jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Robert Hussein | Jonathan Schmidt | Tomás Barros | Miguel A. L. Marques | Silvana Botti
Source: MRS Bulletin (article, 2022-04-27)
Field: Materials Science — Materials Chemistry

Machine-learning correction to density-functional crystal structure optimization Abstract Density functional theory is routinely applied to predict crystal structures. The most common exchange-correlation functionals used to this end are the Perdew–Burke–Ernzerhof (PBE) approximation and its variant PBEsol. We investigate the performance of these functionals for the prediction of lattice parameters and show how to enhance their accuracy using machine learning. Our data set is constituted by experimental crystal structures of the Inorganic Crystal Structure Database matched with PBE-optimized structures stored in the materials project database. We complement these data with PBEsol calculations. We demonstrate that the accuracy and precision of PBE/PBEsol volume predictions can be noticeably improved a posteriori by employing simple, explainable machine learning models. These models can improve PBE unit cell volumes to match the accuracy of PBEsol calculations, and reduce the error of the latter with respect to experiment by 35 percent. Further, the error of PBE lattice constants is reduced by a factor of 3–5. A further benefit of our approach is the implicit correction of finite temperature effects without performing phonon calculations. Impact statement Knowledge about the crystal structure of solids is essential for describing their elastic and electronic properties. In particular, their accurate prediction is essential to predict the electronic properties of not-yet-synthesized materials. Lattice parameters are most commonly calculated by density functional theory using the Perdew–Burke–Ernzerhof (PBE) approximation and its variant PBEsol as exchange-correlation functional. They are successful in describing materials properties but do, however, not always achieve the desired accuracy in comparison with experiments. We propose a computationally efficient scheme based on interpretable machine learning to optimize crystal structures. We demonstrate that the accuracy of PBE- and PBEsol-structures can be, therewith, enhanced noticeably. In particular, the PBE unit cells, available in materials databases, can be improved to the level of the more accurate PBEsol calculations and the error of the latter with respect to the experiment can be reduced by 35 percent. An additional advantage of our scheme is the implicit inclusion of finite temperature corrections, which makes expensive phonon calculations unnecessary. Graphical abstract Materials science ; Computer science Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Ke Xu | Ting Liang | Yuequn Fu | Zhen Wang | Zheyong Fan | Ning Wei | Jianbin Xu | Zhisen Zhang | Jianyang Wu
Source: Applied Physics Letters (article, 2022-09-26)
Field: Materials Science — Materials Chemistry

Gradient nano-grained graphene as 2D thermal rectifier: A molecular dynamics based machine learning study Machine learning has become an excellent tool for scientists and engineers to predict, design, and fabricate next-generation material. Here, we report the thermal conductivity and thermal rectification of gradient-nano-grained graphene (GNGG) by molecular dynamic simulation with machine learning. It is revealed that the thermal conductivity of GNGG is mainly determined by the average grain size, while its thermal rectification factor varies linearly with the gradient of nanograins. Deep neural network-based machine learning models are developed to estimate the thermal transport properties of GNGG using microstructural signatures, such as the location, number, and orientation of 5|7 pairs. The results stress the pivotal roles of 5|7 defects in the planar thermal transports of graphene and indicate that high-performance 2D thermal rectifiers for heat flow control and energy harvesting can be achieved by bio-inspired gradient structure engineering. The findings are expected to supply a theoretical strategy for the design of bio-inspired materials and create a method to predict the potential properties of the material candidates by using machine learning, which can save the abundant expense of developing the material by using the classical method. Thermal conductivity ; Graphene ; Materials science ; Temperature gradient ; Nano- ; Rectifier (neural networks) ; Molecular dynamics ; Thermal ; Artificial intelligence ; Artificial neural network ; Rectification ; Computer science ; Grain boundary ; Nanotechnology ; Mechanical engineering ; Composite material ; Physics ; Engineering ; Electrical engineering ; Microstructure Thermal properties of materials ; Machine Learning in Materials Science
Authors: Hongxing Liang | Pengfei Liu | Min Xu | Haotong Li | Edouard Asselin
Source: International Journal of Quantum Chemistry (article, 2022-11-28)
Field: Materials Science — Materials Chemistry

