jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Gao‐Peng Ren | Ke‐Jun Wu | Yuchen He
Source: Journal of Chemical Information and Modeling (article, 2023-04-27)
Field: Materials Science — Materials Chemistry

Enhancing Molecular Representations Via Graph Transformation Layers Molecular representation learning is an essential component of many molecule-oriented tasks, such as molecular property prediction and molecule generation. In recent years, graph neural networks (GNNs) have shown great promise in this area, representing a molecule as a graph composed of nodes and edges. There are increasing studies showing that coarse-grained or multiview molecular graphs are important for molecular representation learning. Most of their models, however, are too complex and lack flexibility in learning different granular information for different tasks. Here, we proposed a flexible and simple graph transformation layer (i.e., LineEvo), a plug-and-use module for GNNs, which enables molecular representation learning from multiple perspectives. The LineEvo layer transforms fine-grained molecular graphs into coarse-grained ones based on the line graph transformation strategy. Especially, it treats the edges as nodes and generates the new connected edges, atom features, and atom positions. By stacking LineEvo layers, GNNs can learn multilevel information, from atom-level to triple-atoms level and coarser level. Experimental results show that the LineEvo layers can improve the performance of traditional GNNs on molecular property prediction benchmarks on average by 7%. Additionally, we show that the LineEvo layers can help GNNs have more expressive power than the Weisfeiler-Lehman graph isomorphism test. Molecular graph ; Computer science ; Stacking ; Graph isomorphism ; Graph ; Theoretical computer science ; Representation (politics) ; Line graph ; Topology (electrical circuits) ; Chemistry ; Mathematics ; Combinatorics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Anruo Zhong | Clovis Lapointe | Alexandra M. Goryaeva | Jacopo Baima | Manuel Athènes | Mihai‐Cosmin Marinica
Source: Physical Review Materials (article, 2023-02-13)
Field: Materials Science — Materials Chemistry

Anharmonic thermo-elasticity of tungsten from accelerated Bayesian adaptive biasing force calculations with data-driven force fields The elastic properties of tungsten, a ubiquitous material in future energy systems, are investigated up to its melting temperature by means of a data-driven approach. The proposed workflow combines machine learning of the force field and enhanced sampling of the crystalline structure. While the machine learning force field achieves the accuracy of ab initio calculations, its implementation in sampling methods is often limited due to its high computational cost, which is commonly a few orders of magnitude larger than that of traditional potentials. To overcome this limitation, we propose a fast and robust Bayesian sampling scheme aiming at estimating the fully anharmonic free energy of crystalline solids with the help of an improved adaptive biasing force method. This method performs a thermodynamic integration from a harmonic reference system, wherein zero frequencies associated with the periodic boundaries are screened off. The proposed sampling method drastically improves the speed of convergence and overall accuracy. We demonstrate the efficiency of the improved method by calculating the second order derivatives of the free energy, such as the elastic constants, which are performed almost 100 times faster than with the standard methods. The proposed method enables the prediction of the elastic properties of tungsten in the range of temperatures that cannot be investigated experimentally, from 2100 K to the melting temperature. The accuracy and numerical efficiency of the proposed strategy open up many avenues for the reliable prediction of finite-temperature properties of materials, such as the relative stability of structural defects and elastic constants. Materials science ; Force field (fiction) ; Biasing ; Anharmonicity ; Convergence (economics) ; Ab initio ; Statistical physics ; Tungsten ; Bayesian probability ; Algorithm ; Computer science ; Voltage ; Condensed matter physics ; Artificial intelligence ; Physics Machine Learning in Materials Science ; Nuclear Materials and Properties ; High Temperature Alloys and Creep
Authors: Mustafa Dağ | Ercan Aydoğmuş | Zehra Gülten Yalçın | Hasan Arslanoğlu
Source: Materials Today Communications (article, 2023-05-29)
Field: Materials Science — Polymers and Plastics

Diatomite reinforced modified safflower oil-based epoxy biocomposite production: Optimization with RSM and assessment of outcomes by ANN Biocomposite ; Materials science ; Response surface methodology ; Thermogravimetric analysis ; Fourier transform infrared spectroscopy ; Epoxy ; Composite material ; Scanning electron microscope ; Chemical engineering ; Composite number ; Computer science ; Machine learning Polymer composites and self-healing ; Natural Fiber Reinforced Composites ; Electrospun Nanofibers in Biomedical Applications
Authors: Soojung Yang | Rafael Gómez‐Bombarelli
Source: arXiv (Cornell University) (preprint, 2023-03-02)
Field: Materials Science — Materials Chemistry

