysis of produced waters remains one of the main challenges of scale-risk mitigation, as it enables timely control over scale inhibitor type and dosage. While many analytical methods are potentially applicable to produced waters, most of them lack reliability under field conditions or do not meet increasingly tight cost requirements. X-ray fluorescence (XRF) spectrometry is routinely used in the oil field for analysis of cores, drilling cuttings, and muds, as it provides quick noninvasive detection of many elements simultaneously. Inexpensive portable handheld analyzers typically feature limited element range, sensitivity, and resolution, and are therefore expected to be inferior to benchtop analyzers. The question is whether handheld devices can offer suitable detection limits and accuracy for produced water analysis, despite these limitations. At the same time, one of the major challenges of XRF, the so-called matrix effect, is strong in produced waters and also negatively affects the analysis accuracy. This study demonstrates how multivariate machine-learning (ML) techniques can be applied to the full XRF spectra recorded with a handheld analyzer. ML spectra processing is shown to successfully mitigate matrix effects and enable simultaneous quantification of all ions of interest. Interestingly, key physical (density) and chemical (total dissolved solids and hardness) properties of produced water can also be quantified using ML techniques. In the paper, the experimental protocols are described first, followed by a detailed discussion of the data workflows, which covers the XRF spectra preprocessing, algorithm selection and tuning, and independent validation procedures. Over 50 different ML algorithms are trained on different spectra ranges of a multicomponent calibration dataset, and the three best models are applied to several real-life produced water sample sets for validation. A rigorous error analysis is performed for all ML models. In field samples, the resulting analysis errors (RMSE) are less than 100 mg/L for barium and strontium, less than 150 mg/L for sulfate, and remarkably small/accurate for other ions and properties considering measurement with a handheld device. Computer science ; Spectrum analyzer ; Preprocessor ; Matrix (chemical analysis) ; Chemistry ; Artificial intelligence ; Chromatography Spectroscopy and Chemometric Analyses ; Mineral Processing and Grinding ; Geochemistry and Geologic Mapping
Authors: Haifeng Zhang | Lianzhu Zhou
Source: Wireless Communications and Mobile Computing (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Evaluation of Performance of Chlorinated Polyethylene Using Wireless Network and Artificial Intelligence Technology Chemical enterprises are presently confronted with several difficult issues, including high power consumption, dangerous risk evaluation, and environmental regulation, all of which push industrial and academic institutions to develop new technologies, catalysts, and materials. Chlorinated polyethylene (CPE) is a polymer made by replacing H2 molecules in high density‐(C2H4) n with chloride ions. CPE elastomers are made from a high density‐(C2H4) backbone, and it was chlorinated using a free radical aqueous slurry technique. However, such fundamental polymer characteristics are insufficient to explain the performance characteristics of chlorinated polyethylene elastomers. Artificial intelligence (AI) has had a massive effect on all sections of the chemical sector, with tremendous potential that has revolutionized value supply chains, enhanced efficiency, and opened up new ways to the marketplace. As a result, in this research, we offer a methodology for the performance characterization of chlorinated polyethylene based on artificial intelligence (AI) and wireless network technology. The AI tools can search through enormous databases of known compounds and their attributes, leveraging the data to generate new possibilities. The dataset is first gathered. The chemical characterization is classified using the K ‐nearest neighbor (KNN) technique. This program was created to examine molecule structures and forecast the outcomes of new chemical reactions. Bayesian optimization is used to improve characterization performance. The proposed method will contribute to the future usage of AI in the chemical sector. Computer science ; Characterization (materials science) ; Bayesian network ; Slurry ; Artificial intelligence ; Process engineering ; Biochemical engineering ; Materials science ; Nanotechnology ; Engineering Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Computational Drug Discovery Methods
Authors: Qianxiang Ai | Alexander J. Norquist | Joshua Schrier
Source: Digital Discovery (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Predicting compositional changes of organic–inorganic hybrid materials with Augmented CycleGAN Image-to-image translation models applied to materials: augmented CycleGAN models for predicting chemical compositions of hybrid materials. Compositional data ; Materials science ; Mineralogy ; Computer science ; Chemistry ; Machine learning Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Jan Berger | Alper-Sedat Dönmez | Aladin Ullrich | Hana Bunzen | Roland A. Fischer | Gregor Kieslich
Source: Communications Chemistry (article, 2022-12-22)
Field: Materials Science — Materials Chemistry

Reduced thermal expansion by surface-mounted nanoparticles in a pillared-layered metal-organic framework Control of thermal expansion (TE) is important to improve material longevity in applications with repeated temperature changes or fluctuations. The TE behavior of metal-organic frameworks (MOFs) is increasingly well understood, while the impact of surface-mounted nanoparticles (NPs) on the TE properties of MOFs remains unexplored despite large promises of NP@MOF composites in catalysis and adsorbate diffusion control. Here we study the influence of surface-mounted platinum nanoparticles on the TE properties of Pt@MOF (Pt@Zn<sub>2</sub>(DP-bdc)<sub>2</sub>dabco; DP-bdc<sup>2-</sup>=2,5-dipropoxy-1,4-benzenedicarboxylate, dabco=1,4-diazabicyclo[2.2.2]octane). We show that TE is largely retained at low platinum loadings, while high loading results in significantly reduced TE at higher temperatures compared to the pure MOF. These findings support the chemical intuition that surface-mounted particles restrict deformation of the MOF support and suggest that composite materials exhibit superior TE properties thereby excluding thermal stress as limiting factor for their potential application in temperature swing processes or catalysis. DABCO ; Materials science ; Metal-organic framework ; Nanoparticle ; Catalysis ; Octane ; Platinum ; Thermal expansion ; Chemical engineering ; Surface diffusion ; Nanotechnology ; Composite number ; Composite material ; Chemistry ; Physical chemistry ; Organic chemistry ; Adsorption Thermal Expansion and Ionic Conductivity ; Advanced Battery Materials and Technologies ; Thermal properties of materials
Authors: Sandeep K Vaishnav | HS Bhawara | Rajesh Mishra
Source: International Journal of Science and Research Archive (article, 2022-12-29)
Field: Chemistry — Spectroscopy

Overview of nano enabled sensor for analysis of explosive substances Threats of armed conflict and terrorist attacks present difficult challenges for defense agencies around the world and are an increasing source of worry for the public and security-conscious policymakers. The environmental monitoring of residue or discarded explosives in soil, as well as the detection of ultra-low levels of explosive compounds in remote areas or under extreme conditions for anti-terrorist activities, continue to pose significant challenges. Most explosives produce very little vapors, making it difficult to detect them with common techniques for other compounds. Due to a number of factors, including the large variety of explosives substances, the enormous number of deployment methods, and the dearth of low-cost sensors with high sensitivity and selectivity makes explosive detection very complicated and expensive task. To defeat explosives-based terrorism, sensors must have high sensitivity and selectivity as well as the capacity to produce and deploy them at lower costs. Nanotechnology-based sensors have an excellent possibility of meeting all the criteria for a successful approach to explosive trace detection. The design and analytical capabilities of explosive detection systems employing nanomaterial as signal transducers are covered in this overview. The review article also discusses the importance of nano-enabled technologies for the explosive detection in security applications, gives background on the technology, and identifies other issues that need to be resolved. Explosive material ; Explosive detection ; Terrorism ; Computer science ; Computer security ; Software deployment ; Risk analysis (engineering) ; Nanotechnology ; Environmental science ; Business ; Materials science ; Political science ; Chemistry Mass Spectrometry Techniques and Applications ; Mechanical and Optical Resonators ; Ion-surface interactions and analysis