jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yanxian Tao | Xiongzhi Zeng | Fan Yi | Jie Liu | Zhenyu Li | Jinlong Yang
Source: arXiv (Cornell University) (preprint, 2022-06-08)
Field: Materials Science — Materials Chemistry

Exploring accurate potential energy surfaces via integrating variational quantum eigensovler with machine learning The potential energy surface (PES) is crucial for interpreting a variety of chemical reaction processes. However, predicting accurate PESs with high-level electronic structure methods is a challenging task due to the high computational cost. As an appealing application of quantum computing, we show in this work that variational quantum algorithms can be integrated with machine learning (ML) techniques as a promising scheme for exploring accurate PESs. Different from using a ML model to represent the potential energy, we encode the molecular geometry information into a deep neural network (DNN) for representing parameters of the variational quantum eigensolver (VQE), leaving the PES to the wave function ansatz. Once the DNN model is trained, the variational optimization procedure that hinders the application of the VQE to complex systems is avoided and thus the evaluation of PESs is significantly accelerated. Numerical results demonstrate that a simple DNN model is able to reproduce accurate PESs for small molecules. Ansatz ; Computer science ; Quantum ; Wave function ; Artificial neural network ; Quantum computer ; Potential energy ; Potential energy surface ; Function (biology) ; ENCODE ; Artificial intelligence ; Statistical physics ; Physics ; Quantum mechanics ; Chemistry ; Molecule Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Quantum Computing Algorithms and Architecture
Authors: Sadaf Mehdi | Saideep Nannapaneni | Gisuk Hwang
Source: SSRN Electronic Journal (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Structural-Material-Operational Performance Relationship for Pool Boiling on Enhanced Surfaces Using Deep Neural Network Model Artificial neural network ; Boiling ; Process engineering ; Computer science ; Materials science ; Artificial intelligence ; Engineering ; Thermodynamics ; Physics High voltage insulation and dielectric phenomena ; Heat Transfer and Boiling Studies ; Erosion and Abrasive Machining
Authors: Zong-Rong Ye | Sheng-Hsuan Hung | Berlin Chen | Ming‐Kang Tsai
Source: ChemRxiv (preprint, 2022-02-16)
Field: Materials Science — Materials Chemistry

Assessment of Predicting Frontier Orbital Energies for Small Organic Molecules Using Knowledge-Based and Structural Information A systematic comparison is demonstrated for the predictions of frontier orbital energies – HOMO (EH), LUMO (EL), and energy gap (ΔEHL) of the molecules in QM9 dataset, where it contains 120k-plus three-dimensional organic molecule structures determined by first-principle simulations. The target molecular properties (EH, EL, and ΔEHL) are predicted using the linear regression (LR), machine learning (random forest, RF), and continuous-filter convolutional neural network (SchNET) approaches. LR and RF models built upon various knowledge-based descriptors, being derived from SMILES of the molecules, can provide predictivity of the target properties with the mean-absolute-errors (MAEs) at 4-6 times of chemical accuracy (0.043 eV). The best approach – SchNET, using the graph representation derived from molecular Cartesian coordinates, is confirmed to provide MAEs of EH, EL, and ΔEHL at 0.051, 0.041, and 0.076 eV, respectively. With the introduction of bond-step matrix representation with SchNET model, the computational cost of dataset preparation can be substantially reduced, and the corresponding MAEs increases moderately to 2-3 times of chemical accuracy. The chemical interpretation of the important descriptors identified in the LR and RF models appear to align with the chemical knowledge of describing these molecular electronic properties, however, being accompanied with tolerable prediction errors. The combination of bond-step representation and SchNET model can provide an assessable-and-balanced option for the high-throughput screening of organic molecules and the preparation of data science approach. HOMO/LUMO ; Molecule ; Representation (politics) ; Molecular descriptor ; Computer science ; Convolutional neural network ; Molecular orbital ; Cartesian coordinate system ; Artificial neural network ; Artificial intelligence ; Chemistry ; Algorithm ; Machine learning ; Mathematics ; Quantitative structure–activity relationship ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: B. Moses Abraham | Priyanka Sinha | Prosun Halder | Jayant K. Singh
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2022-12-08)
Field: Materials Science — Materials Chemistry

