jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mingjian Zhou | Liqing Liu | Jiahao Liu | Zihang Mei
Source: Nanomaterials (article, 2023-04-25)
Field: Materials Science — Materials Chemistry

Prediction and Control of Thermal Transport at Defective State Gr/h-BN Heterojunction Interfaces The control of interfacial thermal conductivity is the key to two-dimensional heterojunction in semiconductor devices. In this paper, by using non-equilibrium molecular dynamics (NEMD) simulations, we analyze the regulation of interfacial thermal energy transport in graphene (Gr)/hexagonal boron nitride (<i>h</i>-BN) heterojunctions and reveal the variation mechanism of interfacial thermal energy transport. The calculated results show that 2.16% atomic doping can effectively improve interfacial heat transport by more than 15.6%, which is attributed to the enhanced phonon coupling in the mid-frequency region (15-25 THz). The single vacancy in both N and B atoms can significantly reduce the interfacial thermal conductivity (ITC), and the ITC decreases linearly with the increase in vacancy defect concentration, mainly due to the single vacancy defects leading to an increased phonon participation rate (PPR) below 0.4 in the low-frequency region (0-13 THz), which shows the phonon the localization feature, which hinders the interfacial heat transport. Finally, a BP neural network algorithm is constructed using machine learning to achieve fast prediction of the ITC of Gr/<i>h</i>-BN two-dimensional heterogeneous structures, and the results show that the prediction error of the model is less than 2%, and the method will provide guidance and reference for the design and optimization of the ITC of more complex defect-state heterogeneous structures. Heterojunction ; Materials science ; Phonon ; Thermal conductivity ; Vacancy defect ; Semiconductor ; Molecular dynamics ; Doping ; Optoelectronics ; Boron nitride ; Graphene ; Coupling (piping) ; Thermal ; Condensed matter physics ; Nanotechnology ; Computational chemistry ; Thermodynamics ; Chemistry ; Composite material ; Physics Thermal properties of materials ; Graphene research and applications ; Advanced Thermoelectric Materials and Devices
Authors: Shiqiang Zhang | Juan S Campos Salazar | Christian Feldmann | D. Walz | Frederik Sandfort | Miriam Mathea | Calvin Tsay | Ruth Misener
Source: arXiv (Cornell University) (preprint, 2023-05-16)
Field: Materials Science — Materials Chemistry

Optimizing over trained GNNs via symmetry breaking Optimization over trained machine learning models has applications including: verification, minimizing neural acquisition functions, and integrating a trained surrogate into a larger decision-making problem. This paper formulates and solves optimization problems constrained by trained graph neural networks (GNNs). To circumvent the symmetry issue caused by graph isomorphism, we propose two types of symmetry-breaking constraints: one indexing a node 0 and one indexing the remaining nodes by lexicographically ordering their neighbor sets. To guarantee that adding these constraints will not remove all symmetric solutions, we construct a graph indexing algorithm and prove that the resulting graph indexing satisfies the proposed symmetry-breaking constraints. For the classical GNN architectures considered in this paper, optimizing over a GNN with a fixed graph is equivalent to optimizing over a dense neural network. Thus, we study the case where the input graph is not fixed, implying that each edge is a decision variable, and develop two mixed-integer optimization formulations. To test our symmetry-breaking strategies and optimization formulations, we consider an application in molecular design. Lexicographical order ; Computer science ; Search engine indexing ; Graph ; Symmetry breaking ; Graph isomorphism ; Artificial neural network ; Theoretical computer science ; Mathematical optimization ; Algorithm ; Mathematics ; Artificial intelligence ; Combinatorics ; Line graph Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Colton C. Seegmiller | Sterling G. Baird | Hasan M. Sayeed | Taylor D. Sparks
Source: ChemRxiv (preprint, 2023-03-13)
Field: Materials Science — Materials Chemistry