Chemically Transferable Generative Backmapping of Coarse-Grained Proteins Coarse-graining (CG) accelerates molecular simulations of protein dynamics by simulating sets of atoms as singular beads. Backmapping is the opposite operation of bringing lost atomistic details back from the CG representation. While machine learning (ML) has produced accurate and efficient CG simulations of proteins, fast and reliable backmapping remains a challenge. Rule-based methods produce poor all-atom geometries, needing computationally costly refinement through additional simulations. Recently proposed ML approaches outperform traditional baselines but are not transferable between proteins and sometimes generate unphysical atom placements with steric clashes and implausible torsion angles. This work addresses both issues to build a fast, transferable, and reliable generative backmapping tool for CG protein representations. We achieve generalization and reliability through a combined set of innovations: representation based on internal coordinates; an equivariant encoder/prior; a custom loss function that helps ensure local structure, global structure, and physical constraints; and expert curation of high-quality out-of-equilibrium protein data for training. Our results pave the way for out-of-the-box backmapping of coarse-grained simulations for arbitrary proteins. Computer science ; Generative grammar ; Representation (politics) ; Granularity ; Molecular dynamics ; Algorithm ; Theoretical computer science ; Artificial intelligence ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Protein Structure and Dynamics ; Block Copolymer Self-Assembly
Authors: Hossein Hajiabolhassan | Zahra Taheri | Ali Hojatnia | Yavar Taheri Yeganeh
Source: Journal of Chemical Information and Modeling (article, 2023-05-15)
Field: Materials Science — Materials Chemistry

FunQG: Molecular Representation Learning via Quotient Graphs To accurately predict molecular properties, it is important to learn expressive molecular representations. Graph neural networks (GNNs) have made significant advances in this area, but they often face limitations like neighbors-explosion, under-reaching, oversmoothing, and oversquashing. Additionally, GNNs tend to have high computational costs due to their large number of parameters. These limitations emerge or increase when dealing with larger graphs or deeper GNN models. One potential solution is to simplify the molecular graph into a smaller, richer, and more informative one that is easier to train GNNs. Our proposed molecular graph coarsening framework called <b>FunQG</b>, uses <b>Fun</b>ctional groups as building blocks to determine a molecule's properties, based on a graph-theoretic concept called <b>Q</b>uotient <b>G</b>raph. We show through experiments that the resulting informative graphs are much smaller than the original molecular graphs and are thus more suitable for training GNNs. We apply FunQG to popular molecular property prediction benchmarks and compare the performance of popular baseline GNNs on the resulting data sets to that of state-of-the-art baselines on the original data sets. Our experiments demonstrate that FunQG yields notable results on various data sets while dramatically reducing the number of parameters and computational costs. By utilizing functional groups, we can achieve an interpretable framework that indicates their significant role in determining the properties of molecular quotient graphs. Consequently, FunQG is a straightforward, computationally efficient, and generalizable solution for addressing the molecular representation learning problem. Computer science ; Quotient ; Graph ; Molecular graph ; Theoretical computer science ; Smoothing ; Machine learning ; Artificial intelligence ; Representation (politics) ; Mathematics ; Combinatorics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Fedor Khorobrykh | С. А. Климин | B. A. Kulnitskiy | Faridun N. Jalolov | Alexander G. Kvashnin | А. А. Елисеев | А. Н. Кириченко | Vladimir A. Prenas | В. Н. Денисов | N. N. Melnik | Павел Б. Сорокин | Mikhail Popov
Source: Carbon (article, 2023-07-21)
Field: Materials Science — Materials Chemistry

Cluster structure of ultrahard fullerite revealed by Raman spectroscopy Nanoclusters ; Raman spectroscopy ; Materials science ; Resonance (particle physics) ; Excited state ; Molecular physics ; Analytical Chemistry (journal) ; Chemistry ; Nanotechnology ; Atomic physics ; Optics ; Physics Diamond and Carbon-based Materials Research ; High-pressure geophysics and materials ; Fullerene Chemistry and Applications
Authors: Romy Garg | Nikhil Ram Patra | Soumyajit Samal | Shubham Babbar | Kaushik Parida
Source: Nanoscale (review, 2023-01-01)
Field: Materials Science — Materials Chemistry

