jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hatem Helal | Jesun Firoz | Jenna Bilbrey | Mario Michael Krell | Tom Murray | Ang Li | Sotiris S. Xantheas | Sutanay Choudhury
Source: arXiv (Cornell University) (preprint, 2022-11-25)
Field: Materials Science — Materials Chemistry

Extreme Acceleration of Graph Neural Network-based Prediction Models for Quantum Chemistry Molecular property calculations are the bedrock of chemical physics. High-fidelity \textit{ab initio} modeling techniques for computing the molecular properties can be prohibitively expensive, and motivate the development of machine-learning models that make the same predictions more efficiently. Training graph neural networks over large molecular databases introduces unique computational challenges such as the need to process millions of small graphs with variable size and support communication patterns that are distinct from learning over large graphs such as social networks. This paper demonstrates a novel hardware-software co-design approach to scale up the training of graph neural networks for molecular property prediction. We introduce an algorithm to coalesce the batches of molecular graphs into fixed size packs to eliminate redundant computation and memory associated with alternative padding techniques and improve throughput via minimizing communication. We demonstrate the effectiveness of our co-design approach by providing an implementation of a well-established molecular property prediction model on the Graphcore Intelligence Processing Units (IPU). We evaluate the training performance on multiple molecular graph databases with varying degrees of graph counts, sizes and sparsity. We demonstrate that such a co-design approach can reduce the training time of such molecular property prediction models from days to less than two hours, opening new possibilities for AI-driven scientific discovery. Computer science ; Artificial neural network ; Graph database ; Graph ; Property (philosophy) ; Computation ; Theoretical computer science ; Software ; Artificial intelligence ; Machine learning ; Algorithm Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Mohammad Tohidi Vahdat | Kumar Agrawal Varoon | Giovanni Pizzi
Source: arXiv (Cornell University) (preprint, 2022-07-18)
Field: Materials Science — Materials Chemistry

Machine-learning accelerated identification of exfoliable two-dimensional materials Two-dimensional (2D) materials have been a central focus of recent research because they host a variety of properties, making them attractive both for fundamental science and for applications. It is thus crucial to be able to identify accurately and efficiently if bulk three-dimensional (3D) materials are formed by layers held together by a weak binding energy that, thus, can be potentially exfoliated into 2D materials. In this work, we develop a machine-learning (ML) approach that, combined with a fast preliminary geometrical screening, is able to efficiently identify potentially exfoliable materials. Starting from a combination of descriptors for crystal structures, we work out a subset of them that are crucial for accurate predictions. Our final ML model, based on a random forest classifier, has a very high recall of 98\%. Using a SHapely Additive exPlanations (SHAP) analysis, we also provide an intuitive explanation of the five most important variables of the model. Finally, we compare the performance of our best ML model with a deep neural network architecture using the same descriptors. To make our algorithms and models easily accessible, we publish an online tool on the Materials Cloud portal that only requires a bulk 3D crystal structure as input. Our tool thus provides a practical yet straightforward approach to assess whether any 3D compound can be exfoliated into 2D layers. Computer science ; Random forest ; Identification (biology) ; Artificial neural network ; Machine learning ; Classifier (UML) ; Artificial intelligence ; Focus (optics) ; Deep learning ; Variety (cybernetics) ; Cloud computing ; Data mining ; Physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Zhuoran Qiao | Anders S. Christensen | Matthew Welborn | Frederick R. Manby | Animashree Anandkumar | Thomas F. Miller
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2022-05-20)
Field: Materials Science — Materials Chemistry

OrbNet-Equi: Informing geometric deep learning with electronic interactions to accelerate quantum chemistry Source data for the manuscript "Informing geometric deep learning with electronic interactions to accelerate quantum chemistry"; The SDC21 training dataset; Neural network code and evaluation examples. Version 2 note: The license is updated. Typos in README.md and minor numerical errors in the U_0 predictions have been corrected. Version 3 note: Additional scripts and summary dataframes for cube-based charge density analysis. Quantum chemistry ; Quantum ; Chemistry ; Nanotechnology ; Theoretical physics ; Computer science ; Cognitive science ; Psychology ; Physics ; Quantum mechanics ; Materials science ; Physical chemistry Machine Learning in Materials Science ; History and advancements in chemistry
Authors: Eric Yao | Christopher A. Kendziora | Robert Furstenberg | Christopher J. Breshike | Drew M. Finton | Drew C. Kendziora
Source: 2022 IEEE International Conference on Data Mining Workshops (ICDMW) (article, 2022-11-01)
Field: Chemistry — Analytical Chemistry

