jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yong Zhao | Mohammed Al‐Fahdi | Ming Hu | Edirisuriya M. Dilanga Siriwardane | Yuqi Song | Alireza Nasiri | Jianjun Hu
Source: Advanced Science (preprint, 2021-08-05)
Field: Materials Science — Materials Chemistry

High‐Throughput Discovery of Novel Cubic Crystal Materials Using Deep Generative Neural Networks High-throughput screening has become one of the major strategies for the discovery of novel functional materials. However, its effectiveness is severely limited by the lack of sufficient and diverse materials in current materials repositories such as the open quantum materials database (OQMD). Recent progress in deep learning have enabled generative strategies that learn implicit chemical rules for creating hypothetical materials with new compositions and structures. However, current materials generative models have difficulty in generating structurally diverse, chemically valid, and stable materials. Here we propose CubicGAN, a generative adversarial network (GAN) based deep neural network model for large scale generative design of novel cubic materials. When trained on 375 749 ternary materials from the OQMD database, the authors show that the model is able to not only rediscover most of the currently known cubic materials but also generate hypothetical materials of new structure prototypes. A total of 506 such materials have been verified by phonon dispersion calculation. Considering the importance of cubic materials in wide applications such as solar panels, the GAN model provides a promising approach to significantly expand existing materials repositories, enabling the discovery of new functional materials via screening. The new crystal structures discovered are freely accessible at www.carolinamatdb.org. Generative grammar ; Ternary operation ; Deep learning ; Computer science ; Throughput ; Artificial neural network ; Artificial intelligence ; Crystal (programming language) ; Generative model ; Crystal structure prediction ; Materials science ; Nanotechnology ; Machine learning ; Crystal structure ; Chemistry ; Wireless Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Topic Modeling
Authors: Samir Rom | Aishwaryo Ghosh | Anita Halder | Tanusri Saha‐Dasgupta
Source: Physical Review Materials (article, 2021-04-05)
Field: Materials Science — Materials Chemistry

Machine learning classification of binary semiconductor heterostructures Heterostructures of two semiconductors are at the heart of semiconductor devices with tremendous technological importance. The prediction and designing of semiconductor heterostructures of a specific type is a difficult materials science problem, posing a challenge to experimental and computational investigations. In this study, we first establish that the prediction of heterostructure type can be made with good accuracy from the knowledge of the band structure of constituent semiconductors. Following this, we apply machine learning, built on features characterizing constituent semiconductors, on a known dataset of binary semiconductor heterostructures extended by a synthetic minority oversampling technique. A significant feature of engineering made it possible to train a classifier model predicting the heterostructure type with an accuracy of $89%$. Using the trained model, a large number (872 number) of unknown heterostructure semiconductor types involving elemental and binary semiconductors is theoretically predicted. Interestingly, the developed scheme is found to be extendable to heterojunctions of semiconductor quantum dots. Heterojunction ; Semiconductor ; Materials science ; Binary number ; Optoelectronics ; Computer science ; Engineering physics ; Artificial intelligence ; Physics ; Mathematics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; ZnO doping and properties
Authors: Honghui Shang | Haidi Wang
Source: AIP Advances (article, 2021-03-01)
Field: Materials Science — Materials Chemistry

Anharmonic Raman spectra simulation of crystals from deep neural networks Raman spectroscopy is an effective tool to analyze the structures of various materials as it provides chemical and compositional information. However, the computation demands for Raman spectra are typically significant because quantum perturbation calculations need to be performed beyond ground state calculations. This work introduces a novel route based on deep neural networks (DNNs) and density-functional perturbation theory to access anharmonic Raman spectra for extended systems. Both the dielectric susceptibility and the potential energy surface are trained using DNNs. The ab initio anharmonic vibrational Raman spectra can be reproduced well with machine learning and DNNs. Silicon and paracetamol crystals are used as showcases to demonstrate the computational efficiency. Anharmonicity ; Raman spectroscopy ; Ab initio ; Perturbation theory (quantum mechanics) ; Spectral line ; Ab initio quantum chemistry methods ; Molecular physics ; Potential energy surface ; Artificial neural network ; Materials science ; Density functional theory ; Raman scattering ; Computational physics ; Physics ; Chemistry ; Computational chemistry ; Computer science ; Condensed matter physics ; Optics ; Quantum mechanics ; Molecule ; Machine learning Machine Learning in Materials Science ; Spectroscopy Techniques in Biomedical and Chemical Research ; Thermography and Photoacoustic Techniques
Authors: Sebastian J. R. Lee | Tamara Husch | Feizhi Ding | Thomas F. Miller
Source: The Journal of Chemical Physics (article, 2021-03-25)
Field: Materials Science — Materials Chemistry

Analytical gradients for molecular-orbital-based machine learning Molecular-orbital-based machine learning (MOB-ML) enables the prediction of accurate correlation energies at the cost of obtaining molecular orbitals. Here, we present the derivation, implementation, and numerical demonstration of MOB-ML analytical nuclear gradients, which are formulated in a general Lagrangian framework to enforce orthogonality, localization, and Brillouin constraints on the molecular orbitals. The MOB-ML gradient framework is general with respect to the regression technique (e.g., Gaussian process regression or neural networks) and the MOB feature design. We show that MOB-ML gradients are highly accurate compared to other ML methods on the ISO17 dataset while only being trained on energies for hundreds of molecules compared to energies and gradients for hundreds of thousands of molecules for the other ML methods. The MOB-ML gradients are also shown to yield accurate optimized structures at a computational cost for the gradient evaluation that is comparable to a density-corrected density functional theory calculation. Kriging ; Atomic orbital ; Molecular orbital ; Computer science ; Orthogonality ; Artificial neural network ; Gaussian ; Regression ; Statistical physics ; Brillouin zone ; Density functional theory ; Gaussian process ; Artificial intelligence ; Algorithm ; Machine learning ; Molecule ; Physics ; Mathematics ; Quantum mechanics ; Statistics ; Geometry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Bowen Gao | Mingxuan Sun | Zhipeng Ding | Wenzhu Liu
Source: Journal of Alloys and Compounds (article, 2021-05-27)
Field: Materials Science — Materials Chemistry

Machine learning-optimized synthesis of doped TiO2 with improved photocatalytic performance: A multi-step workflow supported by designed wet-lab experiments Workflow ; Machine learning ; Artificial intelligence ; Computer science ; Artificial neural network ; Bootstrapping (finance) ; Photocatalysis ; Mathematics Machine Learning in Materials Science ; Advanced Photocatalysis Techniques ; TiO2 Photocatalysis and Solar Cells
Authors: Moonsoo Ra | Younggun Boo | Jae Min Jeong | Battsetseg Jargalsaikhan | Jinha Jeong | Woong Lee
Source: RSC Advances (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Classification of crystal structures using electron diffraction patterns with a deep convolutional neural network Investigations have been made to explore the applicability of an off-the-shelf deep convolutional neural network (DCNN) architecture, residual neural network (ResNet), to the classification of the crystal structure of materials using electron diffraction patterns without prior knowledge of the material systems under consideration. The dataset required for training and validating the ResNet architectures was obtained by the computer simulation of the selected area electron diffraction (SAD) in transmission electron microscopy. Acceleration voltages, zone axes, and camera lengths were used as variables and crystal information format (CIF) files obtained from open crystal data repositories were used as inputs. The cubic crystal system was chosen as a model system and five space groups of 213, 221, 225, 227, and 229 in the cubic system were selected for the test and validation, based on the distinguishability of the SAD patterns. The simulated diffraction patterns were regrouped and labeled from the viewpoint of computer vision, <i>i.e.</i>, the way how the neural network recognizes the two-dimensional representation of three-dimensional lattice structure of crystals, for improved training and classification efficiency. Comparison of the various ResNet architectures with varying number of layers demonstrated that the ResNet101 architecture could classify the space groups with the validation accuracy of 92.607%. Convolutional neural network ; Computer science ; Artificial neural network ; Diffraction ; Electron diffraction ; Artificial intelligence ; Residual neural network ; Pattern recognition (psychology) ; Crystal (programming language) ; Crystal structure ; Crystallography ; Optics ; Physics ; Chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Hongliang Yang | Yifan Zhu | Erting Dong | Yabei Wu | Jiong Yang | Wenqing Zhang
Source: Physical review. B./Physical review. B (article, 2021-09-27)
Field: Materials Science — Materials Chemistry

Dual adaptive sampling and machine learning interatomic potentials for modeling materials with chemical bond hierarchy The development of reliable and flexible machine learning based interatomic potentials (ML-IPs) is becoming increasingly important in studying the physical properties of complex condensed matter systems. Besides the structure descriptor model for total energy decomposition, the trial-and-error approach used in the design of the training dataset makes the ML-IP hardly improvable and reliable for modeling materials with chemical bond hierarchy. In this work, a dual adaptive sampling (DAS) method with an on the fly ambiguity threshold was developed to automatically generate an effective training dataset covering a wide temperature range or a wide spectrum of thermodynamic conditions. The DAS method consists of an inner loop for exploring the local configuration space and an outer loop for covering a wide temperature range. We validated the developed DAS method by simulating thermal transport of complex materials. The simulation results show that even with a substantially small dataset, our approach not only accurately reproduces the energies and forces but also predicts reliably effective high-order force constants to at least fourth order. The lattice thermal conductivity and its temperature dependence were evaluated using the Green-Kubo simulations with ML-IP for $\mathrm{Co}{\mathrm{Sb}}_{3}$ with up to third-order phonon scattering, and those for ${\mathrm{Mg}}_{3}{\mathrm{Sb}}_{2}$ with up to fourth-order phonon scattering, and all show good agreements with experiments. Our work provides an avenue to effectively construct a training dataset for ML-IP of complex materials with chemical bond hierarchy. Computer science ; Lattice (music) ; Phonon ; Statistical physics ; Scattering ; Materials science ; Machine learning ; Physics ; Algorithm ; Condensed matter physics ; Quantum mechanics Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; Thermal properties of materials
Authors: Yulong Chen | Rong Li | Hongri Suo | Chongxuan Liu
Source: ACS ES&T Engineering (article, 2021-06-07)
Field: Materials Science — Materials Chemistry

Evaluation of a Data-Driven, Machine Learning Approach for Identifying Potential Candidates for Environmental Catalysts: From Database Development to Prediction Data-driven, machine learning approaches are increasingly used for the discovery and development of catalytic materials in the area of material science and engineering. In this paper, the approach was evaluated with respect to its applicability in identifying potential environmental catalysts (ECs) using the selective catalytic reduction (SCR) of the air pollutant NOx as an example. The detailed procedures including database assemblage, the training and testing of a machine learning model, the validation and prediction of the model, and model uncertainties are all provided. The results indicated that there is a significant amount of data accumulated in environmental catalysts that can be exploited for accelerating the exploration and optimization of ECs for specific applications. The results also indicated that the approach is powerful for identifying new ECs and optimizing conditions for ECs synthesis and applications. However, the reported data in the literature are often incomplete, which limits the application potentials of the data. With limited data, the simulated results from the model contained uncertainties, especially in the prediction of unknown ECs. Repeated predictions and ensemble averaging were then proposed as an approach to find conditions for synthesizing and applying promising ECs. Computer science ; Machine learning ; Artificial intelligence ; Training set ; Experimental data ; Database ; Data mining ; Mathematics Catalytic Processes in Materials Science ; Machine Learning in Materials Science ; Catalysis and Oxidation Reactions
Authors: Asif Chowdhury | Wenqiang Yang | Andreas Heyden | Gabriel Terejanu
Source: The Journal of Physical Chemistry C (article, 2021-08-10)
Field: Materials Science — Materials Chemistry

Comparative Study on the Machine Learning-Based Prediction of Adsorption Energies for Ring and Chain Species on Metal Catalyst Surfaces Computation of adsorption and transition-state energies for a large number of surface intermediates for numerous active site models poses significant computational overhead in computational screening of catalysts. Machine learning (ML) techniques can be used to predict part of these energies. To predict the energies, ML models need to be fed appropriate metal and species descriptors. For complex surface chemistries, the structures of the intermediate species can vary greatly. In this paper, working with the hydrodeoxygenation of succinic acid on six different metal surfaces, we have studied the effect of linear and non-linear ML models used along with pen-and-paper-based species descriptors and two categories of metal descriptors on two different categories of intermediate species: chain and ring. More specifically, our computations include the prediction of chain species when trained on only chain species and also when trained on both chain and ring species. Similar computations were performed for predictions of ring species. In each case, the results of linear ML models were compared with kernel-based non-linear models. Our results indicate that ring species data do not improve the prediction of chain species. Similarly, chain species data do not improve the prediction of ring species. The use of non-linear ML models, however, did help to minimize the prediction errors compared to the linear models. The study also shows that electronic or adsorption energy-based metal descriptors along with bond count-based species fingerprints can achieve a mean absolute error (MAE) of less than 0.2 eV for complex chain molecules when used with an appropriate machine learning model. Chain (unit) ; Ring (chemistry) ; Computation ; Linear model ; Kernel (algebra) ; Computational chemistry ; Chemistry ; Biological system ; Computer science ; Mathematics ; Machine learning ; Physics ; Algorithm ; Organic chemistry ; Quantum mechanics ; Discrete mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Electrocatalysts for Energy Conversion
Authors: Zhe Yang | Ye Sheng | Cong Zhu | Jianyue Ni | Zhenyu Zhu | Jinyang Xi | Wu Zhang | Jiong Yang
Source: Journal of Materiomics (article, 2021-11-23)
Field: Materials Science — Materials Chemistry

Accurate and explainable machine learning for the power factors of diamond-like thermoelectric materials The application of machine learning (ML)-based methods to the study of thermoelectric (TE) materials is promising. Although conventional ML algorithms can achieve high prediction performance, their lack of interpretability severely obstructs researchers from extracting material-oriented insights from ML models. In this work, high ML-based prediction performance was achieved with respect to TE power factors (PFs), and the results were well understood by the SHapley Additive exPlanations (SHAP), a method to identify the correlations between targets and descriptors. We designed a robust PF prediction model for diamond-like compounds via a stacking technique, and the model achieved a coefficient of determination value above 0.95 on the test set. From the SHAP analysis, the PFs were negatively correlated with electronegativity and positively correlated with the descriptor “volume per atom” based on the previously reported dataset. TE domain knowledge was adopted to understand these correlations. This work shows that ML models can achieve high accuracy while exhibiting good interpretability, making them useful for materials scientists. Interpretability ; Stacking ; Test set ; Materials science ; Electronegativity ; Machine learning ; Diamond ; Power (physics) ; Computer science ; Artificial intelligence ; Thermoelectric effect ; Seebeck coefficient ; Set (abstract data type) ; Domain (mathematical analysis) ; Thermodynamics ; Mathematics ; Physics Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; Thermal properties of materials
Authors: Zhongyu Wan | Quan‐De Wang
Source: The Journal of Physical Chemistry Letters (article, 2021-11-18)
Field: Materials Science — Materials Chemistry

Machine Learning Prediction of the Exfoliation Energies of Two-Dimension Materials via Data-Driven Approach Exfoliation energy is one of the fundamental parameters in the science and engineering of two-dimensional (2D) materials. Traditionally, it was obtained via indirect experimental measurement or first-principles calculations, which are very time- and resource-consuming. Herein, we provide an efficient machine learning (ML) method to accurately predict the exfoliation energies for 2D materials. Toward this end, a series of simple descriptors with explicit physical meanings are defined. Regression trees (RT), support vector machines (SVM), multiple linear regression (MLR), and ensemble trees (ET) are compared to develop the most suitable model for the prediction of exfoliation energies. It is shown that the ET model can efficiently predict the exfoliation energies through extensive validations and stability analysis. The influence of the defined features on the exfoliation energies is analyzed by sensitivity analysis to provide novel physical insight into the affecting factors of the exfoliation energies. Exfoliation joint ; Support vector machine ; Sensitivity (control systems) ; Dimension (graph theory) ; Linear regression ; Stability (learning theory) ; Machine learning ; Computer science ; Series (stratigraphy) ; Materials science ; Artificial intelligence ; Algorithm ; Mathematics ; Nanotechnology ; Engineering ; Graphene ; Geology Graphene research and applications ; Machine Learning in Materials Science ; Thermal properties of materials
Authors: Haomin Liu | Ali K. Shargh | Niaz Abdolrahim
Source: Materialia (article, 2021-11-20)
Field: Materials Science — Materials Chemistry

Mining structure-property linkage in nanoporous materials using an interpretative deep learning approach Nanoporous ; Stiffness ; Materials science ; Curvature ; Convolutional neural network ; Scaling ; Linkage (software) ; Property (philosophy) ; Saddle ; Computer science ; Artificial intelligence ; Process (computing) ; Microstructure ; Biological system ; Machine learning ; Nanotechnology ; Composite material ; Mathematical optimization ; Geometry ; Mathematics Nanoporous metals and alloys ; Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Hailong Yuan | Romain Génois | Estelle Glais | Fei Chen | Qiang Shen | Lianmeng Zhang | E. Faulques | Luyuan Qi | Florian Massuyeau | Romain Gautier
Source: Matter (article, 2021-10-27)
Field: Materials Science — Materials Chemistry

Machine learning identification of experimental conditions for the synthesis of single-phase white phosphors Phosphor ; Dopant ; Materials science ; Quenching (fluorescence) ; Biological system ; White light ; Process engineering ; Computer science ; Analytical Chemistry (journal) ; Nuclear engineering ; Doping ; Chemistry ; Optoelectronics ; Fluorescence ; Engineering ; Chromatography ; Optics ; Physics Luminescence Properties of Advanced Materials ; Advanced Photocatalysis Techniques ; Perovskite Materials and Applications
Authors: Junhao Yuan | Qing Wang | Zhen Li | Chuang Dong | Pengcheng Zhang | Xiangdong Ding
Source: Computational Materials Science (article, 2021-04-09)
Field: Materials Science — Materials Chemistry

Domain-knowledge-oriented data pre-processing and machine learning of corrosion-resistant γ-U alloys with a small database Quinary ; Weighting ; Cluster (spacecraft) ; Corrosion ; Database ; Alloy ; Materials science ; Computer science ; Equivalence (formal languages) ; Algorithm ; Mathematics ; Physics ; Metallurgy Nuclear Materials and Properties ; Hydrogen embrittlement and corrosion behaviors in metals ; Fusion materials and technologies