A review on accelerated development of skin-like MXene electrodes: from experimental to machine learning Foreshadowing future needs has catapulted the progress of skin-like electronic devices for human-machine interactions. These devices possess human skin-like properties such as stretchability, self-healability, transparency, biocompatibility, and wearability. This review highlights the recent progress in a promising material, MXenes, to realize soft, deformable, skin-like electrodes. Various structural designs, fabrication strategies, and rational guidelines adopted to realize MXene-based skin-like electrodes are outlined. We explicitly discussed machine learning-based material informatics to understand and predict the properties of MXenes. Finally, an outlook on the existing challenges and the future roadmap to realize soft skin-like MXene electrodes to facilitate technological advances in the next-generation human-machine interactions has been described. Human–machine system ; Development (topology) ; Materials science ; Electrode ; Nanotechnology ; Computer science ; Systems engineering ; Artificial intelligence ; Engineering ; Chemistry MXene and MAX Phase Materials ; Advanced Sensor and Energy Harvesting Materials ; Advanced Memory and Neural Computing
Authors: Aftab Anwar | Wenyi Yang | Jing Li | Yanwei Wang | Bo Sun | Muhammad Ameen | Ismail Shah | Chunsheng Li | Zia Ul Mustafa | Yaseen Muhammad
Source: Cogent Engineering (article, 2023-07-04)
Field: Materials Science — Biomaterials

Predicting the compressive strength of cellulose nanofibers reinforced concrete using regression machine learning models Cellulose nanofibers (CNFs) are the newly introduced plant-based materials in the construction industry to ensure sustainable development. The use of artificial intelligence (AI) techniques especially machine learning (ML) models has assisted to economized civil engineering. This research aims to determine the compressive strength of cellulose nanofibers reinforced concrete by using supervised regression machine learning techniques for analysis before adopting to utilize. To achieve this task, the machine learning models: Random Forest (RF), Linear Regression (LR), Support Vector Regressor (SVR), Gradient Boosting Regressor (GBR), Ada Boosting Regressor (ABR), K-Neighbor Regressor (KNN), Bagging Regressor (BR), XG Boost Regressor (XGBR), Decision Tree (DT), and Pruned Decision Tree (PDT) were implemented. An experimental-based dataset containing 695 data points was prepared and split into two categories (Training dataset = 70%, Testing dataset = 30%) for the evolution of ML models. There were seven independent variables: cement (kg/m3), water (kg/m3), CNFs (kg/m3), superplasticizer (kg/m3), fine aggregate (kg/m3), coarse aggregate (kg/m3), and age (Day) variables as an input and one dependent variable: compressive strength fc of CNFs reinforced concrete (MPa) as an output. The following metrics were employed to gauge the ability of the model: R2, MAPE, MAE, MSE, and RMSE. The findings specified that seven out of ten models (RF, BR, XGBR, DT, GBR, ABR, and KNN) to predict the compressive strength of CNFs concrete had a firm capability (R2 >0.72, MAPE ≤ 0.1, and MAE ≤ 5) confirming to the standard of R2 value greater than 0.60 and metrics values very less, close to one. According to the sensitivity analysis of Random Forest model, water and cement were the factors with the biggest effects on the prediction of CNFs reinforced concrete, while the smallest effecting variable was coarse aggregate. It was concluded that the RF, BR, and DT were the premier predicting models. Compressive strength ; Random forest ; Support vector machine ; Machine learning ; Decision tree ; Artificial intelligence ; Computer science ; Linear regression ; Boosting (machine learning) ; Statistics ; Mathematics ; Materials science ; Composite material Advanced Cellulose Research Studies ; Natural Fiber Reinforced Composites ; Innovative concrete reinforcement materials
Authors: Sandra Elvira Fajardo Muñoz | Anthony J. Freire Castro | Michael I. Mejía Garzón | Galo J. Páez Fajardo | Galo J. Páez Gracia
Source: Frontiers in Chemical Engineering (article, 2023-01-10)
Field: Chemistry — Analytical Chemistry

