jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Benji Maruyama | Jason Hattrick‐Simpers | William D. Musinski | Lori Graham‐Brady | Kang Li | Jonathan Hollenbach | Anup K. Singh | Mitra L. Taheri
Source: MRS Bulletin (article, 2022-11-01)
Field: Materials Science — Materials Chemistry

Artificial intelligence for materials research at extremes Abstract Materials development is slow and expensive, taking decades from inception to fielding. For materials research at extremes, the situation is even more demanding, as the desired property combinations such as strength and oxidation resistance can have complex interactions. Here, we explore the role of AI and autonomous experimentation (AE) in the process of understanding and developing materials for extreme and coupled environments. AI is important in understanding materials under extremes due to the highly demanding and unique cases these environments represent. Materials are pushed to their limits in ways that, for example, equilibrium phase diagrams cannot describe. Often, multiple physical phenomena compete to determine the material response. Further, validation is often difficult or impossible. AI can help bridge these gaps, providing heuristic but valuable links between materials properties and performance under extreme conditions. We explore the potential advantages of AE along with decision strategies. In particular, we consider the problem of deciding between low-fidelity, inexpensive experiments and high-fidelity, expensive experiments. The cost of experiments is described in terms of the speed and throughput of automated experiments, contrasted with the human resources needed to execute manual experiments. We also consider the cost and benefits of modeling and simulation to further materials understanding, along with characterization of materials under extreme environments in the AE loop. Graphical abstract AI sequential decision-making methods for materials research: Active learning, which focuses on exploration by sampling uncertain regions, Bayesian and bandit optimization as well as reinforcement learning (RL), which trades off exploration of uncertain regions with exploitation of optimum function value. Bayesian and bandit optimization focus on finding the optimal value of the function at each step or cumulatively over the entire steps, respectively, whereas RL considers cumulative value of the labeling function, where the latter can change depending on the state of the system (blue, orange, or green). Computer science ; Fidelity ; Reinforcement learning ; Process (computing) ; Bayesian optimization ; Heuristic ; Artificial intelligence ; Machine learning Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Probabilistic and Robust Engineering Design
Authors: R. Paxton Thedford | Fei Yu | William R. T. Tait | Kunal Shastri | Francesco Monticone | Ulrich Wiesner
Source: Advanced Materials (review, 2022-07-21)
Field: Materials Science — Materials Chemistry

The Promise of Soft‐Matter‐Enabled Quantum Materials The field of quantum materials has experienced rapid growth over the past decade, driven by exciting new discoveries with immense transformative potential. Traditional synthetic methods to quantum materials have, however, limited the exploration of architectural control beyond the atomic scale. By contrast, soft matter self-assembly can be used to tailor material structure over a large range of length scales, with a vast array of possible form factors, promising emerging quantum material properties at the mesoscale. This review explores opportunities for soft matter science to impact the synthesis of quantum materials with advanced properties. Existing work at the interface of these two fields is highlighted, and perspectives are provided on possible future directions by discussing the potential benefits and challenges which can arise from their bridging. Materials science ; Soft matter ; Nanotechnology ; Soft materials ; Quantum ; Engineering physics ; Chemical engineering ; Physics ; Quantum mechanics ; Colloid ; Engineering Electronic and Structural Properties of Oxides ; 2D Materials and Applications ; Graphene research and applications
Authors: Li Zhu | Wanying Zhu | Xin Hu | Yingying Lin | Siti Machmudah | Wahyu Diono | Hideki Kanda | Motonobu Goto
Source: Polymers (article, 2022-02-02)
Field: Materials Science — Biomaterials