A study of two‐dimensional single atom‐supported <scp>MXenes</scp> as hydrogen evolution reaction catalysts using density functional theory and machine learning Abstract Screening promising hydrogen evolution reaction (HER) electrocatalysts for water splitting is crucial for the industrial scalability of sustainable energy storage. As HER catalysts, two‐dimensional (2D) MXenes are promising substitution materials for platinum. Tuning the surface termination and loading a single atom can help to improve the electrocatalytic performance of 2D MXenes. We utilized density functional theory (DFT) calculations to explore the catalyst activity, thermal stability, and dynamic stability of 2D single atom‐loaded MXenes with surface terminations. We demonstrate that 21 uninvestigated 2D single‐atom MXene catalysts, among 264 promising candidates, show an electrocatalytic activity surpassing that of platinum. Among the 21 most promising HER catalysts, 7 (Ti 3 C 2 I 2 Ir, Ti 3 C 2 Br 2 Cu, Ti 3 C 2 Br 2 Pt, Ti 3 C 2 Cl 2 Cu, Ti 3 C 2 Cl 2 Pt, Ti 3 C 2 Se 2 Au, and Ti 3 C 2 Te 2 Nb) are dynamically and thermally stable. Furthermore, machine learning tools predicted the catalyst activity and thermal stability using elemental properties that are easily available in chemical data repositories. MXenes ; Catalysis ; Density functional theory ; Water splitting ; Platinum ; Materials science ; Thermal stability ; Atom (system on chip) ; Hydrogen ; Chemical engineering ; Physical chemistry ; Nanotechnology ; Computational chemistry ; Chemistry ; Computer science ; Photocatalysis ; Organic chemistry MXene and MAX Phase Materials ; Advanced Photocatalysis Techniques ; Electrocatalysts for Energy Conversion
Authors: Ivan Kurniawan | Yoshio Miura | K. Hono
Source: Physical Review Materials (article, 2022-09-14)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine learning study of highly spin-polarized Heusler alloys at finite temperature A huge magnetoresistance (MR) ratio exceeding 2000% at cryogenic temperature that was reported for half-metallic Heusler alloy based magnetic tunnel junctions showed large degradation at room temperature, which impedes practical application of Heusler alloy based MR devices. This motivates us to explore alternative Heusler alloys that show high spin polarization at finite temperatures. Here, we propose half-metallic Heusler alloys based on finite-temperature first-principles calculation via the disordered local moment method together with machine learning. We found several prospective materials at room temperature such as ${\mathrm{Co}}_{2}{\mathrm{MnGa}}_{0.2}{\mathrm{As}}_{0.8}$ and ${\mathrm{Co}}_{2}{\mathrm{FeAl}}_{0.4}{\mathrm{Sn}}_{0.6}$. We also investigated two combinatorial series, ${\mathrm{Co}}_{2}{\mathrm{MnGa}}_{y}{\mathrm{As}}_{1\text{\ensuremath{-}}y}$ and ${\mathrm{Co}}_{2}{\mathrm{FeAl}}_{y}{\mathrm{Sn}}_{1\text{\ensuremath{-}}y}$, to understand the effect of alloy mixing on temperature dependence and found that Fermi level tuning significantly improved the spin polarization and its temperature dependence, especially in ${\mathrm{Co}}_{2}{\mathrm{FeAl}}_{y}{\mathrm{Sn}}_{1\text{\ensuremath{-}}y}$. FEAL ; Materials science ; Condensed matter physics ; Alloy ; Magnetoresistance ; Spin polarization ; Magnetic moment ; Spin (aerodynamics) ; Intermetallic ; Physics ; Thermodynamics ; Metallurgy ; Magnetic field ; Electron ; Quantum mechanics Heusler alloys: electronic and magnetic properties ; Magnetic properties of thin films ; 2D Materials and Applications
Authors: Kazuaki Z. Takahashi
Source: Physical Chemistry Chemical Physics (article, 2022-12-09)
Field: Materials Science — Materials Chemistry