Discovering Chemically Novel, High-TemperatureSuperconductors One of the biggest unsolved problems in condensed matter physics is what mechanism causes high-temperature superconductivity and if there is a material that can exhibit superconductivity at both room temperature and atmospheric pressure. Among the many important properties of a superconductor, the critical temperature (Tc) or transition temperature is the point at which a material transitions into a superconductive state. In this implementation, machine learning is used to predict the critical temperatures of chemically unique compounds in an attempt to identify new chemically novel, high-temperature superconductors. The training data set (SuperCon) consists of known superconductors and their critical temperatures, and the testing data set (NOMAD) consists of around 700,000 novel chemical formulae. The chemical formulae in these data sets are first passed through a collection of rapid screening tools, SMACT, to check for chemical validity. Next, the DiSCoVeR algorithm is used to train on the SuperCon data to form a model, and then screens through batches of the formulae in the NOMAD data set. Having a combination of a chemical distance metric, density-aware dimensionality reduction, clustering, and a regression model, the DiSCoVeR algorithm serves as a tool to identify and assess these superconducting compositions [1]. This research and implementation resulted in the screening of chemically novel compositions exhibiting critical temperatures upwards of 150 K, which correlates to superconductors in the cuprate class. This implementation demonstrates a process of performing machine learning-assisted superconductor screening (while exploring chemically distinct spaces) which can be utilized in the materials discovery process. Superconductivity ; Cuprate ; Curse of dimensionality ; Cluster analysis ; High-temperature superconductivity ; Set (abstract data type) ; Computer science ; Materials science ; Physics ; Machine learning ; Condensed matter physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Reese E. Jones | Cosmin Safta | Ari Frankel
Source: Computational Mechanics (article, 2023-03-31)
Field: Materials Science — Materials Chemistry

Deep learning and multi-level featurization of graph representations of microstructural data Discretization ; Convolutional neural network ; Computer science ; Homogenization (climate) ; Graph ; Artificial intelligence ; Deep learning ; Segmentation ; Feature learning ; Pattern recognition (psychology) ; ENCODE ; Algorithm ; Theoretical computer science ; Mathematics ; Mathematical analysis Machine Learning in Materials Science ; Composite Material Mechanics ; Topology Optimization in Engineering
Authors: Po‐Yen Tung | Hassan Aftab Sheikh | Matthew Ball | Farhang Nabiei | R. J. Harrison
Source: Geochemistry Geophysics Geosystems (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

SIGMA: Spectral Interpretation Using Gaussian Mixtures and Autoencoder Abstract Identification of unknown micro‐ and nano‐sized mineral phases is commonly achieved by analyzing chemical maps generated from hyperspectral imaging data sets, particularly scanning electron microscope—energy dispersive X‐ray spectroscopy (SEM‐EDS). However, the accuracy and reliability of mineral identification are often limited by subjective human interpretation, non‐ideal sample preparation, and the presence of mixed chemical signals generated within the electron‐beam interaction volume. Machine learning has emerged as a powerful tool to overcome these problems. Here, we propose a machine‐learning approach to identify unknown phases and unmix their overlapped chemical signals. This approach leverages the guidance of Gaussian mixture modeling clustering fitted on an informative latent space of pixel‐wise elemental data points modeled using a neural network autoencoder, and unmixes the overlapped chemical signals of phases using non‐negative matrix factorization. We evaluate the reliability and the accuracy of the new approach using two SEM‐EDS data sets: a synthetic mixture sample and a real‐world particulate matter sample. In the former, the proposed approach successfully identifies all major phases and extracts background‐subtracted single‐phase chemical signals. The unmixed chemical spectra show an average similarity of 83.0% with the ground truth spectra. In the second case, the approach demonstrates the ability to identify potentially magnetic Fe‐bearing particles and their background‐subtracted chemical signals. We demonstrate a flexible and adaptable approach that brings a significant improvement to mineralogical and chemical analysis in a fully automated manner. The proposed analysis process has been built into a user‐friendly Python code with a graphical user interface for ease of use by general users. Hyperspectral imaging ; Autoencoder ; Chemical imaging ; Computer science ; Artificial intelligence ; Pattern recognition (psychology) ; Ground truth ; Gaussian ; Artificial neural network ; Cluster analysis ; Biological system ; Physics Machine Learning in Materials Science ; Geochemistry and Geologic Mapping ; Mineral Processing and Grinding
Authors: Varadarajan Rengaraj | Sebastian Jost | Franz Bethke | Christian Plessl | Hossein Mirhosseini | Andrea Walther | Thomas D. Kühne
Source: Computation (article, 2023-05-09)
Field: Materials Science — Materials Chemistry

