jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Brandon Faceira | Lionel Teulé‐Gay | Gian‐Marco Rignanese | Aline Rougier
Source: The Journal of Physical Chemistry Letters (article, 2022-08-23)
Field: Materials Science — Polymers and Plastics

Toward the Prediction of Electrochromic Properties of WO<sub>3</sub> Films: Combination of Experimental and Machine Learning Approaches WO<sub>3</sub> is the state of the art of electrochromic oxide materials finding technological application in smart windows. In this work, a set of WO<sub>3</sub> thin films were deposited by magnetron sputtering by varying total pressure, oxygen partial pressure, and power. On each film two properties were measured, the electrochemical reversibility and the blue color persistence of Li<sub><i>x</i></sub>WO<sub>3</sub> films in simulated ambient conditions. With the help of machine learning, prediction maps for such electrochromic properties, namely, color persistence and reversibility, were designed. High-performance WO<sub>3</sub> films were targeted by a global score which is the product of these two properties. The combined approach of experimental measurements and machine learning led to a complete picture of electrochromic properties depending of sputtering parameters providing an efficient tool in regards to time saving. Electrochromism ; Materials science ; Sputtering ; Sputter deposition ; Electrochromic devices ; Thin film ; Partial pressure ; Optoelectronics ; Oxygen ; Nanotechnology ; Electrode ; Chemistry Transition Metal Oxide Nanomaterials ; Gas Sensing Nanomaterials and Sensors ; Conducting polymers and applications
Authors: Wabi Demeke | Yongtae Kim | Jiyoung Jung | Jaywan Chung | Byungki Ryu | Seunghwa Ryu
Source: Energy Reports (article, 2022-05-20)
Field: Materials Science — Materials Chemistry

Neural network-assisted optimization of segmented thermoelectric power generators using active learning based on a genetic optimization algorithm Because the properties of thermoelectric (TE) materials are strongly dependent on temperature and differ considerably, segmented TE legs composed of multiple stacked TE materials have been investigated to provide efficient operation of TE devices. However, owing to the inherent nonlinearity that limits the application of conventional optimization approaches, the optimal configuration of segmented TEGs (STEGs) must have been sought heuristically. In this study, we propose a systematic approach that enables the efficient exploration and exploitation of the vast design space of STEGs by leveraging the fast inference of deep learning. First, we train a neural network (NN) model using a dataset generated from finite element analysis (FEA) for a single TE leg with four stacked segments from among 18 TE materials with varying segment lengths and external loads. We then use a genetic optimization algorithm (GOA) with our trained NN model to search for high-performance design candidates. The performance of the new candidates is computed and validated based on FEA, and the results are used to update the NN through an active learning technique. After iteratively performing the procedure, we can fine-tune the TE legs for optimal efficiency, optimal power, and given combinations of both power and efficiency. Furthermore, we discuss the physical origin of optimally performing STEGs. Artificial neural network ; Computer science ; Finite element method ; Genetic algorithm ; Power (physics) ; Optimal design ; Inference ; Thermoelectric generator ; Algorithm ; Artificial intelligence ; Thermoelectric effect ; Machine learning ; Engineering ; Structural engineering Advanced Thermoelectric Materials and Devices ; Thermal Radiation and Cooling Technologies ; Thermal properties of materials
Authors: Jinlei Shen | Shravan Kotha | Ryan Noraas | Vasisht Venkatesh | Somnath Ghosh
Source: International Journal of Plasticity (article, 2022-01-13)
Field: Materials Science — Materials Chemistry

Developing parametrically upscaled constitutive and crack nucleation models for the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e6631" altimg="si5.svg"><mml:mrow><mml:mi>α</mml:mi><mml:mo>/</mml:mo><mml:mi>β</mml:mi></mml:mrow></mml:math> Ti64 alloy Nucleation ; Constitutive equation ; Materials science ; Microstructure ; Plasticity ; Mechanics ; Structural engineering ; Metallurgy ; Thermodynamics ; Composite material ; Physics ; Engineering Microstructure and mechanical properties ; Titanium Alloys Microstructure and Properties ; Metallurgy and Material Forming
Authors: Nicolas A. Lee | Sabrina C. Shen | Markus J. Buehler
Source: Matter (article, 2022-11-01)
Field: Materials Science — Materials Chemistry