Molecular cluster analysis using local order parameters selected by machine learning Accurately extracting local molecular structures is essential for understanding the mechanisms of phase and structural transitions. A promising method to characterize the local molecular structure is defining the value of the local order parameter (LOP) for each particle. This work develops the Molecular Assembly structure Learning package for Identifying Order parameters (MALIO), a machine learning package that can propose an optimal (set of) LOP(s) quickly and automatically for a huge number of LOP species and various methods of selecting neighboring particles for the calculation. We applied this package to distinguish between the nematic and smectic phases of uniaxial liquid crystal molecules, and selected candidate LOPs that could be used to precisely observe the nematic-smectic phase transition. The LOP candidates were used to observe the nucleation and subsequent percolation transition, and the effect of the choice of LOP species and neighboring particles on the statistics of local molecular structures (clusters) was examined. The procedure revealed the time evolution of the number of clusters and the dependence of the percolation curve on the number of neighboring particles for each LOP species. The LOP species with the lowest dependence on the number of neighboring particles was the best-performing LOP species in the MALIO screening strategy. These results not only show that machine learning can powerfully screen a huge number of LOP species and suggest only a few promising candidates, but also indicate that MALIO can select the best LOP species. Percolation (cognitive psychology) ; Cluster (spacecraft) ; Nucleation ; Statistical physics ; Phase transition ; Liquid crystal ; Particle (ecology) ; Chemical physics ; Physics ; Materials science ; Computer science ; Biological system ; Condensed matter physics ; Thermodynamics ; Biology Material Dynamics and Properties ; nanoparticles nucleation surface interactions ; Liquid Crystal Research Advancements
Authors: Satvik Lolla | Haotong Liang | A. Gilad Kusne | Ichiro Takeuchi | William Ratcliff
Source: Journal of Applied Crystallography (article, 2022-07-28)
Field: Materials Science — Materials Chemistry

A semi-supervised deep-learning approach for automatic crystal structure classification The structural solution problem can be a daunting and time-consuming task. Especially in the presence of impurity phases, current methods, such as indexing, become more unstable. In this work, the novel approach of semi-supervised learning is applied towards the problem of identifying the Bravais lattice and the space group of inorganic crystals. The reported semi-supervised generative deep-learning model can train on both labeled data, <i>i.e.</i> diffraction patterns with the associated crystal structure, and unlabeled data, <i>i.e.</i> diffraction patterns that lack this information. This approach allows the models to take advantage of the troves of unlabeled data that current supervised learning approaches cannot, which should result in models that can more accurately generalize to real data. In this work, powder diffraction patterns are classified into all 14 Bravais lattices and 144 space groups (the number is limited due to sparse coverage in crystal structure databases), which covers more crystal classes than other studies. The reported models also outperform current deep-learning approaches for both space group and Bravais lattice classification using fewer training data. Bravais lattice ; Artificial intelligence ; Computer science ; Diffraction ; Machine learning ; Crystal system ; Pattern recognition (psychology) ; Supervised learning ; Crystal structure ; Lattice (music) ; Chemistry ; Artificial neural network ; Physics ; Crystallography ; Optics X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Crystallography and molecular interactions
Authors: Zhaoning Yu | Hongyang Gao
Source: arXiv (Cornell University) (preprint, 2022-02-01)
Field: Materials Science — Materials Chemistry

Molecular Representation Learning via Heterogeneous Motif Graph Neural Networks We consider feature representation learning problem of molecular graphs. Graph Neural Networks have been widely used in feature representation learning of molecular graphs. However, most existing methods deal with molecular graphs individually while neglecting their connections, such as motif-level relationships. We propose a novel molecular graph representation learning method by constructing a heterogeneous motif graph to address this issue. In particular, we build a heterogeneous motif graph that contains motif nodes and molecular nodes. Each motif node corresponds to a motif extracted from molecules. Then, we propose a Heterogeneous Motif Graph Neural Network (HM-GNN) to learn feature representations for each node in the heterogeneous motif graph. Our heterogeneous motif graph also enables effective multi-task learning, especially for small molecular datasets. To address the potential efficiency issue, we propose to use an edge sampler, which can significantly reduce computational resources usage. The experimental results show that our model consistently outperforms previous state-of-the-art models. Under multi-task settings, the promising performances of our methods on combined datasets shed light on a new learning paradigm for small molecular datasets. Finally, we show that our model achieves similar performances with significantly less computational resources by using our edge sampler. Motif (music) ; Computer science ; Feature learning ; Graph ; Molecular graph ; Theoretical computer science ; Deep learning ; Artificial neural network ; Artificial intelligence Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Jie Lu | yanghua chen | Meng Xu | YingZhang
Source: Materials Research Express (article, 2022-10-01)
Field: Materials Science — Biomaterials