A Two-Step Machine Learning Method for Predicting the Formation Energy of Ternary Compounds Predicting the chemical stability of yet-to-be-discovered materials is an important aspect of the discovery and development of virtual materials. The conventional approach for computing the enthalpy of formation based on ab initio methods is time consuming and computationally demanding. In this regard, alternative machine learning approaches are proposed to predict the formation energies of different classes of materials with decent accuracy. In this paper, one such machine learning approach, a novel two-step method that predicts the formation energy of ternary compounds, is presented. In the first step, with a classifier, we determine the accuracy of heuristically calculated formation energies in order to increase the size of the training dataset for the second step. The second step is a regression model that predicts the formation energy of the ternary compounds. The first step leads to at least a 100% increase in the size of the dataset with respect to the data available in the Materials Project database. The results from the regression model match those from the existing state-of-the-art prediction models. In addition, we propose a slightly modified version of the Adam optimizer, namely centered Adam, and report the results from testing the centered Adam optimizer. Ternary operation ; Computer science ; Machine learning ; Artificial intelligence ; Regression ; Classifier (UML) ; Enthalpy ; Ab initio ; Algorithm ; Chemistry ; Mathematics ; Thermodynamics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Tomer Weiss | Luca Cosmo | Eduardo Mayo Yanes | Sabyasachi Chakraborty | Alex Bronstein | Renana Gershoni‐Poranne
Source: ChemRxiv (preprint, 2023-04-05)
Field: Materials Science — Materials Chemistry

Guided Diffusion for Inverse Molecular Design The holy grail of materials science is de novo molecular design -- i.e., the ability to engineer molecules with desired characteristics. Recently, this goal has become increasingly achievable thanks to developments such as equivariant graph neural networks that can better predict molecular properties, and to the improved performance of generation tasks, in particular of conditional generation, in text-to-image generators and large language models. Herein, we introduce GaUDI, a guided diffusion model for inverse molecular design, which combines these advances and can generate novel molecules with desired properties. GaUDI decouples the generator and the property-predicting models and can be guided using both point-wise targets and open-ended targets (e.g., minimum/maximum). We demonstrate GaUDI’s effectiveness using single- and multiple-objective tasks applied to newly-generated data sets of polycyclic aromatic systems, achieving nearly 100% validity of generated molecules. Further, for some tasks, GaUDI discovers better molecules than those present in our data set of 475k molecules. Holy Grail ; Computer science ; Molecular graph ; Inverse ; Generator (circuit theory) ; Set (abstract data type) ; Equivariant map ; Theoretical computer science ; Graph ; Programming language ; Mathematics ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering
Authors: Marko Čanađija | Valentina Košmerl | Martin Zlatić | Domagoj Vrtovšnik | Neven Munjas
Source: European Journal of Mechanics - A/Solids (article, 2023-11-27)
Field: Materials Science — Materials Chemistry

A computational framework for nanotrusses: Input convex neural networks approach The present research aims to provide a practical numerical tool for the mechanical analysis of nanoscale trusses with similar accuracy to molecular dynamics (MD). As a first step, MD simulations of uniaxial tensile and compression tests of all possible chiralities of single-walled carbon nanotubes up to 4 nm in diameter were performed using the AIREBO potential. The results represent a dataset consisting of stress/strain curves that were then used to develop a neural network that serves as a surrogate for a constitutive model for all nanotubes considered. The cornerstone of the new framework is a partially input convex integrable neural network. It turns out that convexity enables favorable convergence properties required for implementation in the classical nonlinear truss finite element available in Abaqus. This completes a molecular dynamics-machine learning-finite element framework suitable for the static analysis of large, nanoscale, truss-like structures. The performance is verified through a comprehensive set of examples that demonstrate ease of use, accuracy, and robustness. Truss ; Finite element method ; Artificial neural network ; Convexity ; Robustness (evolution) ; Nonlinear system ; Computer science ; Constitutive equation ; Regular polygon ; Truss bridge ; Convergence (economics) ; Applied mathematics ; Mathematical optimization ; Algorithm ; Materials science ; Structural engineering ; Computational science ; Mathematics ; Engineering ; Artificial intelligence ; Geometry ; Physics Nonlocal and gradient elasticity in micro/nano structures ; Force Microscopy Techniques and Applications ; Carbon Nanotubes in Composites
Authors: Geemi P. Wellawatte | Philippe Schwaller
Source: arXiv (Cornell University) (preprint, 2023-11-07)
Field: Materials Science — Materials Chemistry