PVP/Highly Dispersed AgNPs Nanofibers Using Ultrasonic-Assisted Electrospinning Silver nanoparticles (AgNPs) are novel materials with antibacterial, antifungal, and antiviral activities over a wide range. This study aimed to prepare polyvinylpyrrolidone (PVP) electrospinning composites with uniformly distributed AgNPs. In this study, starch-capped ~2 nm primary AgNPs were first synthesized using Atmospheric pressure Pulsed Discharge Plasma (APDP) at AC 10 kV and 10 kHz. Then, 0.6 wt.% AgNPs were mixed into a 10 wt.% PVP ethanol-based polymer solution and coiled through an Ultrasonic-assisted Electrospinning device (US-ES) with a 50 W and 50 kHz ultrasonic generator. At 12 kV and a distance of 10 cm, this work successfully fabricated AgNPs-PVP electrospun fibers. The electrospun products were characterized using Scanning Electron Microscopy (SEM), Transmission Electron Microscopy (TEM), High-Resolution TEM (HR-TEM), Fourier Transform Infrared Spectroscopy (FT-IR), X-ray Diffraction (XRD), Thermogravimetric (TG), and X-ray Photoelectron Spectroscopy (XPS) methods. Electrospinning ; Polyvinylpyrrolidone ; Materials science ; Nanofiber ; Thermogravimetric analysis ; X-ray photoelectron spectroscopy ; Scanning electron microscope ; Transmission electron microscopy ; Chemical engineering ; Fourier transform infrared spectroscopy ; Silver nanoparticle ; Nuclear chemistry ; Nanoparticle ; Polymer ; Nanotechnology ; Composite material ; Polymer chemistry ; Chemistry Electrospun Nanofibers in Biomedical Applications ; Polymer Nanocomposite Synthesis and Irradiation ; Graphene and Nanomaterials Applications
Authors: Joel Yeo | Huifei Jin | Armando Rodrigo Mor | Chau Yuen | Norasage Pattanadech | Wayes Tushar | Tapan Kumar Saha | Chee Seng Ng
Source: IEEE Transactions on Power Delivery (article, 2022-06-16)
Field: Materials Science — Materials Chemistry

Localisation of Partial Discharge in Power Cables Through Multi-Output Convolutional Recurrent Neural Network and Feature Extraction This paper proposes an algorithmic approach constructed from a convolutional recurrent neural network (CRNN) iterated with examination of extracted features for partial discharge (PD) localisation; tests were conducted offline on medium voltage (MV) power cables. To evaluate the performance of the algorithm, a case study was performed on 7 cables deliberately selected to comprehensively illustrate the difficulties encountered in field testing. The experimental test results prove that the proposed concept is able to identify and localise discharges besmirched with significant quantities of noise. Main contribution of the methodology is the successful automated interpretation of measurements acquired under noisy challenging field constraints. Partial discharge ; Convolutional neural network ; Feature extraction ; Computer science ; Iterated function ; Recurrent neural network ; Noise (video) ; Field (mathematics) ; Artificial intelligence ; Power (physics) ; Feature (linguistics) ; Artificial neural network ; Convolution (computer science) ; Pattern recognition (psychology) ; Voltage ; Algorithm ; Electronic engineering ; Engineering ; Mathematics ; Electrical engineering High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Image and Signal Denoising Methods
Authors: Rebecca J. Clements | Joshua Dickman | Jay Johal | Jennie Martin | Joseph Glover | Graeme M. Day
Source: MRS Bulletin (article, 2022-10-01)
Field: Materials Science — Materials Chemistry

Roles and opportunities for machine learning in organic molecular crystal structure prediction and its applications Abstract The field of crystal structure prediction (CSP) has changed dramatically over the past decade and methods now exist that will strongly influence the way that new materials are discovered, in areas such as pharmaceutical materials and the discovery of new, functional molecular materials with targeted properties. Machine learning (ML) methods, which are being applied in many areas of chemistry, are starting to be explored for CSP. This article discusses the areas where ML is expected to have the greatest impact on CSP and its applications: improving the evaluation of energies; analyzing the landscapes of predicted structures and for the identification of promising molecules for a target property. Graphical abstract Identification (biology) ; Computer science ; Crystal structure prediction ; Field (mathematics) ; Property (philosophy) ; Nanotechnology ; Data science ; Machine learning ; Artificial intelligence ; Molecule ; Materials science ; Chemistry ; Mathematics ; Epistemology ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Stefan Gugler | Markus Reiher
Source: Journal of Chemical Theory and Computation (article, 2022-10-11)
Field: Materials Science — Materials Chemistry