Deep Neural Networks and Data Accentuation for Standoff Detection of Dangerous Chemicals We are developing machine learning algorithms to identify chemicals of interest by their diffuse infrared reflectance signatures. The Infrared Backscatter Imaging Spectroscopy (IBIS) technology is used to capture these reflectance signatures through a mobile cart-based system. An infrared laser is directed toward the target surface from a standoff distance, and an MCT focal plane array collects the backscatter images from different wavelengths of laser into what is known as a hyperspectral image cube (hypercube for short). The hypercubes from our measurements are represented in a matrix of numbers. These hypercubes have a 128x128 front face representing the spatial dimensions (the physical image), and a depth of 153 representing the spectral dimension, which contains the IBIS measurements from various wavelengths of laser. These data are fed into a deep neural network machine learning algorithm for classification into specific chemicals (analytes). We propose a data accentuation technique to improve classification performance on IBIS data. Furthermore, we demonstrate the capability of machine learning algorithms to perform pixel-by-pixel discrimination of chemicals within a hypercube image, as well as the simultaneous detection of multiple different chemicals. We also demonstrate the capacity of machine learning algorithms to perform with high precision and recall under noisy conditions. The algorithm goes beyond simply identifying the presence of a chemical, in that it is also able to pinpoint the spatial location of each chemical within the field of view. Hyperspectral imaging ; Computer science ; Artificial intelligence ; Pixel ; Remote sensing ; Data cube ; Artificial neural network ; Spectral signature ; Computer vision ; Backscatter (email) ; Hypercube ; Pattern recognition (psychology) ; Data mining ; Geology ; Telecommunications Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Liang Chen | Bowen Wang | Shaogang Hao | Guangyong Chen | Pheng‐Ann Heng | Xiaolong Zou
Source: Research Square (preprint, 2022-10-21)
Field: Materials Science — Materials Chemistry

Multi-Task Mixture Density Graph Neural Networks for Predicting Cu-based Single-Atom Alloy Catalysts for CO2 Reduction Reduction (mathematics) ; Atom (system on chip) ; Alloy ; Catalysis ; Graph ; Task (project management) ; Artificial neural network ; Materials science ; Computer science ; Chemistry ; Artificial intelligence ; Mathematics ; Theoretical computer science ; Metallurgy ; Parallel computing ; Engineering ; Organic chemistry Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Catalytic Processes in Materials Science
Authors: Zhuoran Qiao | Anders S. Christensen | Matthew Welborn | Frederick R. Manby | Animashree Anandkumar | Thomas F. Miller
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2022-05-20)
Field: Materials Science — Materials Chemistry

OrbNet-Equi: Informing geometric deep learning with electronic interactions to accelerate quantum chemistry Source data for the manuscript "Informing geometric deep learning with electronic interactions to accelerate quantum chemistry"; The SDC21 training dataset; Neural network code and evaluation examples. Version 2 note: The license is updated. Typos in README.md and minor numerical errors in the U_0 predictions have been corrected. Version 3/4 note: Additional scripts and summary dataframes for cube-based charge density analysis. Quantum ; Chemistry ; Deep learning ; Computer science ; Nanotechnology ; Artificial intelligence ; Theoretical physics ; Human–computer interaction ; Physics ; Quantum mechanics ; Materials science Machine Learning in Materials Science ; History and advancements in chemistry
Authors: Suiying Ye | Nastaran Meftahi | Igor Lyskov | tian tian | Sudhir Kumar | Andrew J. Christofferson | David A. Winkler | Chih‐Jen Shih | Salvy P. Russo | Jean‐Christophe Leroux | Yinyin Bao
Source: ChemRxiv (preprint, 2022-05-18)
Field: Materials Science — Materials Chemistry