Extracting human interpretable structure-property relationships in chemistry using XAI and large language models Explainable Artificial Intelligence (XAI) is an emerging field in AI that aims to address the opaque nature of machine learning models. Furthermore, it has been shown that XAI can be used to extract input-output relationships, making them a useful tool in chemistry to understand structure-property relationships. However, one of the main limitations of XAI methods is that they are developed for technically oriented users. We propose the XpertAI framework that integrates XAI methods with large language models (LLMs) accessing scientific literature to generate accessible natural language explanations of raw chemical data automatically. We conducted 5 case studies to evaluate the performance of XpertAI. Our results show that XpertAI combines the strengths of LLMs and XAI tools in generating specific, scientific, and interpretable explanations. Property (philosophy) ; Computer science ; Field (mathematics) ; Artificial intelligence ; Raw data ; Natural language processing ; Data science ; Epistemology ; Mathematics Machine Learning in Materials Science ; Explainable Artificial Intelligence (XAI) ; Computational Drug Discovery Methods
Authors: Cheng‐Chu Chung | Charles Clark | Chonghang Zhao | Kim Kisslinger | Fernando Camino | Dmytro Nykypanchuk | Hui Zhong | Sanjit Ghose | Ruipeng Li | Chang‐Yong Nam | Yu‐chen Karen Chen‐Wiegart
Source: Advanced Materials Interfaces (article, 2023-09-19)
Field: Materials Science — Materials Chemistry

Oxidation Driven Thin‐Film Solid‐State Metal Dealloying Forming Bicontinuous Nanostructures Abstract Solid‐state metal dealloying (SSMD) is a promising method for fabricating nanoscale metallic composites and nanoporous metals across a range of materials. Thin‐film SSMD is particularly attractive due to its ability to create fine features via solid‐state interfacial reactions within a thin‐film geometry, which can be integrated into devices for various applications. This work examines a new dealloying couple, namely the Nb–Al alloy with the dealloying agent Sc, as previously predicted in the machine‐learning (ML) models. Prior ML predictions aimed to guide the design of nanoarchitectured materials through dealloying, relying on intuition‐driven discovery within a large parameter space. However, this work reveals that at the nanoscale, the involvement of oxygen in thin film processing may instead drive the dealloying process, resulting in the formation of bicontinuous nanostructures similar to those formed by metal‐agent dealloying. The phase evolution, as well as chemical and morphological changes, are closely analyzed using a combination of X‐ray absorption spectroscopy, diffraction, and scanning transmission electron microscopy to understand the mechanisms behind nanostructure formation. The findings suggest a potential pathway for utilizing oxygen to drive the formation of bicontinuous metal–metal oxide nanocomposites, paving the way for further development of functional nanoporous materials in diverse fields. Materials science ; Nanoporous ; Nanostructure ; Thin film ; Nanotechnology ; Alloy ; Nanoscopic scale ; Nanocomposite ; Oxide ; Metal ; Chemical engineering ; Metallurgy Nanoporous metals and alloys ; Electrocatalysts for Energy Conversion ; Supercapacitor Materials and Fabrication
Authors: Indranil Roy | Hamdy Abouelella | Rajnikant V. Umretiya | Subhrajit Roychowdhury | Bojun Feng | Sandipp Krishnan Ravi | Sayan Ghosh | Raúl B. Rebak | Daniel M. Ruscitto | Vipul Gupta | Andrew Hoffman
Source: MRS Advances (article, 2023-03-06)
Field: Materials Science — Materials Chemistry