Artificial intelligence models for yield efficiency optimization, prediction, and production scalability of essential oil extraction processes from citrus fruit exocarps Introduction: Excessive demand, environmental problems, and shortages in market-leader countries have led the citrus (essential) oil market price to drift to unprecedented high levels with negative implications for citrus oil-dependent secondary industries. However, the high price conditions have promoted market incentives for the incorporation of new small-scale suppliers as a short-term supply solution for the market. Essential oil chemical extraction via steam distillation is a valuable option for these new suppliers at a lab and small-scale production level. Nevertheless, mass-scaling production requires prediction tools for better large-scale control of outputs. Methods: This study provides an intelligent model based on a multi-layer perceptron (MLP) artificial neural network (ANN) for developing a highly reliable numerical dependency between the chemical extraction output from essential oil steam distillation processes (output vector) and orange peel mass loading (input vector). In a data pool of 25 extraction experiments, 14 output–input pairs were the in training set, 6 in the testing set, and 5 cross-compared the model’s accuracy with traditional numerical approaches. Results and Discussion: After varying the number of nodes in the hidden layer, a 1–9–1 MLP topology best optimizes the statistical parameters (coefficient of determination (R2) and mean square error) of the testing set, achieving a precision of nearly 97.6%. Our model can capture non-linearity behavior when scaling-up production output for mass production processes, thus providing a viable answer for the scalability issue with a state-of-the-art computational tool for planning, management, and mass production of citrus essential oils. Scalability ; Computer science ; Artificial neural network ; Multilayer perceptron ; Production (economics) ; Artificial intelligence ; Machine learning ; Mathematical optimization ; Mathematics Spectroscopy and Chemometric Analyses ; Edible Oils Quality and Analysis ; Postharvest Quality and Shelf Life Management
Authors: Mei Li | Lu Zhang | Lingli Jiang | Zhibo Zhao | Youhua Long | Dongmei Chen | Jun Bin | Chao Kang | Yajuan Liu
Source: Talanta (article, 2023-09-17)
Field: Chemistry — Analytical Chemistry

Label-free Raman microspectroscopic imaging with chemometrics for cellular investigation of apple ring rot and nondestructive early recognition using near-infrared reflection spectroscopy with machine learning Chemistry ; Chemical imaging ; Botryosphaeria dothidea ; Chemometrics ; Partial least squares regression ; Raman spectroscopy ; Imaging spectroscopy ; Lignin ; Biological system ; Hyperspectral imaging ; Optics ; Botany ; Chromatography ; Artificial intelligence ; Machine learning ; Computer science ; Organic chemistry Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Essential Oils and Antimicrobial Activity
Authors: Haoyi Yu | Zhaoyang Xie | Chenhao Li | Chi Li | Leonardo de S. Menezes | Stefan A. Maier | Haoran Ren
Source: Applied Physics Letters (article, 2023-12-11)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Dispersion engineering of metalenses Optical dispersion, the variation of the speed of light with frequency in a material, presents significant challenges in modern optical systems, including chromatic aberration and pulse signal distortion. Traditional approaches to dispersion engineering of an optical lens system require the use of a set of sub-lenses of opposite dispersion properties, largely increasing the overall lens thickness. Ultrathin metasurfaces offer unprecedented control over optical wavefronts with advanced functionalities. Developing achromatic metalenses has thereby emerged as a timely research topic for metasurface research. This Perspective article provides a comprehensive overview of dispersion engineering methods in metalenses, including the use of 2D and 3D meta-atoms fabricated from planar lithography and 3D laser lithography methods, respectively. We compare key figures of merit of achromatic metalenses developed for different wavelength ranges and discuss recent inverse design of large-scale achromatic metalenses. We believe advanced machine learning methods as well as hybrid nanofabrication of diffractive metalenses, refractive lenses, and metamaterials-like spaceplates could offer promising avenues for overcoming current challenges and eventually push ultrathin achromatic optics to practical applications in optics-related fields. Achromatic lens ; Optics ; Lens (geology) ; Wavefront ; Dispersion (optics) ; Metamaterial ; Photolithography ; Materials science ; Chromatic aberration ; Lithography ; Optoelectronics ; Physics ; Chromatic scale Metamaterials and Metasurfaces Applications ; Orbital Angular Momentum in Optics ; Plasmonic and Surface Plasmon Research
Authors: Kangmo Yeo | Sukmin Jeong
Source: Applied Surface Science (article, 2023-03-01)
Field: Materials Science — Materials Chemistry