An automated biomateriomics platform for sustainable programmable materials discovery Process (computing) ; Computer science ; Characterization (materials science) ; Artificial intelligence ; Data science ; Machine learning ; Systems engineering ; Biochemical engineering ; Nanotechnology ; Engineering ; Materials science Machine Learning in Materials Science ; Material Selection and Properties ; Design Education and Practice
Authors: William Yi Wang | Junlei Yin | Zaixian Chai | Xin Chen | Wenping Zhao | Jiaqi Lu | Feng Sun | Qinggong Jia | Xingyu Gao | Bin Tang | Xidong Hui | Haifeng Song | Fei Xue | Zi‐Kui Liu | Jinshan Li
Source: Journal of Materials Informatics (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Big data-assisted digital twins for the smart design and manufacturing of advanced materials: from atoms to products Motivated by the ever-increasing wealth of data boosted by national strategies in terms of data-driven Integrated Computational Materials Engineering (ICME), Materials Genome Engineering, Materials Genome Infrastructures, Industry 4.0, Materials 4.0 and so on, materials informatics represents a unique strategy in revealing the fundamental relationships in the development and manufacturing of advanced materials. Materials developments are becoming ever more integrated with robust data-driven and data-intensive technologies. In the present review, big data-assisted digital twins (DTs) for the smart design and manufacturing of advanced materials are presented from the perspective of the digital thread. In the introduction of the DT design paradigm in the ICME era, the simulation aspects of DT and the data and design infrastructures are discussed. Referring to the simulation and theoretical factors of DTs, high-throughput simulation and automation and artificial intelligence-assisted multiscale atomistic modeling are detailed through several cases studies. With respect to data and data mining technologies, entropy and its application for attribute selection in decision trees are discussed to emphasize knowledge-based modeling, simulation and data analysis in machine learning coherently. Guided by the perspectives and case studies of the digital thread, we present our recent work on the design, manufacturing and product service via big data-assisted DTs for smart design and manufacturing by integrating some of these advanced concepts and technologies. It is believed that big data-assisted DTs for smart design and manufacturing effectively support better products with the application of novel materials by reducing the time and cost of materials design and deployment. Big data ; Computer science ; Data science ; Automation ; Thread (computing) ; Industry 4.0 ; Digital manufacturing ; Manufacturing engineering ; Systems engineering ; Industrial engineering ; Engineering ; Data mining ; Mechanical engineering Machine Learning in Materials Science ; Additive Manufacturing and 3D Printing Technologies ; Manufacturing Process and Optimization
Authors: Zhilong Wang | Yanqiang Han | Junfei Cai | An Chen | Jinjin Li
Source: Journal of Energy Chemistry (article, 2022-04-07)
Field: Materials Science — Materials Chemistry

Vision for energy material design: A roadmap for integrated data-driven modeling Systems engineering ; Computer science ; Nanotechnology ; Materials science ; Engineering Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Catalysis and Oxidation Reactions
Authors: Konstantin Karandashev | O. Anatole von Lilienfeld
Source: The Journal of Chemical Physics (article, 2022-03-15)
Field: Materials Science — Materials Chemistry

