jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hadeel Moustafa | Peder Lyngby | Jens Jørgen Mortensen | Kristian S. Thygesen | Karsten W. Jacobsen
Source: arXiv (Cornell University) (preprint, 2022-10-17)
Field: Materials Science — Materials Chemistry

Hundreds of new, stable, one-dimensional materials from a generative machine learning model We use a generative neural network model to create thousands of new, one-dimensional materials. The model is trained using 508 stable one-dimensional materials from the Computational 1D Materials Database (C1DB) database. More than 500 of the new materials are shown with density functional theory calculations to be dynamically stable and with heats of formation within 0.2 eV of the convex hull of known materials. Some of the new materials could also have been obtained by chemical element substitution in the training materials, but completely new classes of materials are also produced. The band structures, electronic densities of states, work functions, effective masses, and phonon spectra of the new materials are calculated, and the data are added to C1DB. Generative grammar ; Convex hull ; Artificial neural network ; Density functional theory ; Stability (learning theory) ; Phonon ; Materials science ; Element (criminal law) ; Work (physics) ; Generative model ; Computer science ; Regular polygon ; Artificial intelligence ; Physics ; Machine learning ; Computational chemistry ; Chemistry ; Mathematics ; Geometry ; Condensed matter physics ; Thermodynamics Machine Learning in Materials Science
Authors: Jingxi Li | Tianyi Gan | Yifan Zhao | Bijie Bai | Che‐Yung Shen | Songyu Sun | Mona Jarrahi | Aydogan Özcan
Source: arXiv (Cornell University) (preprint, 2022-12-05)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Unidirectional Imaging using Deep Learning-Designed Materials A unidirectional imager would only permit image formation along one direction, from an input field-of-view (FOV) A to an output FOV B, and in the reverse path, the image formation would be blocked. Here, we report the first demonstration of unidirectional imagers, presenting polarization-insensitive and broadband unidirectional imaging based on successive diffractive layers that are linear and isotropic. These diffractive layers are optimized using deep learning and consist of hundreds of thousands of diffractive phase features, which collectively modulate the incoming fields and project an intensity image of the input onto an output FOV, while blocking the image formation in the reverse direction. After their deep learning-based training, the resulting diffractive layers are fabricated to form a unidirectional imager. As a reciprocal device, the diffractive unidirectional imager has asymmetric mode processing capabilities in the forward and backward directions, where the optical modes from B to A are selectively guided/scattered to miss the output FOV, whereas for the forward direction such modal losses are minimized, yielding an ideal imaging system between the input and output FOVs. Although trained using monochromatic illumination, the diffractive unidirectional imager maintains its functionality over a large spectral band and works under broadband illumination. We experimentally validated this unidirectional imager using terahertz radiation, very well matching our numerical results. Using the same deep learning-based design strategy, we also created a wavelength-selective unidirectional imager, where two unidirectional imaging operations, in reverse directions, are multiplexed through different illumination wavelengths. Diffractive unidirectional imaging using structured materials will have numerous applications in e.g., security, defense, telecommunications and privacy protection. Optics ; Physics ; Broadband ; Monochromatic color ; Computer science ; Polarization (electrochemistry) ; Wavelength ; Diffraction Metamaterials and Metasurfaces Applications ; Photonic and Optical Devices ; Terahertz technology and applications
Authors: Hao Yuan | Bin Cao | Kangdong You | Ziqiang Dong | TongYi ZHANG | Jubo Peng | Shanshan Cai | Xiaobin Luo | Chen LIU | JiaJun WANG
Source: Scientia Sinica Technologica (article, 2022-08-15)
Field: Materials Science — Materials Chemistry