Machine learning insight into h-BN growth on Pt(111) from atomic states Boron ; Monolayer ; Substrate (aquarium) ; Nitrogen ; Materials science ; Crystallography ; Precipitation ; Chemical physics ; Nanotechnology ; Chemistry ; Physics ; Organic chemistry Graphene research and applications ; Machine Learning in Materials Science ; 2D Materials and Applications
Authors: Huizhen Kuang | Xianfeng Cao | Jingyuan Li | Lingli Wang
Source:  (article, 2023-12-12)
Field: Materials Science — Materials Chemistry

HGBO-DSE: Hierarchical GNN and Bayesian Optimization based HLS Design Space Exploration High-Level Synthesis (HLS) design space exploration aims to find Pareto-optimal designs in the vast directive configuration space. This paper proposes an automatic framework, HGBO-DSE, which consists of a Hierarchical Graph neural network Predictor (HGP) to estimate post-implementation PPA accurately, a Tree-structured Design space Modeler (TDM) to remove the invalid configurations, and a Bayesian Optimization based Multi-objective Exploration engine (BOME) to search Pareto solutions efficiently at function/loop/array/operator-level. A standard dataset is constructed to facilitate AI EDA-related research. The experimental results demonstrate that our HGP can reduce the prediction error of power, critical path delay and resource utilization to 4.21%~7.72%, which outperforms the state-of-the-art works significantly. BOME integrated with our novel algorithm MOTPE-FL can achieve better Pareto fronts than meta-heuristic algorithms SA and NSGA-II, with PPA gains of 72.00% and 30.47% respectively. BOME with HGP can accelerate the DSE process by up to 24 × with an average speedup of 14×. Computer science ; Design space exploration ; Bayesian optimization ; Pareto principle ; Speedup ; Multi-objective optimization ; Heuristic ; Critical path method ; Space exploration ; Bayesian probability ; Path (computing) ; Algorithm ; Mathematical optimization ; Machine learning ; Artificial intelligence ; Parallel computing ; Mathematics ; Engineering ; Embedded system Machine Learning in Materials Science ; VLSI and FPGA Design Techniques ; Manufacturing Process and Optimization
Authors: Sowmya Natarajan | Vijayakumar Ponnusamy
Source: Foods (article, 2023-03-09)
Field: Chemistry — Analytical Chemistry

Classification of Organic and Conventional Vegetables Using Machine Learning: A Case Study of Brinjal, Chili and Tomato Growing organic food is becoming a challenging task with increasing demand. Food fraud activity has increased considerably with the increase in population growth. Consumers cannot visually distinguish between conventional and organically grown food products. Spectroscopic methodologies are presented to identify chemicals in food, thereby identifying organic and conventional food. Such spectroscopic techniques are laboratory-based, take more time to produce an outcome, and are costlier. Thus, this research designed a portable, low-cost multispectral sensor system to discriminate between organic and conventional vegetables. The designed multispectral sensor system uses a wavelength range (410 nm-940 nm) that includes three bands, namely visible (VIS), ultraviolet (UV) and near-infrared (NIR) spectra, to enhance the accuracy of detection. Tomato, brinjal and green chili samples are employed for the experiment. The organic and conventional discrimination problem is formulated as a classification problem and solved through random forest (RF) and neural network (NN) models, which achieve 92% and 89% accuracy, respectively. A two-stage enhancement mechanism is proposed to improve accuracy. In the first stage, the fuzzy logic mechanism generates additional feature sets. Ant colony optimization (ACO) algorithm-based parameter tuning and feature selection are employed in the second stage to enhance accuracy further. This two-stage improvement mechanism results in 100% accuracy in discriminating between organic and conventional vegetable samples. The detected adulterant is displayed on a web page through an IoT-developed application module to be accessed from anywhere. Multispectral image ; Artificial intelligence ; Computer science ; Population ; Random forest ; Pattern recognition (psychology) ; Machine learning Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research