MXenes4HER The development of low-cost and highly-efficient pathways to produce renewable energy resources has been one of the top priorities in the science community. To that end, the holistic evaluation and the breathtaking success of MXenes have gained intense momentum by unlocking several new directions toward its usage in catalysis due to their evolutionary hallmark properties. However, the large variety of chemical compositions makes it highly difficult to screen appropriate materials. Hence, it is an urgent need to discover properties that provide information about the catalytic activity at a lower computational cost, particularly to screen over a pool of chemically related family of materials. With this aim, we establish a robust and more broadly applicable multistep workflow from the toolbox of supervised machine learning (ML) algorithms to construct well-trained data-driven models for predicting the hydrogen evolution reaction (HER) activity over 4,500 MM'XT<sub>2</sub>-type MXenes, where 25% of the material space (1,125 systems) is randomly selected to evaluate the HER performance using density functional theory (DFT) calculations. Environmental science MXene and MAX Phase Materials ; Inorganic Chemistry and Materials ; Boron and Carbon Nanomaterials Research
Authors: 直希 野平 | Naoki Nohira | 太一 市澤 | Taichi Ichisawa | Wan-Ting Chiu | 晃 海瀨 | Akira Umise | 正樹 田原 | Masaki Tahara | 秀樹 細田 | Hideki Hosoda
Source: Institutional Repositories DataBase (IRDB) (article, 2022-11-01)
Field: Materials Science — Materials Chemistry

Deep learning based semantic segmentation techniques for microstructure classification in β titanium biomedical shape memory alloys Deep learning ; Segmentation ; Pattern recognition (psychology) ; Image segmentation ; Microstructure Titanium Alloys Microstructure and Properties ; Shape Memory Alloy Transformations ; Machine Learning in Materials Science
Authors: Giuseppe D’Alessio | Alberto Cuoci | Alessandro Parente
Source: Data-Centric Engineering (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Feature extraction and artificial neural networks for the on-the-fly classification of high-dimensional thermochemical spaces in adaptive-chemistry simulations—ADDENDUM An abstract is not available for this content. As you have access to this content, full HTML content is provided on this page. A PDF of this content is also available in through the 'Save PDF' action button. Addendum ; On the fly ; Artificial neural network ; Content (measure theory) ; Computer science ; Feature (linguistics) ; Feature extraction ; Extraction (chemistry) ; Artificial intelligence ; Chemistry ; Pattern recognition (psychology) ; Biological system ; Chromatography ; Operating system ; Mathematics ; Biology Machine Learning in Materials Science ; Advanced Chemical Sensor Technologies ; Catalysis and Oxidation Reactions
Authors: Thomas E. Markland
Source: Proceedings of the 2022 International Symposium on Molecular Spectroscopy (article, 2022-06-20)
Field: Chemistry — Physical and Theoretical Chemistry

ELUCIDATING, ANALYZING, AND DESIGNING SPECTROSCOPIES: LEVERAGING THEORY AND CHEMICAL INTUITION TO GET THE MOST OUT OF MACHINE LEARNING Intuition ; Computer science ; Artificial intelligence ; Machine learning ; Cognitive science ; Human–computer interaction ; Data science ; Psychology Various Chemistry Research Topics ; Metabolomics and Mass Spectrometry Studies ; Spectroscopy and Chemometric Analyses
Authors: Constantine Tsounis | Priyank V. Kumar | Hassan Masood | Rutvij Pankaj Kulkarni | Gopalakrishnan Sai Gautam | Christoph R. Müller | Rose Amal | Denis A. Kuznetsov
Source: Angewandte Chemie (article, 2022-10-24)
Field: Materials Science — Materials Chemistry