Machine learning of the mechanical properties and data-driven design of lead-free solder alloys <p indent="0mm">The development of lead-free solder alloys with high-strength, high-toughness, good creep, thermal-fatigue resistance, and good soldering properties are essential for the industrial development of modern electronics and microelectronics. Improving the strength of solder alloys usually induces a reduction in ductility due to the tradeoff between strength and ductility, which ultimately results in deterioration in toughness. However, it is challenging to optimize these two mutually restrictive properties of solder alloys simultaneously while comprehensively improving the mechanical properties. In this study, two kinds of machine learning models were established for Sn-based solder alloys. The alloy composition and atomic scale parameters were used as input features, respectively, and tensile strength and fracture elongation were the target properties. Two machine learning models with atomic features exhibited high tensile strength and fracture elongation prediction accuracies, respectively. A virtual sample space was constructed for Sn-based alloys according to the range and variation step size of the alloying elements in the original training dataset, and the average absolute SHAP values were used to rank and select features. With the L<sub>2</sub> norm of strength and ductility as an index of the comprehensive mechanical property, two virtual samples on the Pareto front were selected for experimental verification. The experimental results were highly consistent with the ML predictions, and the selected samples exhibited improved properties in terms of their comprehensive mechanical properties. The results of this study can be used as guidance for the multi-objective optimization design of complex alloys. Soldering ; Lead (geology) ; Materials science ; Metallurgy ; Geology Titanium Alloys Microstructure and Properties ; Aluminum Alloy Microstructure Properties ; Advanced Welding Techniques Analysis
Authors: Christopher K. H. Borg | Eric S. Muckley | Clara Nyby | James E. Saal | Logan Ward | Apurva Mehta | Bryce Meredig
Source: arXiv (Cornell University) (preprint, 2022-10-24)
Field: Materials Science — Materials Chemistry

Quantifying the performance of machine learning models in materials discovery The predictive capabilities of machine learning (ML) models used in materials discovery are typically measured using simple statistics such as the root-mean-square error (RMSE) or the coefficient of determination ($r^2$) between ML-predicted materials property values and their known values. A tempting assumption is that models with low error should be effective at guiding materials discovery, and conversely, models with high error should give poor discovery performance. However, we observe that no clear connection exists between a "static" quantity averaged across an entire training set, such as RMSE, and an ML property model's ability to dynamically guide the iterative (and often extrapolative) discovery of novel materials with targeted properties. In this work, we simulate a sequential learning (SL)-guided materials discovery process and demonstrate a decoupling between traditional model error metrics and model performance in guiding materials discoveries. We show that model performance in materials discovery depends strongly on (1) the target range within the property distribution (e.g., whether a 1st or 10th decile material is desired); (2) the incorporation of uncertainty estimates in the SL acquisition function; (3) whether the scientist is interested in one discovery or many targets; and (4) how many SL iterations are allowed. To overcome the limitations of static metrics and robustly capture SL performance, we recommend metrics such as Discovery Yield ($DY$), a measure of how many high-performing materials were discovered during SL, and Discovery Probability ($DP$), a measure of likelihood of discovering high-performing materials at any point in the SL process. Mean squared error ; Computer science ; Property (philosophy) ; Machine learning ; Function (biology) ; Process (computing) ; Artificial intelligence ; Measure (data warehouse) ; Range (aeronautics) ; Data mining ; Business process discovery ; Statistics ; Mathematics ; Work in process ; Engineering Machine Learning in Materials Science ; Geochemistry and Geologic Mapping ; X-ray Diffraction in Crystallography
Authors: Jie Hu | Yongquan Jiang | Yan Yang | Houchen Zuo
Source: arXiv (Cornell University) (preprint, 2022-11-06)
Field: Materials Science — Materials Chemistry

Prediction of superconducting properties of materials based on machine learning models The application of superconducting materials is becoming more and more widespread. Traditionally, the discovery of new superconducting materials relies on the experience of experts and a large number of "trial and error" experiments, which not only increases the cost of experiments but also prolongs the period of discovering new superconducting materials. In recent years, machine learning has been increasingly applied to materials science. Based on this, this manuscript proposes the use of XGBoost model to identify superconductors; the first application of deep forest model to predict the critical temperature of superconductors; the first application of deep forest to predict the band gap of materials; and application of a new sub-network model to predict the Fermi energy level of materials. Compared with our known similar literature, all the above algorithms reach state-of-the-art. Finally, this manuscript uses the above models to search the COD public dataset and identify 50 candidate superconducting materials with possible critical temperature greater than 90 K. Superconductivity ; Machine learning ; Computer science ; Artificial intelligence ; Condensed matter physics ; Physics Machine Learning in Materials Science
Authors: Fereshteh Ramezani | Sheikh Parvez | J. Pierce Fix | Arthur Battaglin | Seamus Whyte | Nicholas J. Borys | Bradley M. Whitaker
Source: Research Square (preprint, 2022-10-18)
Field: Materials Science — Materials Chemistry