Machine Learning-assisted Exploration of a Universal Polymer Platform with Charge Transfer-dependent Full-color Emission Understanding the color tuning of solid-state emissive materials is essential from a fundamental mechanistic viewpoint, as well as for practical applications. The development of color-tunable fluorescent materials with simple chemical compositions and easy to synthesize is highly desirable, but practically challenging. Despite copious research into molecular design and engineering, a general and facile polymer platform that offers high flexibility and broad extensibility in emission color tuning is still lacking. Here, we report a universal yet simple platform based on through-space charge transfer (TSCT) polymers, that has full-color tunable emission and was developed with the aid of predictive machine learning models. Using a single acceptor (A) fluorophore as the initiator for atom transfer radical polymerization (ATRP), a series of electron donor (D) groups containing simple polycyclic aromatic moieties (e.g., pyrene) are introduced either by one-step copolymerization or by end-group functionalization of a pre-synthesized polymer. By manipulating donor-acceptor (D-A) interactions via controlled polymer synthesis, continuous blue-to-red emission color tuning was easily achieved in solid polymers. Theoretical investigations confirm the structurally dependent TSCT-induced emission redshifts. We also exemplify how these TSCT polymers can be used as a general design platform for solid-state stimuli-responsive materials with high-contrast photochromic emission, by applying them to proof-of-concept information encryption. Polymer ; Atom-transfer radical-polymerization ; Materials science ; Fluorophore ; Surface modification ; Nanotechnology ; Acceptor ; Copolymer ; Photochemistry ; Fluorescence ; Chemical engineering ; Chemistry Luminescence and Fluorescent Materials ; Photochromic and Fluorescence Chemistry ; Molecular Sensors and Ion Detection
Authors: Zhengfei Shui | Yuxin Huo
Source: 2022 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) (article, 2022-06-24)
Field: Materials Science — Materials Chemistry

Analysis of the reaction system of C4 olefin prepared by ethanol coupling based on machine learning This paper interprets the reaction system of ethanol-coupled preparation of C4 olefins from the two aspects of Prince mechanism [1] and hydroxyaldehyde condensation mechanism[1], gives the deep mechanism of industrial C4 olefins, and then analyzes the topic through numerical angles and mechanism angles, and analyzes the products of the main equations in the reaction system from the mechanism point of view, and analyzes the reaction rate of the corresponding equations and its sensitivity to catalyst and temperature through the selectivity of the product, enthalpy and entropy of chemical reactions. In the chemical reaction system, the five factors most influential on the chemical reaction were extracted, namely temperature, CoSiO2, Co load, HAP, ethanol concentration, and then the random regression forest was used to supervise the data set, a prediction model was established, and then the catalytic effect of different catalysts was analyzed by machine learning, and the parameters of the PSO particle swarm algorithm were used to optimize the parameters, and finally the reaction conditions for the chemical reaction system to generate more C4 olefins were obtained. Catalysis ; Enthalpy ; Reaction mechanism ; Chemical equation ; Chemical reaction ; Reaction rate ; Olefin fiber ; Chemistry ; Mechanism (biology) ; Computer science ; Coupling reaction ; Thermodynamics ; Biological system ; Algorithm ; Physical chemistry ; Organic chemistry ; Physics Machine Learning in Materials Science
Authors: Salmiah Jamal Mat Rosid | Azman Azid | Aisyah Fathiah Ahmad | Nursyamimi Zulkurnain | Susilawati Toemen | Wan Azelee Wan Abu Bakar | Ahmad Zamani Ab Halim | Wan Nur Aini Wan Mokhtar | Sarina Mat Rosid
Source: Environmental Engineering Research (article, 2022-02-12)
Field: Materials Science — Materials Chemistry

Optimization and physicochemical studies of alumina supported samarium oxide based catalysts using artificial neural network in methanation reaction Developed countries are increasing their demand for natural gas as it is an industrial requirement for fuel transportation. Most of modern society relies heavily on vehicles. However, the presence of CO2 gas has led to the categorization of sour natural gas which reduces the quality and price of natural gas. Therefore, the catalytic methanation technique was applied to convert carbon dioxide (CO2) to methane (CH4) gas and reduce the emissions of CO2 within the environment. In this study, samarium oxide supported on alumina doped with ruthenium and manganese was synthesized via wet impregnation. X-ray diffraction (XRD) analysis revealed samarium oxide, Sm2O3 and manganese oxide, MnO2 as an active species. The reduction temperature for active species was at a low reaction temperature, 268.2oC with medium basicity site as in Temperature Programme Reduction (TPR) and Temperature Programme Desorption (TPD) analyses. Field Emission Scanning Electron Microscopy (FESEM) analysis showed an agglomeration of particle size. The characterised potential catalyst of Ru/Mn/Sm (5:35:60)/Al2O3 (RMS 5:35:60) calcined at 1,000oC revealed 100% conversion of CO2 with 68.87% CH4 formation at the reaction temperature of 400oC. These results were verified by artificial neural network (ANN) with validation R2 of 0.99 indicating all modelling data are acceptable. Methanation ; Catalysis ; Syngas ; Methane ; Calcination ; Chemical engineering ; Manganese ; Materials science ; Oxide ; Samarium ; Inorganic chemistry ; Chemistry ; Metallurgy ; Organic chemistry Catalytic Processes in Materials Science ; Catalysts for Methane Reforming ; Catalysis and Oxidation Reactions
Authors: Jian-Gang Kong | Zhao, Ke-Lin | Jian Li | Qingxu Li | Yu Liu | Rui Zhang | Jia-Ji Zhu | Kai Chang
Source: arXiv (Cornell University) (preprint, 2022-10-19)
Field: Materials Science — Materials Chemistry

Self-supervised Representations and Node Embedding Graph Neural Networks for Accurate and Multi-scale Analysis of Materials Supervised machine learning algorithms, such as graph neural networks (GNN), have successfully predicted material properties. However, the superior performance of GNN usually relies on end-to-end learning on large material datasets, which may lose the physical insight of multi-scale information about materials. And the process of labeling data consumes many resources and inevitably introduces errors, which constrains the accuracy of prediction. We propose to train the GNN model by self-supervised learning on the node and edge information of the crystal graph. Compared with the popular manually constructed material descriptors, the self-supervised atomic representation can reach better prediction performance on material properties. Furthermore, it may provide physical insights by tuning the range information. Applying the self-supervised atomic representation on the magnetic moment datasets, we show how they can extract rules and information from the magnetic materials. To incorporate rich physical information into the GNN model, we develop the node embedding graph neural networks (NEGNN) framework and show significant improvements in the prediction performance. The self-supervised material representation and the NEGNN framework may investigate in-depth information from materials and can be applied to small datasets with increased prediction accuracy. Computer science ; Embedding ; Graph ; Representation (politics) ; Artificial intelligence ; Machine learning ; Artificial neural network ; Node (physics) ; Enhanced Data Rates for GSM Evolution ; Supervised learning ; Data mining ; Pattern recognition (psychology) ; Theoretical computer science Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Graph Neural Networks
Authors: David E. Sommer | Scott T. Dunham
Source: arXiv (Cornell University) (preprint, 2022-03-17)
Field: Materials Science — Materials Chemistry