Advancing MXene Electrocatalysts for Energy Conversion Reactions: Surface, Stoichiometry, and Stability Abstract MXenes, due to their tailorable chemistry and favourable physical properties, have great promise in electrocatalytic energy conversion reactions. To exploit fully their enormous potential, further advances specific to electrocatalysis revolving around their performance, stability, compositional discovery and synthesis are required. The most recent advances in these aspects are discussed in detail: surface functional and stoichiometric modifications which can improve performance, Pourbaix stability related to their electrocatalytic operating conditions, density functional theory and advances in machine learning for their discovery, and prospects in large scale synthesis and solution processing techniques to produce membrane electrode assemblies and integrated electrodes. This Review provides a perspective that is complemented by new density functional theory calculations which show how these recent advances in MXene material design are paving the way for effective electrocatalysts required for the transition to integrated renewable energy systems. MXenes ; Electrocatalyst ; Density functional theory ; Nanotechnology ; Energy transformation ; Pourbaix diagram ; Materials science ; Chemistry ; Electrode ; Electrochemistry ; Computational chemistry ; Physical chemistry ; Physics ; Thermodynamics MXene and MAX Phase Materials ; Advanced Photocatalysis Techniques ; Electrocatalysts for Energy Conversion
Authors: Subramanian K. R. S. Sankaranarayanan | Suvo Banik | Debdas Dhabal | Henry Chan | Sukriti Manna | Mathew J. Cherukara | Valeria Molinero
Source: Research Square (preprint, 2022-08-30)
Field: Materials Science — Materials Chemistry

CEGAN: Crystal Edge Graph Attention Network for multiscale classification of materials environment Enhanced Data Rates for GSM Evolution ; Graph ; Computer science ; Crystal (programming language) ; Materials science ; Artificial intelligence ; Theoretical computer science Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced X-ray and CT Imaging
Authors: Cai‐Zhuang Wang | Renhai Wang | Weiyi Xia | Tyler J. Slade | Xinyu Fan | Huafeng Dong | Kai‐Ming Ho | P. C. Canfield
Source: Research Square (preprint, 2022-05-12)
Field: Materials Science — Materials Chemistry

ML-guided discovery of La-Co-Pb ternary compounds involving immiscible pairs of Co and Pb elements Abstract Ternary compounds with an immiscible pair of elements are relatively unexplored but promising for novel quantum materials discovery. Exploring what third element and its ratio that can be added to make stable ternary compounds out of an immiscible pair of elements remains a great challenge because the number of possible combinatorial combinations of chemical compositions and crystal structures is enormous. In this work, we combine a machine learning (ML) method with ab initio calculations to efficiently search for the energetically favorable ternary La-Co-Pb compounds containing immiscible elements Co and Pb. Three previously reported structures are correctly captured by our approach even though these structures are excluded from the database for ML. Moreover, we predict a new stable La 3 CoPb compound and 57 low-energy La-Co-Pb ternary compounds whose formation energies are less than 100 meV/atom above the convex hull. Attempts to synthesize La 3 CoPb via multiple techniques ranging from arc melting and annealing, solid state reactions, and high pressure synthesis produce mixed or multi-phases samples with, at best, ambiguous signals of the predicted lowest-energy La 3 CoPb and the second lowest-energy La 18 Co 28 Pb 3 phases. Ternary operation ; Chemistry ; Atom (system on chip) ; Ab initio ; Crystallography ; Organic chemistry ; Computer science Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Rare-earth and actinide compounds
Authors: Yang Ban | Bryan Sundahl | Jawad Ahmed | Crystal Barrera | S. V. Sreenivasan | Roger T. Bonnecaze | Meghali C. Chopra
Source: Metrology, Inspection, and Process Control XXXVI (article, 2022-05-03)
Field: Materials Science — Surfaces, Coatings and Films

Automated extraction of critical dimension from SEM with Weave Current best practices for the extraction of critical dimensions (CDs) from microscopic images requires semiconductor process engineers to analyze images one by one, which is tedious, prone to human bias, time-consuming and expensive. Automated CD extraction using machine learning methodologies is an approach to accelerate and improve the accuracy of this process. Deep learning convolutional neural nets specifically can be used effectively for image segmentation and identification of different material regions; however, providing enough annotated data for training and testing is an ongoing challenge. Here, we demonstrate a method where only one sample image is needed for the neural net to learn how to extract the CDs of interests. The methodology is specifically demonstrated for extracting CDs from a metal assisted chemical etching process. Each experimental SEM image is automatically measured in about 45 seconds. The extracted CD measurements are within 4 nm (&lt;5% error) of the human measured CDs. This automated extraction enables process engineers to improve the accuracy of their metrology workflow, reduce their total metrology costs, and accelerate their process development. Dimension (graph theory) ; Critical dimension ; Extraction (chemistry) ; Computer science ; Mathematics ; Chromatography ; Physics ; Optics ; Chemistry Electron and X-Ray Spectroscopy Techniques ; Manufacturing Process and Optimization ; Industrial Vision Systems and Defect Detection
Authors: Ramón Frey | Bastien F. Grosso | Jean Pascal Fandré | Benjamin Mächler | Nicola A. Spaldin | Aria Mansouri Tehrani
Source: arXiv (Cornell University) (preprint, 2022-01-14)
Field: Materials Science — Materials Chemistry