An orbital-based representation for accurate quantum machine learning We introduce an electronic structure based representation for quantum machine learning (QML) of electronic properties throughout chemical compound space. The representation is constructed using computationally inexpensive ab initio calculations and explicitly accounts for changes in the electronic structure. We demonstrate the accuracy and flexibility of resulting QML models when applied to property labels, such as total potential energy, HOMO and LUMO energies, ionization potential, and electron affinity, using as datasets for training and testing entries from the QM7b, QM7b-T, QM9, and LIBE libraries. For the latter, we also demonstrate the ability of this approach to account for molecular species of different charge and spin multiplicity, resulting in QML models that infer total potential energies based on geometry, charge, and spin as input. HOMO/LUMO ; Representation (politics) ; Ab initio ; Ionization energy ; Electronic structure ; Charge (physics) ; Quantum chemistry ; Electron ; Multiplicity (mathematics) ; Spin (aerodynamics) ; Quantum chemical ; Quantum ; Flexibility (engineering) ; Physics ; Computer science ; Statistical physics ; Ionization ; Quantum mechanics ; Mathematics ; Molecule ; Geometry ; Thermodynamics ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Dongxiao Chen | Cheng Shang | Zhi‐Pan Liu
Source: The Journal of Chemical Physics (article, 2022-02-07)
Field: Materials Science — Materials Chemistry

Automated search for optimal surface phases (ASOPs) in grand canonical ensemble powered by machine learning The surface of a material often undergoes dramatic structure evolution under a chemical environment, which, in turn, helps determine the different properties of the material. Here, we develop a general-purpose method for the automated search of optimal surface phases (ASOPs) in the grand canonical ensemble, which is facilitated by the stochastic surface walking (SSW) global optimization based on global neural network (G-NN) potential. The ASOP simulation starts by enumerating a series of composition grids, then utilizes SSW-NN to explore the configuration and composition spaces of surface phases, and relies on the Monte Carlo scheme to focus on energetically favorable compositions. The method is applied to silver surface oxide formation under the catalytic ethene epoxidation conditions. The known phases of surface oxides on Ag(111) are reproduced, and new phases on Ag(100) are revealed, which exhibit novel structure features that could be critical for understanding ethene epoxidation. Our results demonstrate that the ASOP method provides an automated and efficient way for probing complex surface structures that are beneficial for designing new functional materials under working conditions. Surface (topology) ; Canonical ensemble ; Grand canonical ensemble ; Focus (optics) ; Artificial neural network ; Monte Carlo method ; Canonical correlation ; Computer science ; Composition (language) ; Oxide ; Materials science ; Series (stratigraphy) ; Statistical physics ; Biological system ; Physics ; Artificial intelligence ; Mathematics ; Geometry Machine Learning in Materials Science ; Quantum Dots Synthesis And Properties ; Catalytic Processes in Materials Science
Authors: Jacob B. J. Chapman | Pui-Wai Ma
Source: Scientific Reports (article, 2022-12-27)
Field: Materials Science — Materials Chemistry

A machine-learned spin-lattice potential for dynamic simulations of defective magnetic iron A machine-learned spin-lattice interatomic potential (MSLP) for magnetic iron is developed and applied to mesoscopic scale defects. It is achieved by augmenting a spin-lattice Hamiltonian with a neural network term trained to descriptors representing a mix of local atomic configuration and magnetic environments. It reproduces the cohesive energy of BCC and FCC phases with various magnetic states. It predicts the formation energy and complex magnetic structure of point defects in quantitative agreement with density functional theory (DFT) including the reversal and quenching of magnetic moments near the core of defects. The Curie temperature is calculated through spin-lattice dynamics showing good computational stability at high temperature. The potential is applied to study magnetic fluctuations near sizable dislocation loops. The MSLP transcends current treatments using DFT and molecular dynamics, and surpasses other spin-lattice potentials that only treat near-perfect crystal cases. Condensed matter physics ; Mesoscopic physics ; Magnetic moment ; Physics ; Lattice (music) ; Curie temperature ; Embedded atom model ; Interatomic potential ; Molecular dynamics ; Materials science ; Ferromagnetism ; Quantum mechanics Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Microstructure and Mechanical Properties of Steels
Authors: Michael Grossutti | Joseph D’Amico | Jonathan Quintal | Hugh MacFarlane | Amanda Quirk | John Dutcher
Source: The Journal of Physical Chemistry Letters (article, 2022-06-21)
Field: Chemistry — Analytical Chemistry