Prediction of mechanical properties of Mg-rare earth alloys by machine learning Abstract In this work, the quantitative relationship among the composition, processing history and mechanical properties of Magnesium-rare earth alloys was established by machine learning (ML). Based on support vector regression (SVR) algorithm, ML models were established with inputs of 310 sets of data, which can predict ultimate tensile strength (UTS), yield strength (YS) and elongation (EL) with well accuracy. In order to verify the general applicability of our model, new data were collected from the literature, and the ML models was used to predict their mechanical properties respectively. The MAPE of UTS, YS and EL predicted by SVR model are 9%, 12% and 36%, respectively. The reasons for the deviation of the predicted results were also analyzed. The effects of rare earth elements on UTS, YS and EL were analyzed by the SVR models. The established ML model was used to recommend the composition and processing history of new Magnesium-rare earth alloys with high mechanical properties. Rare earth ; Ultimate tensile strength ; Magnesium ; Support vector machine ; Elongation ; Materials science ; Regression analysis ; Machine learning ; Algorithm ; Computer science ; Artificial intelligence ; Metallurgy Magnesium Alloys: Properties and Applications ; Aluminum Alloys Composites Properties ; Aluminum Alloy Microstructure Properties
Authors: Xiaotong Liu | Pierre-Paul De Breuck | Linghui Wang | Gian‐Marco Rignanese
Source: npj Computational Materials (article, 2022-11-11)
Field: Materials Science — Materials Chemistry

A simple denoising approach to exploit multi-fidelity data for machine learning materials properties Abstract Machine-learning models have recently encountered enormous success for predicting the properties of materials. These are often trained based on data that present various levels of accuracy, with typically much less high- than low-fidelity data. In order to extract as much information as possible from all available data, we here introduce an approach which aims to improve the quality of the data through denoising. We investigate the possibilities that it offers in the case of the prediction of the band gap using both limited experimental data and density-functional theory relying on different exchange-correlation functionals. After analyzing the raw data thoroughly, we explore different ways to combine the data into training sequences and analyze the effect of the chosen denoiser. We also study the effect of applying the denoising procedure several times until convergence. Finally, we compare our approach with various existing methods to exploit multi-fidelity data and show that it provides an interesting improvement. Exploit ; Fidelity ; Computer science ; Noise reduction ; Machine learning ; Raw data ; Artificial intelligence ; Convergence (economics) ; Data mining ; Simple (philosophy) Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Markus Meuwly
Source: The Journal of Physical Chemistry B (article, 2022-03-14)
Field: Materials Science — Materials Chemistry

Atomistic Simulations for Reactions and Vibrational Spectroscopy in the Era of Machine Learning─<i>Quo Vadis?</i> Atomistic simulations using accurate energy functions can provide molecular-level insight into functional motions of molecules in the gas and in the condensed phase. This Perspective delineates the present status of the field from the efforts of others and some of our own work and discusses open questions and future prospects. The combination of physics-based long-range representations using multipolar charge distributions and kernel representations for the bonded interactions is shown to provide realistic models for the exploration of the infrared spectroscopy of molecules in solution. For reactions, empirical models connecting dedicated energy functions for the reactant and product states allow statistically meaningful sampling of conformational space whereas machine-learned energy functions are superior in accuracy. The future combination of physics-based models with machine-learning techniques and integration into all-purpose molecular simulation software provides a unique opportunity to bring such dynamics simulations closer to reality. Molecular dynamics ; Statistical physics ; Computer science ; Range (aeronautics) ; Status quo ; Field (mathematics) ; Perspective (graphical) ; Work (physics) ; Force field (fiction) ; Chemical physics ; Computational chemistry ; Physics ; Chemistry ; Artificial intelligence ; Aerospace engineering ; Mathematics ; Quantum mechanics ; Engineering Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Spectroscopy and Quantum Chemical Studies
Authors: Paria Coliaie | Aditya Prajapati | Rabia Ali | Akshay Korde | Manish S. Kelkar | Nandkishor K. Nere | Meenesh R. Singh
Source: ACS Sensors (article, 2022-01-20)
Field: Chemistry — Electrochemistry