Quantum Chemical Roots of Machine-Learning Molecular Similarity Descriptors In this work, we explore the quantum chemical foundations of descriptors for molecular similarity. Such descriptors are key for traversing chemical compound space with machine learning. Our focus is on the Coulomb matrix and on the smooth overlap of atomic positions (SOAP). We adopt a basic framework that allows us to connect both descriptors to electronic structure theory. This framework enables us to then define two new descriptors that are more closely related to electronic structure theory, which we call Coulomb lists and smooth overlap of electron densities (SOED). By investigating their usefulness as molecular similarity descriptors, we gain new insights into how and why Coulomb matrix and SOAP work. Moreover, Coulomb lists avoid the somewhat mysterious diagonalization step of the Coulomb matrix and might provide a direct means to extract subsystem information that can be compared across Born-Oppenheimer surfaces of varying dimension. For the electron density, we derive the necessary formalism to create the SOED measure in close analogy to SOAP. Because this formalism is more involved than that of SOAP, we review the essential theory as well as introduce a set of approximations that eventually allow us to work with SOED in terms of the same implementation available for the evaluation of SOAP. We focus our analysis on elementary reaction steps, where transition state structures are more similar to either reactant or product structures than the latter two are with respect to one another. The prediction of electronic energies of transition state structures can, however, be more difficult than that of stable intermediates due to multi-configurational effects. The question arises to what extent molecular similarity descriptors rooted in electronic structure theory can resolve these intricate effects. Coulomb ; Computer science ; Similarity (geometry) ; Chemical space ; Electronic structure ; Analogy ; Formalism (music) ; Statistical physics ; Artificial intelligence ; Chemical physics ; Quantum mechanics ; Computational chemistry ; Physics ; Chemistry ; Electron ; Drug discovery Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Chenru Duan | Aditya Nandy | Husain Adamji | Yuriy Román‐Leshkov | Heather J. Kulik
Source: Journal of Chemical Theory and Computation (article, 2022-06-23)
Field: Materials Science — Materials Chemistry

Machine Learning Models Predict Calculation Outcomes with the Transferability Necessary for Computational Catalysis Virtual high-throughput screening (VHTS) and machine learning (ML) have greatly accelerated the design of single-site transition-metal catalysts. VHTS of catalysts, however, is often accompanied with a high calculation failure rate and wasted computational resources due to the difficulty of simultaneously converging all mechanistically relevant reactive intermediates to expected geometries and electronic states. We demonstrate a dynamic classifier approach, i.e., a convolutional neural network that monitors geometry optimizations on the fly, and exploit its good performance and transferability in identifying geometry optimization failures for catalyst design. We show that the dynamic classifier performs well on all reactive intermediates in the representative catalytic cycle of the radical rebound mechanism for the conversion of methane to methanol despite being trained on only one reactive intermediate. The dynamic classifier also generalizes to chemically distinct intermediates and metal centers absent from the training data without loss of accuracy or model confidence. We rationalize this superior model transferability as arising from the use of electronic structure and geometric information generated on-the-fly from density functional theory calculations and the convolutional layer in the dynamic classifier. When used in combination with uncertainty quantification, the dynamic classifier saves more than half of the computational resources that would have been wasted on unsuccessful calculations for all reactive intermediates being considered. Classifier (UML) ; Transferability ; Computer science ; Convolutional neural network ; Exploit ; Artificial intelligence ; Catalysis ; Machine learning ; Chemistry Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Electrocatalysts for Energy Conversion
Authors: Rojalina Priyadarshini | Hillol Joardar | Sukant Kishoro Bisoy | T. Badapanda
Source: Solid State Communications (article, 2022-12-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Crystal structural prediction of perovskite materials using machine learning: A comparative study Boosting (machine learning) ; Oversampling ; Artificial intelligence ; Gradient boosting ; Machine learning ; Computer science ; Algorithm ; Outlier ; Crystal structure prediction ; Crystal (programming language) ; Test set ; Crystal structure ; Test data ; Data mining ; Chemistry ; Crystallography ; Random forest Multiferroics and related materials ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Florian Göltl | Manos Mavrikakis
Source: ChemCatChem (article, 2022-10-14)
Field: Materials Science — Materials Chemistry