Deep Learning and Infrared Spectroscopy: Representation Learning with a β-Variational Autoencoder Infrared (IR) spectra contain detailed and extensive information about the chemical composition and bonding environment in a sample. However, this information is difficult to extract from complex heterogeneous systems because of overlapping absorptions due to different generative factors. We implement a deep learning approach to study the complex spectroscopic changes that occur in cross-linked polyethylene (PEX-a) pipe by training a β-variational autoencoder (β-VAE) on a database of PEX-a pipe spectra. We show that the β-VAE outperforms principal component analysis (PCA) and learns interpretable and independent representations of the generative factors of variance in the spectra. We apply the β-VAE encoder to a hyperspectrum of a crack in the wall of a pipe to evaluate the spatial distribution of these learned representations. This study shows how deep learning architectures like β-VAE can enhance the analysis of spectroscopic data of complex heterogeneous systems. Autoencoder ; Principal component analysis ; Pattern recognition (psychology) ; Artificial intelligence ; Representation (politics) ; Computer science ; Infrared spectroscopy ; Deep learning ; Generative model ; Infrared ; Feature learning ; Spectral line ; Biological system ; Encoder ; Sample (material) ; Generative grammar ; Machine learning ; Chemistry ; Physics ; Optics ; Organic chemistry Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Hongmei Yuan | Shulin Han | Rui Hu | Wen-Na Jiao | Mengke Li | Huijun Liu | Ying Fang
Source: Materials Today Physics (article, 2022-05-06)
Field: Materials Science — Materials Chemistry

Machine learning for accelerated prediction of the Seebeck coefficient at arbitrary carrier concentration Seebeck coefficient ; Thermoelectric effect ; Artificial neural network ; Materials science ; Independence (probability theory) ; Correlation coefficient ; Power (physics) ; Thermoelectric materials ; Metrology ; Condensed matter physics ; Machine learning ; Computer science ; Thermodynamics ; Physics ; Statistics ; Mathematics Advanced Thermoelectric Materials and Devices ; Heusler alloys: electronic and magnetic properties ; Machine Learning in Materials Science
Authors: Andrea Grisafi | Alan M. Lewis | Mariana Rossi | Michele Ceriotti
Source: Journal of Chemical Theory and Computation (article, 2022-12-01)
Field: Materials Science — Materials Chemistry

Electronic-Structure Properties from Atom-Centered Predictions of the Electron Density The electron density of a molecule or material has recently received major attention as a target quantity of machine-learning models. A natural choice to construct a model that yields transferable and linear-scaling predictions is to represent the scalar field using a multicentered atomic basis analogous to that routinely used in density fitting approximations. However, the nonorthogonality of the basis poses challenges for the learning exercise, as it requires accounting for all the atomic density components at once. We devise a gradient-based approach to directly minimize the loss function of the regression problem in an optimized and highly sparse feature space. In so doing, we overcome the limitations associated with adopting an atom-centered model to learn the electron density over arbitrarily complex data sets, obtaining very accurate predictions using a comparatively small training set. The enhanced framework is tested on 32-molecule periodic cells of liquid water, presenting enough complexity to require an optimal balance between accuracy and computational efficiency. We show that starting from the predicted density a single Kohn-Sham diagonalization step can be performed to access total energy components that carry an error of just 0.1 meV/atom with respect to the reference density functional calculations. Finally, we test our method on the highly heterogeneous QM9 benchmark data set, showing that a small fraction of the training data is enough to derive ground-state total energies within chemical accuracy. Computer science ; Atom (system on chip) ; Benchmark (surveying) ; Electron density ; Scaling ; Basis (linear algebra) ; Statistical physics ; Test set ; Set (abstract data type) ; Algorithm ; Electron ; Physics ; Mathematics ; Artificial intelligence ; Quantum mechanics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Computational Drug Discovery Methods
Authors: Miguel Ángel Delgado Noguera | Borja Millán | José Manuel Andújar
Source: Agriculture (article, 2022-12-20)
Field: Chemistry — Analytical Chemistry