Automatic 2D Material Detection in Optical Images using Deep Learning Based Computer Vision Abstract Computer vision algorithms can quickly analyze numerous images and identify useful information with high accuracy. Recently, computer vision has been used to identify 2D materials in microscope images. 2D materials have important fundamental properties allowing for their use in many potential applications, including many in quantum information science and engineering. In order to use these materials for research and product development, single-layer 2D crystallites must be prepared through an exfoliation procedure and then identified using reflected light optical microscopy. Performing these searches manually is a time-consuming and tedious task. Deploying deep learning-based computer vision algorithms for 2D material search can automate the flake detection task with minimal need for human intervention. In this work, we have implemented a new deep learning pipeline to classify crystallites of 2D materials based on coarse thickness classifications in reflected-light optical micrographs. We have used DetectorRS as the object detector and trained it on 177 images containing hexagonal boron nitride (hBN) flakes of varying thickness. The trained model achieved a high detection accuracy for the rare category of thin flakes (&lt;50 atomic layers thick). Further analysis shows that our proposed pipeline could be generalized to various microscope settings and is robust against changes in color or substrate background. Artificial intelligence ; Computer science ; Pipeline (software) ; Computer vision ; Microscope ; Deep learning ; Task (project management) ; Object detection ; Materials science ; Pattern recognition (psychology) ; Optics ; Engineering Machine Learning in Materials Science ; Gas Sensing Nanomaterials and Sensors ; Ga2O3 and related materials
Authors: Udaykumar Gajera | D. Spiga | Danila Amoroso | Francesco Delodovici | Silvia Picozzi
Source: arXiv (Cornell University) (preprint, 2022-02-15)
Field: Materials Science — Materials Chemistry

Towards machine learning for microscopic mechanisms: a formula search for crystal structure stability based on atomic properties Machine Learning (ML) techniques are revolutionizing the way to perform efficient materials modeling. Nevertheless, not all the ML approaches allow for the understanding of microscopic mechanisms at play in different phenomena. To address the latter aspect, we propose a combinatorial machine-learning approach to obtain physical formulas based on simple and easily-accessible ingredients, such as atomic properties. The latter are used to build materials features that are finally employed, through Linear Regression, to predict the energetic stability of semiconducting binary compounds with respect to zincblende and rocksalt crystal structures. The adopted models are trained using dataset built from first-principles calculations. Our results show that already one-dimensional (1D) formulas well describe the energetics; a simple grid-search optimization of the automatically-obtained 1D-formulas enhances the prediction performances at a very small computational cost. In addition, our approach allows to highlight the role of the different atomic properties involved in the formulas. The computed formulas clearly indicate that "spatial" atomic properties (i.e. radii indicating maximum probability densities for $s,p,d$ electronic shells) drive the stabilization of one crystal structure with respect to the other, suggesting the major relevance of the radius associated to the $p$-shell of the cation species. Stability (learning theory) ; Simple (philosophy) ; Relevance (law) ; Binary number ; RADIUS ; Atomic radius ; Computer science ; Crystal (programming language) ; Statistical physics ; Hyperparameter optimization ; Artificial intelligence ; Algorithm ; Machine learning ; Physics ; Mathematics ; Quantum mechanics ; Support vector machine Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Electron Microscopy Techniques and Applications
Authors: Matteo Aldeghi | Connor W. Coley
Source: Chemical Science (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

A graph representation of molecular ensembles for polymer property prediction Synthetic polymers are versatile and widely used materials. Similar to small organic molecules, a large chemical space of such materials is hypothetically accessible. Computational property prediction and virtual screening can accelerate polymer design by prioritizing candidates expected to have favorable properties. However, in contrast to organic molecules, polymers are often not well-defined single structures but an ensemble of similar molecules, which poses unique challenges to traditional chemical representations and machine learning approaches. Here, we introduce a graph representation of molecular ensembles and an associated graph neural network architecture that is tailored to polymer property prediction. We demonstrate that this approach captures critical features of polymeric materials, like chain architecture, monomer stoichiometry, and degree of polymerization, and achieves superior accuracy to off-the-shelf cheminformatics methodologies. While doing so, we built a dataset of simulated electron affinity and ionization potential values for >40k polymers with varying monomer composition, stoichiometry, and chain architecture, which may be used in the development of other tailored machine learning approaches. The dataset and machine learning models presented in this work pave the path toward new classes of algorithms for polymer informatics and, more broadly, introduce a framework for the modeling of molecular ensembles. Cheminformatics ; Chemical space ; Computer science ; Polymer ; Graph ; Representation (politics) ; Monomer ; Molecular graph ; Materials informatics ; Graph theory ; Polymerization ; Theoretical computer science ; Artificial intelligence ; Machine learning ; Chemistry ; Computational chemistry ; Mathematics ; Drug discovery ; Engineering informatics ; Organic chemistry Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Weiluo Ren | Weizhong Fu | Wu, Xiaojie | Chen, Ji
Source: arXiv (Cornell University) (preprint, 2022-04-29)
Field: Materials Science — Materials Chemistry

Towards the ground state of molecules via diffusion Monte Carlo on neural networks Diffusion Monte Carlo (DMC) based on fixed-node approximation has enjoyed significant developments in the past decades and become one of the go-to methods when accurate ground state energy of molecules and materials is needed. The remaining bottleneck is the limitations of the inaccurate nodal structure, prohibiting more challenging electron correlation problems to be tackled with DMC. In this work, we apply the neural-network based trial wavefunction in fixed-node DMC, which allows accurate calculation of a broad range of atomic and molecular systems of different electronic characteristics. Our method is superior in both accuracy and efficiency compared to state-of-the-art neural network methods using variational Monte Carlo. Overall, this computational framework provides a new benchmark for accurate solution of correlated electronic wavefunction and also shed light on the chemical understanding of molecules. Wave function ; Bottleneck ; Diffusion Monte Carlo ; Monte Carlo method ; Benchmark (surveying) ; Statistical physics ; Node (physics) ; Artificial neural network ; Computer science ; Variational Monte Carlo ; Ground state ; Electronic structure ; Monte Carlo molecular modeling ; Quantum Monte Carlo ; Physics ; Computational chemistry ; Chemistry ; Mathematics ; Machine learning ; Quantum mechanics Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; Catalysis and Oxidation Reactions
Authors: Maryam Mohebbi
Source: Research Square (preprint, 2022-05-18)
Field: Chemistry — Analytical Chemistry

The Effect of Biopreservatives on the Chemical Properties of Tomato Paste and Predicting Results with Artificial Neural Networks <title>Abstract</title> This study aimed to develop a tomato paste using biopreservatives as substitutes for chemical preservatives. Fennel seed (Foeniculum vulgare Mill) (FSE) and Ziziphora clinopodioides Lam. extract (ZCE) were used as biopreservatives. Physico-chemical properties of tomato paste such as pH, titratable acidity and total soluble solid (brix) were measured during five weeks of storage at 4°C. The results showed that the incorporation of different concentrations of FSE and ZCE significantly (p &lt; 0.05) caused to decreasing in pH and increasing in acidity of tomato pastes. Increasing the extracts level in tomato paste samples significantly (p &lt; 0.05) resulted in increased brix during storage. Overall it can be concluded that utilization of 2 or 3% FSE as biopreservative resulted in the desirable physicochemical in tomato paste. Finally, the self-organizing map (SOM) showed that the winner neuron which has the most data is neuron 31. Input data to perceptron artificial neural network are concentration, and during different weeks (time) is considered as output. The values ​​of the correlation coefficient (R<sup>2</sup>) and the mean of squared errors (MSE) used to evaluate the perceptron artificial neural network with 1 hidden layers and 31 neurons are 0.99301 ,0.99288, 0.000016499, and 0.0085227, respectively, indicating the high accuracy of the network Titratable acid ; Foeniculum ; Correlation coefficient ; Mathematics ; Artificial neural network ; Chemistry ; Horticulture ; Food science ; Machine learning ; Biology ; Computer science ; Statistics Spectroscopy and Chemometric Analyses
Authors: Ji Hoon Park | Joona Bang | June Huh
Source: Polymer Korea (article, 2022-07-25)
Field: Materials Science — Materials Chemistry