Accelerated search for new ferroelectric materials We report the development of a combined machine-learning and high-throughput density functional theory (DFT) framework to accelerate the search for new ferroelectric materials. The framework can predict potential ferroelectric compounds using only elemental composition as input. A series of machine-learning algorithms initially predict the possible stable and insulating stoichiometries with a polar crystal structure, necessary for ferroelectricity, within a given chemical composition space. A classification model then predicts the point groups of these stoichiometries. A subsequent series of high-throughput DFT calculations finds the lowest energy crystal structure within the point group. As a final step, non-polar parent structures are identified using group theory considerations, and the values of the spontaneous polarization are calculated using DFT. By predicting the crystal structures and the polarization values, this method provides a powerful tool to explore new ferroelectric materials beyond those in existing databases. Ferroelectricity ; Density functional theory ; Polar ; Polarization (electrochemistry) ; Stoichiometry ; Materials science ; Series (stratigraphy) ; Crystal structure ; Crystal structure prediction ; Algorithm ; Chemical physics ; Computational chemistry ; Computer science ; Crystallography ; Chemistry ; Physics ; Physical chemistry ; Optoelectronics ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Philipp M. Dohmen | Mila Krämer | Patrick Reiser | Pascal Friederich | Marcus Elstner | Weiwei Xie
Source: arXiv (Cornell University) (preprint, 2022-03-07)
Field: Materials Science — Materials Chemistry

Charge Transfer Simulations using Hamiltonian Elements and Forces from Neural Networks The trajectory surface hopping method has been widely used in the simulation of charge transport in organic semiconductors. In the present study, we employ the machine learning (ML) based Hamiltonian to simulate the charge transport in anthracene and pentacene. The neural network (NN) based models are able to predict not just site energies and couplings but also the gradients of the site energy as well as off-diagonal gradients necessary for forces. We train the models on DFTB-quality data for both anthracene and pentacene. By using the obtained models in propagation simulations, we evaluate their performance in reproducing hole mobilities in these materials in terms of both quality and computational cost. The results show that the charge mobilities obtained using the NN-based Hamiltonian are in very good agreements with the charge mobilities computed using the DFTB-based Hamiltonian. Pentacene ; Hamiltonian (control theory) ; Organic semiconductor ; Diagonal ; Anthracene ; Charge (physics) ; Acceptor ; Charge carrier ; Artificial neural network ; Surface hopping ; Chemical physics ; Materials science ; Statistical physics ; Physics ; Condensed matter physics ; Molecular dynamics ; Computer science ; Optoelectronics ; Quantum mechanics ; Voltage ; Machine learning ; Mathematics ; Mathematical optimization Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Electron and X-Ray Spectroscopy Techniques
Authors: Xin Yang | Arghya Bhowmik | Tejs Vegge | Heine Anton Hansen
Source: ChemRxiv (preprint, 2022-11-28)
Field: Materials Science — Materials Chemistry