Generalized Brønsted‐Evans‐Polanyi Relationships for Reactions on Metal Surfaces from Machine Learning Abstract Brønsted‐Evans‐Polanyi (BEP) relationships, i. e., a linear scaling between reaction and activation energies, lie at the core of computational design of heterogeneous catalysts. However, BEPs are not general and often require reparameterization for each class of reactions. Here we construct generalized BEPs (gBEPs), which can predict activation energies for a diverse dataset of reactions of C, O, N and H containing molecules on metal surfaces. In a first step we develop a set of descriptors based on scaling relationships that can capture the change in chemical identity of reactants during the reaction. Subsequently, we use the reaction energy, these descriptors and a single descriptor for the surface structure to parameterize machine learning based regression approaches for the prediction of activation energies. The best approach we developed shows a Mean Absolute Error (MAE) of 0.11 eV for the training set (80 % of the data set) and 0.23 eV for the test set (20 % of the data set). The methodology presented here allows to calculate activation energies within fractions of seconds on a typical personal computer and due to its generality, accuracy and simplicity in application it might prove to be useful in transition metal catalyst design. Generality ; Scaling ; Test set ; Set (abstract data type) ; Activation energy ; Computer science ; Chemistry ; Mathematics ; Artificial intelligence ; Physical chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Computational Drug Discovery Methods
Authors: Kai Guo | Hao Lü | Zhi Hao Zhao | Fawei Tang | Haibin Wang | Xiaoyan Song
Source: Computational Materials Science (article, 2022-02-04)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Magnetic performance oriented composition design of Sm-Co based alloys by machine learning and experimental studies Atomic radius ; Magnet ; Materials science ; Pseudopotential ; Valence (chemistry) ; Computer science ; Thermodynamics ; Chemistry ; Condensed matter physics ; Mechanical engineering ; Physics ; Engineering Magnetic Properties of Alloys ; Magnetic Properties and Applications ; Hydrogen Storage and Materials
Authors: Chi Zhang | Yunchao Xie | Chen Xie | Hongxing Dong | Long Zhang | Jian Lin
Source: MRS Bulletin (review, 2022-04-01)
Field: Chemistry — Inorganic Chemistry

Accelerated discovery of porous materials for carbon capture by machine learning: A review Flourishing ; Workflow ; Greenhouse gas ; Carbon fibers ; Carbon capture and storage (timeline) ; Computer science ; Nanotechnology ; Materials science ; Climate change ; Environmental science ; Process engineering ; Engineering ; Geology Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies ; Covalent Organic Framework Applications
Authors: Tae‐Hyun Kim | Jeong-Hyeon Park | Ki Wook Jung | Jaechoon Kim | Eun‐Ho Lee
Source: IEEE Access (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Application of Convolutional Neural Network to Predict Anisotropic Effective Thermal Conductivity of Semiconductor Package With increasing complexity of design patterns in semiconductor package substrates caused by demand for high-power semiconductors, it is necessary to be able to predict the thermal properties according to the pattern. Classifying the patterns is important to predict the effective thermal conductivity (ETC), but it has some difficulties due to the variable setting being labor-intensive and creating human uncertainty. These difficulties are amplified by the complexity of the pattern in the printed circuit board (PCB) substrate. This work presents an automated convolutional neural network (CNN)-based algorithm to infer the anisotropic ETCs of package substrates. This algorithm divides a layer-pattern image of a PCB into local unit-cell images and learns the pattern characteristics of each unit cell to identify the local ETC. The algorithm then builds an ETC map by integrating the local ETCs for the entire layer. The entire process is fully automated to reduce human uncertainty and required workforce. The ETC map from the algorithm was then used in finite element (FE) analysis and compared with three other prediction methods. The proposed algorithm can predict the anisotropic ETCs within 2–3 % errors compared to the reference data while other models lead to at least 16 % error. The FE simulation with the ETC map of the algorithm can reflect the effect of the design pattern on the heat flux and temperature distributions on the package layer, leading to the lowest root mean square error in the temperature distribution compared to other models. Convolutional neural network ; Computer science ; Artificial neural network ; Mean squared error ; Thermal conductivity ; Algorithm ; Integrated circuit packaging ; Layer (electronics) ; Integrated circuit ; Pattern recognition (psychology) ; Artificial intelligence ; Materials science ; Mathematics ; Statistics Thermal properties of materials ; Silicon Carbide Semiconductor Technologies ; Electronic Packaging and Soldering Technologies
Authors: Yunfan Jiang | Jingjing Si | Rui Zhang | Godwin Enemali | Bin Zhou | H. McCann | Chang Liu
Source: IEEE Transactions on Neural Networks and Learning Systems (article, 2022-03-24)
Field: Chemistry — Spectroscopy