Deep Learning Model for Prediction of Entanglement Molecular Weight of Polymers 엉킴 분자량은 고분자 사슬의 엉킴에 관한 성질로 고분자의 기계적 물성 및 동적 성질에 관련된 중요성에도 불구하고 광범위한 고분자 종에 따른 측정 데이터가 많이 부족한 실정이다. 본 연구에서는 이러한 문제점을 극복하기 위하여 심층 학습 기법을 활용하여 엉킴 분자량을 예측하고자 하였으며, 분자를 그래프 구조로 변환하여 다루는 그래프 합성곱 신경망을 이용하여 엉킴 분자량을 성공적으로 학습 및 예측하였다. 또한 데이터 수 부족으로 인한 성능의 한계점을 극복하기 위하여, 대용량 데이터를 통해 학습한 지식을 재활용하는 전이 학습 기법을 도입하여 높은 예측 성능 개선을 이루었다. 학습된 인공신경망 모델은 기존 예측 기법보다 높은 예측 성능을 보였다. 본 연구에서 사용된 기계학습 기법은 고분자 역설계 및 물성 예측 기법에 많은 도움을 줄 수 있을 것으로 기대된다. Quantum entanglement ; Polymer ; Deep learning ; Artificial intelligence ; Polymer science ; Statistical physics ; Computer science ; Materials science ; Physics ; Quantum mechanics ; Composite material ; Quantum Machine Learning in Materials Science
Authors: Minyi Dai | Mehmet F. Demirel | Xuanhan Liu | Yingyu Liang | Jia‐Mian Hu
Source: arXiv (Cornell University) (preprint, 2022-09-12)
Field: Materials Science — Materials Chemistry

Graph Neural Network for Predicting the Effective Properties of Polycrystalline Materials: A Comprehensive Analysis We develop a polycrystal graph neural network (PGNN) model for predicting the effective properties of polycrystalline materials, using the Li7La3Zr2O12 ceramic as an example. A large-scale dataset with &gt;5000 different three-dimensional polycrystalline microstructures of finite-width grain boundary is generated by Voronoi tessellation and processing of the electron backscatter diffraction images. The effective ion conductivities and elastic stiffness coefficients of these microstructures are calculated by high-throughput physics-based simulations. The optimized PGNN model achieves a low error of &lt;1.4% in predicting all three diagonal components of the effective Li-ion conductivity matrix, outperforming a linear regression model and two baseline convolutional neural network models. Sequential forward selection method is used to quantify the relative importance of selecting individual grain (boundary) features to improving the property prediction accuracy, through which both the critical and unwanted node (edge) feature can be determined. The extrapolation performance of the trained PGNN model is also investigated. The transfer learning performance is evaluated by using the PGNN model pretrained for predicting conductivities to predict the elastic properties of the same set of microstructures. Extrapolation ; Materials science ; Artificial neural network ; Computer science ; Graph ; Convolutional neural network ; Algorithm ; Artificial intelligence ; Mathematics ; Statistics ; Theoretical computer science Machine Learning in Materials Science ; Fuel Cells and Related Materials
Authors: Tahani A. Alrebdi | Fauziya Yakasai | Yakubu Wudil | F.H Alkallas
Source: SSRN Electronic Journal (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Predicting the Thermal Conductivity of Bi2te3-Based Thermoelectric Energy Materials: A Machine Learning Approach Thermal conductivity ; Materials science ; Thermoelectric materials ; Thermoelectric effect ; Mechanical engineering ; Composite material ; Engineering physics ; Thermodynamics ; Engineering ; Physics Advanced Thermoelectric Materials and Devices