Neural Network Potentials for Accelerated Metadynamics of Oxygen Reduction Kinetics at Au-Water Interfaces The application of ab-initio molecular dynamics (AIMD) for the explicit modeling of reactions at the solid- liquid interfaces can provide new understandings towards the reaction mechanisms. However, prohibitive computational cost severely restricts the time- and length-scale of AIMD. Equiv- ariant graph neural network (GNN) based accurate surrogate potentials can accelerate the speed of performing molecular dynamics after learning on representative structures in a data efficient manner. In this study, we combined uncertainty- aware GNN potentials and enhanced sampling to investigate the reactive process of the oxygen reduction reaction (ORR) at Au(100)-water interface. By using a well-established active learning framework based on CUR matrix decomposition, we can evenly sample equilibrium structures from MD simulations and non-equilibrium reaction intermediates that are rarely vis- ited during the reaction. The trained GNNs have shown excep- tional performance in terms of force prediction accuracy, the ability to reproduce structural properties, and the low uncer- tainties when performing MD and metadynamics simulations. Furthermore, the collective variables employed in this work en- ables an automatic search of reaction pathways and provide a detailed understanding towards the ORR reaction mechanism on Au(100). Our simulations identify an associative reaction mechanism where adsorbed O2 reacts with water to form hy- droxyls through an *OOH transition state. The reaction pro- ceeds without formation of *O with a low reaction barrier of 0.3 eV. The low barrier agrees with the fast reaction kinetics observed experimentally. The methodology employed in this study can pave the way for modeling complex chemical reac- tions at electrochemical interfaces with an explicit solvent at ambient conditions. Metadynamics ; Reaction mechanism ; Molecular dynamics ; Chemistry ; Chemical kinetics ; Reaction rate ; Computer science ; Reaction dynamics ; Kinetics ; Biological system ; Computational chemistry ; Molecule ; Catalysis ; Physics Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Machine Learning and ELM
Authors: Shomik Verma | Miguel Rivera | David O. Scanlon | Aron Walsh
Source: ChemRxiv (preprint, 2022-02-28)
Field: Materials Science — Materials Chemistry

Machine learned calibrations to high-throughput molecular excited state calculations Understanding the excited state properties of molecules provides insights into how they interact with light. These interactions can be exploited to design compounds for photochemical applications, including enhanced spectral conversion of light to increase the efficiency of photovoltaic cells. While chemical discovery is time- and resource-intensive experimentally, computational chemistry can be used to screen large-scale databases for molecules of interest in a procedure known as high-throughput virtual screening. The first step usually involves a high-speed but low-accuracy method to screen large numbers of molecules (potentially millions) so only the best candidates are evaluated with expensive methods. However, use of a coarse first-pass screening method can potentially result in high false positive or false negative rates. Therefore, this study uses machine learning to calibrate a high-throughput technique (xTB-sTDA) against a higher accuracy one (TD-DFT). Testing the calibration model shows a ~6-fold decrease in error in-domain and a ~3-fold decrease out-of-domain. The resulting mean absolute error of ~0.14 eV is in line with previous work in machine learning calibrations and out-performs previous work in linear calibration of xTB-sTDA. We then apply the calibration model to screen a 250k molecule database and map inaccuracies of xTB-sTDA in chemical space. We also show generalizability of the workflow by calibrating against a higher-level technique (CC2), yielding a similarly low error. Overall, this work demonstrates machine learning can be used to develop a both cheap and accurate method for large-scale excited state screening, enabling accelerated molecular discovery across a variety of disciplines. Generalizability theory ; Calibration ; Computer science ; Excited state ; Workflow ; Throughput ; Algorithm ; Domain (mathematical analysis) ; Machine learning ; Artificial intelligence ; Chemistry ; Mathematics ; Physics ; Database ; Statistics ; Atomic physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Gyoung S. Na
Source: 2022 IEEE International Conference on Big Data (Big Data) (article, 2022-12-17)
Field: Materials Science — Materials Chemistry

Conditional Graph Regression for Complex Chemical Systems with Heterogeneous Substructures Graph neural networks (GNNs) have been widely studied as an efficient and generalized method to predict the physical and chemical properties of chemical systems based on a single homogeneous atomic structure, such as molecule and crystalline material. However, most chemical systems in real-world applications of materials science and engineering contain multiple heterogeneous atomic substructures. Nonetheless, existing GNNs for chemical applications assumed homogeneous graphs with the input node or edge features in the same feature space. In this paper, we reformulate the regression problem on chemical systems as a regression problem on core substructures conditioned by environment substructures. Then, we propose conditional atomic subgraph interaction network (CASIN) that predicts the physical and chemical properties of the input chemical systems by learning the atomic interactions between the heterogeneous core and environment substructures in the chemical systems. For three real-world benchmark chemical datasets, CASIN outperformed state-of-the-art GNNs in predicting the physical and chemical properties of the complex solar cell materials and catalyst systems. Computer science ; Chemical space ; Graph ; Chemical process ; Physical system ; Theoretical computer science ; Artificial intelligence ; Chemistry ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Fuel Cells and Related Materials