Machine Learning-Driven, Sensor-Integrated Microfluidic Device for Monitoring and Control of Supersaturation for Automated Screening of Crystalline Materials Integrating sensors in miniaturized devices allow for fast and sensitive detection and precise control of experimental conditions. One of the potential applications of a sensor-integrated microfluidic system is to measure the solute concentration during crystallization. In this study, a continuous-flow microfluidic mixer is paired with an electrochemical sensor to enable in situ measurement of the supersaturation. This sensor is investigated as the predictive measurement of the supersaturation during the antisolvent crystallization of l-histidine in the water-ethanol mixture. Among the various metals tested in a batch system for their sensitivity toward l-histidine, Pt showed the highest sensitivity. A Pt-printed electrode was inserted in the continuous-flow microfluidic mixer, and the cyclic voltammograms of the system were obtained for different concentrations of l-histidine and different water-to-ethanol ratios. The sensor was calibrated for different ratios of antisolvent and concentrations of l-histidine with respect to the change of the measured anodic slope. Additionally, a machine-learning algorithm using neural networks was developed to predict the supersaturation of l-histidine from the measured anodic slope. The electrochemical sensors have shown sensitivity toward l-histidine, l-glutamic acid, and <i>o</i>-aminobenzoic acid, which consist of functional groups present in almost 80% of small-molecule drugs on the market. The machine learning-guided electrochemical sensors can be applied to other small molecules with similar functional groups for automated screening of crystallization conditions in microfluidic devices. Supersaturation ; Crystallization ; Microfluidics ; Materials science ; Histidine ; Detection limit ; Electrochemistry ; Electrode ; Electrochemical gas sensor ; Nanotechnology ; Chemical engineering ; Analytical Chemistry (journal) ; Chemistry ; Chromatography ; Organic chemistry ; Physical chemistry Electrochemical Analysis and Applications ; Analytical Chemistry and Sensors ; Biosensors and Analytical Detection
Authors: Aravin Prince Periyasamy | Lekha Priya Muthusamy | Jiřı́ Militký
Source: Scientific Reports (article, 2022-05-21)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Neural network model applied to electromagnetic shielding effectiveness of ultra-light Ni/Cu coated polyester fibrous materials The purpose of effective electromagnetic interference (EMI) shielding is to prevent EMI from smartphone, wireless, and utilization of other electronic devices. The electrical conductivity of materials strongly influences on the EMI shielding properties. In this work, mainly focus to predict the EMI shielding effectiveness on the ultralight weight fibrous materials by artificial neural network (ANN). Prior to the ANN modelling, the ultra-lightweight fibrous materials were electroplated with different concentration of Ni/Cu and then coated with different silanes. This work utilizes the algorithm to provide accurate quantitative values of EMI shielding effectiveness (EM SE). To compare its performance, the experimental and the predicted EM SE values were validated by root-mean-square error (RMSE), mean absolute percentage error (MAPE) values and correlation coefficient 'r'. The proposed ANN results accurately predict the experimental data with correlation coefficients of 0.991 and 0.997. Further due to its simplicity, reliability as well as its efficient computational capability the proposed ANN model permits relatively fast, cost effective and objective estimates to be made of serving in this industry. EMI ; Electromagnetic shielding ; Electromagnetic interference ; Mean squared error ; Correlation coefficient ; Materials science ; Artificial neural network ; Mean absolute percentage error ; Reliability (semiconductor) ; Computer science ; Composite material ; Biological system ; Machine learning ; Statistics ; Mathematics ; Physics ; Telecommunications Electromagnetic wave absorption materials ; Noise Effects and Management ; Textile materials and evaluations