New, Low-Cost, Hand-Held Multispectral Device for In-Field Fruit-Ripening Assessment The state of ripeness at harvest is a key piece of information for growers as it determines the market price of the yield. This has been traditionally assessed by destructive chemical methods, which lead to low-spatiotemporal resolution in the monitorization of crop development and poor responsiveness for growers. These limitations have shifted the focus to remote-sensing, spectroscopy-based approaches. However, most of the research focusing on these approaches has been accomplished with expensive equipment, which is exorbitant for most users. To combat this issue, this work presents a low-cost, hand-held, multispectral device with original hardware specially designed to face the complexity related to in-field use. The proposed device is based on a development board (AS7265x, AMS AG) that has three sensor chips with a spectral response of eighteen channels in a range from 410 to 940 nm. The proposed device was evaluated in a red-grape field experiment. Briefly, it was used to acquire the spectral signature of eighty red-grape samples in the vineyard. Subsequently, the grape samples were analysed using standard chemical methods to generate ground-truth values of ripening status indicators (soluble solid content (SSC) and titratable acidity (TA)). The eighteen pre-process reflectance measurements were used as input for training artificial neural network models to estimate the two target parameters (SSC and TA). The developed estimation models were evaluated through a leave-one-out cross-validation approach obtaining promising results (R2 = 0.70, RMSE = 1.21 for SSC; and R2 = 0.67, RMSE = 0.91 for TA). Ripeness ; Multispectral image ; Mean squared error ; Computer science ; Ripening ; Ground truth ; Remote sensing ; Spectroradiometer ; Environmental science ; Artificial intelligence ; Mathematics ; Statistics ; Reflectivity ; Geography ; Chemistry ; Food science ; Optics Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Horticultural and Viticultural Research
Authors: David Buterez | Jon Paul Janet | Steven J. Kiddle | Dino Oglić | Píetro Lió
Source: arXiv (Cornell University) (preprint, 2022-11-09)
Field: Materials Science — Materials Chemistry

Graph Neural Networks with Adaptive Readouts An effective aggregation of node features into a graph-level representation via readout functions is an essential step in numerous learning tasks involving graph neural networks. Typically, readouts are simple and non-adaptive functions designed such that the resulting hypothesis space is permutation invariant. Prior work on deep sets indicates that such readouts might require complex node embeddings that can be difficult to learn via standard neighborhood aggregation schemes. Motivated by this, we investigate the potential of adaptive readouts given by neural networks that do not necessarily give rise to permutation invariant hypothesis spaces. We argue that in some problems such as binding affinity prediction where molecules are typically presented in a canonical form it might be possible to relax the constraints on permutation invariance of the hypothesis space and learn a more effective model of the affinity by employing an adaptive readout function. Our empirical results demonstrate the effectiveness of neural readouts on more than 40 datasets spanning different domains and graph characteristics. Moreover, we observe a consistent improvement over standard readouts (i.e., sum, max, and mean) relative to the number of neighborhood aggregation iterations and different convolutional operators. Invariant (physics) ; Permutation (music) ; Computer science ; Graph ; Representation (politics) ; Artificial neural network ; Theoretical computer science ; Artificial intelligence ; Algorithm ; Mathematics ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Ke Yang | Kai Wu | Fangzhou Li | Liangyuan Jia | Sheng Wang | Huiyan Zhang
Source: Fuel Processing Technology (article, 2022-11-17)
Field: Materials Science — Polymers and Plastics

Investigation on the co-pyrolysis of bamboo sawdust and low-density polyethylene via online photoionization mass spectrometry and machine learning methods Low-density polyethylene ; Sawdust ; Pyrolysis ; Polyethylene ; Biomass (ecology) ; Cellulose ; Chemical engineering ; Hemicellulose ; Lignin ; Pulp and paper industry ; Pyrolysis–gas chromatography–mass spectrometry ; Materials science ; Chemistry ; Mass spectrometry ; Raw material ; Organic chemistry ; Chromatography Natural Fiber Reinforced Composites ; Thermochemical Biomass Conversion Processes ; Bamboo properties and applications