Entangling Solid Solutions: Machine Learning of Tensor Networks for\n Materials Property Prediction Progress in the application of machine learning techniques to the prediction\nof solid-state and molecular materials properties has been greatly facilitated\nby the development state-of-the-art feature representations and novel deep\nlearning architectures. A large class of atomic structure representations based\non expansions of smoothed atomic densities have been shown to correspond to\nspecific choices of basis sets in an abstract many-body Hilbert space.\nConcurrently, tensor network structures, conventionally the purview of quantum\nmany-body physics and quantum information, have been successfully applied in\nsupervised and unsupervised learning tasks in computer vision and natural\nlanguage processing. In this work, we argue that architectures based on tensor\nnetworks are well-suited to machine learning on Hilbert-space representations\nof atomic structures. This is demonstrated on supervised learning tasks\ninvolving widely available datasets of density functional theory calculations\nof metal and semiconductor alloys. In particular, we show that certain standard\ntensor network topologies exhibit strong generalizability even on small\ntraining datasets while being parametrically efficient. We further relate this\ngeneralizability to the presence of complex entanglement in the trained tensor\nnetworks. We also discuss connections to learning with generalized structural\nkernels and related strategies for compressing large input feature spaces.\n Generalizability theory ; Tensor (intrinsic definition) ; Artificial intelligence ; Computer science ; Hilbert space ; Quantum entanglement ; Machine learning ; Feature (linguistics) ; Theoretical computer science ; Topology (electrical circuits) ; Quantum ; Mathematics ; Physics ; Quantum mechanics ; Geometry Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Catalysis and Oxidation Reactions
Authors: Julian Schuhmacher | Guglielmo Mazzola | Francesco Tacchino | Olga Dmitriyeva | Tai Bui | Shanshan Huang | Ivano Tavernelli
Source: arXiv (Cornell University) (preprint, 2022-03-14)
Field: Materials Science — Materials Chemistry

Extending the reach of quantum computing for materials science with machine learning potentials Solving electronic structure problems represents a promising field of application for quantum computers. Currently, much effort has been spent in devising and optimizing quantum algorithms for quantum chemistry problems featuring up to hundreds of electrons. While quantum algorithms can in principle outperform their classical equivalents, the polynomially scaling runtime, with the number of constituents, can still prevent quantum simulations of large scale systems. We propose a strategy to extend the scope of quantum computational methods to large scale simulations using a machine learning potential, trained on quantum simulation data. The challenge of applying machine learning potentials in today's quantum setting arises from the several sources of noise affecting the quantum computations of electronic energies and forces. We investigate the trainability of a machine learning potential selecting various sources of noise: statistical, optimization and hardware noise.Finally, we construct the first machine learning potential from data computed on actual IBM Quantum processors for a hydrogen molecule. This already would allow us to perform arbitrarily long and stable molecular dynamics simulations, outperforming all current quantum approaches to molecular dynamics and structure optimization. Quantum computer ; Computer science ; Quantum ; Quantum algorithm ; Noise (video) ; Quantum dynamics ; Computational science ; Artificial intelligence ; Theoretical computer science ; Statistical physics ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture
Authors: Ikki Yasuda | Yusei Kobayashi | Katsuhiro Endo | Y. Hayakawa | Kazuhiko Fujiwara | Kuniaki Yajima | Noriyoshi Arai | Kenji Yasuoka
Source: arXiv (Cornell University) (preprint, 2022-03-07)
Field: Materials Science — Materials Chemistry

Prediction of transport property via machine learning molecular movements Molecular dynamics (MD) simulations are increasingly being combined with machine learning (ML) to predict material properties. The molecular configurations obtained from MD are represented by multiple features, such as thermodynamic properties, and are used as the ML input. However, to accurately find the input--output patterns, ML requires a sufficiently sized dataset that depends on the complexity of the ML model. Generating such a large dataset from MD simulations is not ideal because of their high computation cost. In this study, we present a simple supervised ML method to predict the transport properties of materials. To simplify the model, an unsupervised ML method obtains an efficient representation of molecular movements. This method was applied to predict the viscosity of lubricant molecules in confinement with shear flow. Furthermore, simplicity facilitates the interpretation of the model to understand the molecular mechanics of viscosity. We revealed two types of molecular mechanisms that contribute to low viscosity. Molecular dynamics ; Representation (politics) ; Viscosity ; Computer science ; Computation ; Biological system ; Simplicity ; Interpretation (philosophy) ; Simple (philosophy) ; Property (philosophy) ; Statistical physics ; Lubricant ; Molecular descriptor ; Ideal (ethics) ; Artificial intelligence ; Flow (mathematics) ; Algorithm ; Machine learning ; Chemistry ; Mechanics ; Thermodynamics ; Physics ; Computational chemistry ; Quantitative structure–activity relationship Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Protein Structure and Dynamics