Optimizing chemistry for designing oxidation resistant FeCrAl alloys Abstract Traditionally, FeCrAl alloys played an important role in high-temperature applications due to their ability to form a passive Al oxide film at temperatures above ~ 800 °C. Recently, FeCrAl alloys became of interest for the application of accident tolerant nuclear fuel cladding. This study covers work done at GE Research for better understanding the role of Al, Cr, and Mo in oxidation kinetics and thermodynamics. Several models and commercial prototype alloys have been tested in hydrothermal corrosion autoclave loops, at low temperature steam exposure (~ 400 °C), high temperature steam exposure (~ 1000 °C or higher), and high temperature air exposures. The results provide insights on how chromium and aluminum play a significant role in both high temperature and low temperature oxidation of FeCrAl. Additionally, machine learning tools are used to gain further insights on both predicting future optimized chemistries for balancing the properties of hydrothermal corrosion, low and high temperature steam oxidation, and thermal aging (which is exacerbated due to radiation in a nuclear reactor environment). GE plans to use this framework to further optimize the FeCrAl alloy system for use in nuclear reactor environments. Graphical abstract Materials science ; High-temperature corrosion ; Autoclave ; Cladding (metalworking) ; Corrosion ; Hydrothermal circulation ; Alloy ; Nuclear engineering ; Metallurgy ; Oxide ; Chemical engineering Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Fusion materials and technologies
Authors: Ze-Feng Gao | Shuai Qu | Bocheng Zeng | Liu, Yang | Ji-Rong Wen | Hao Sun | Guo, Peng-Jie | Zhong-Yi Lu
Source: arXiv (Cornell University) (preprint, 2023-11-08)
Field: Materials Science — Electronic, Optical and Magnetic Materials

AI-accelerated Discovery of Altermagnetic Materials Altermagnetism, a new magnetic phase, has been theoretically proposed and experimentally verified to be distinct from ferromagnetism and antiferromagnetism. Although altermagnets have been found to possess many exotic physical properties, the limited availability of known altermagnetic materials hinders the study of such properties. Hence, discovering more types of altermagnetic materials with different properties is crucial for a comprehensive understanding of altermagnetism and thus facilitating new applications in the next generation information technologies, e.g., storage devices and high-sensitivity sensors. Since each altermagnetic material has a unique crystal structure, we propose an automated discovery approach empowered by an AI search engine that employs a pre-trained graph neural network to learn the intrinsic features of the material crystal structure, followed by fine-tuning a classifier with limited positive samples to predict the altermagnetism probability of a given material candidate. Finally, we successfully discovered 50 new altermagnetic materials that cover metals, semiconductors, and insulators confirmed by the first-principles electronic structure calculations. The wide range of electronic structural characteristics reveals that various novel physical properties manifest in these newly discovered altermagnetic materials, e.g., anomalous Hall effect, anomalous Kerr effect, and topological property. Noteworthy, we discovered 4 $i$-wave altermagnetic materials for the first time. Overall, the AI search engine performs much better than human experts and suggests a set of new altermagnetic materials with unique properties, outlining its potential for accelerated discovery of the materials with targeted properties. Antiferromagnetism ; Ferromagnetism ; Computer science ; Topological insulator ; Materials science ; Nanotechnology ; Physics ; Condensed matter physics Magnetic and transport properties of perovskites and related materials ; Magnetic properties of thin films ; Advanced Condensed Matter Physics
Authors: Michael J. Statt | Brian A. Rohr | Kris S. Brown | Dan Guevarra | Jens S. Hummelshøj | Linda Hung | Abraham Anapolsky | John M. Gregoire | Santosh K. Suram
Source: Digital Discovery (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

ESAMP: event-sourced architecture for materials provenance management and application to accelerated materials discovery We present a generalizable database architecture ESAMP that captures the complete provenance associated with a material. We demonstrate this architecture and provenance based machine learning on one of the largest experimental materials databases. Provenance ; Architecture ; Event (particle physics) ; Computer science ; History ; Geology ; Archaeology ; Paleontology ; Physics Machine Learning in Materials Science ; Advanced X-ray and CT Imaging ; Scientific Computing and Data Management