CSTNet: A Dual-Branch Convolutional Neural Network for Imaging of Reactive Flows Using Chemical Species Tomography Chemical species tomography (CST) has been widely used for in situ imaging of critical parameters, e.g., species concentration and temperature, in reactive flows. However, even with state-of-the-art computational algorithms, the method is limited due to the inherently ill-posed and rank-deficient tomographic data inversion and by high computational cost. These issues hinder its application for real-time flow diagnosis. To address them, we present here a novel convolutional neural network, namely CSTNet, for high-fidelity, rapid, and simultaneous imaging of species concentration and temperature using CST. CSTNet introduces a shared feature extractor that incorporates the CST measurements and sensor layout into the learning network. In addition, a dual-branch decoder with internal crosstalk, which automatically learns the naturally correlated distributions of species concentration and temperature, is proposed for image reconstructions. The proposed CSTNet is validated both with simulated datasets and with measured data from real flames in experiments using an industry-oriented sensor. Superior performance is found relative to previous approaches in terms of reconstruction accuracy and robustness to measurement noise. This is the first time, to the best of our knowledge, that a deep learning-based method for CST has been experimentally validated for simultaneous imaging of multiple critical parameters in reactive flows using a low-complexity optical sensor with a severely limited number of laser beams. Robustness (evolution) ; Convolutional neural network ; Computer science ; Tomography ; Inverse problem ; Artificial intelligence ; Deep learning ; Algorithm ; Artificial neural network ; Pattern recognition (psychology) ; Optics ; Mathematics ; Physics Spectroscopy and Laser Applications ; Combustion and flame dynamics ; Atmospheric and Environmental Gas Dynamics
Authors: Daniel R. Harper | Aditya Nandy | N. Arunachalam | Chenru Duan | Jon Paul Janet | Heather J. Kulik
Source: The Journal of Chemical Physics (article, 2022-01-27)
Field: Materials Science — Materials Chemistry

Representations and strategies for transferable machine learning improve model performance in chemical discovery Strategies for machine-learning (ML)-accelerated discovery that are general across material composition spaces are essential, but demonstrations of ML have been primarily limited to narrow composition variations. By addressing the scarcity of data in promising regions of chemical space for challenging targets such as open-shell transition-metal complexes, general representations and transferable ML models that leverage known relationships in existing data will accelerate discovery. Over a large set (∼1000) of isovalent transition-metal complexes, we quantify evident relationships for different properties (i.e., spin-splitting and ligand dissociation) between rows of the Periodic Table (i.e., 3d/4d metals and 2p/3p ligands). We demonstrate an extension to the graph-based revised autocorrelation (RAC) representation (i.e., eRAC) that incorporates the group number alongside the nuclear charge heuristic that otherwise overestimates dissimilarity of isovalent complexes. To address the common challenge of discovery in a new space where data are limited, we introduce a transfer learning approach in which we seed models trained on a large amount of data from one row of the Periodic Table with a small number of data points from the additional row. We demonstrate the synergistic value of the eRACs alongside this transfer learning strategy to consistently improve model performance. Analysis of these models highlights how the approach succeeds by reordering the distances between complexes to be more consistent with the Periodic Table, a property we expect to be broadly useful for other material domains. Chemical space ; Computer science ; Leverage (statistics) ; Row ; Heuristic ; Theoretical computer science ; Machine learning ; Drug discovery ; Artificial intelligence ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics