jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Leslie Ching Ow Tiong | Kim Jeongrae | Sang Soo Han | Dong-Hun Kim
Source: npj Computational Materials (article, 2020-12-17)
Field: Materials Science — Materials Chemistry

Identification of crystal symmetry from noisy diffraction patterns by a shape analysis and deep learning Abstract The robust and automated determination of crystal symmetry is of utmost importance in material characterization and analysis. Recent studies have shown that deep learning (DL) methods can effectively reveal the correlations between X-ray or electron-beam diffraction patterns and crystal symmetry. Despite their promise, most of these studies have been limited to identifying relatively few classes into which a target material may be grouped. On the other hand, the DL-based identification of crystal symmetry suffers from a drastic drop in accuracy for problems involving classification into tens or hundreds of symmetry classes (e.g., up to 230 space groups), severely limiting its practical usage. Here, we demonstrate that a combined approach of shaping diffraction patterns and implementing them in a multistream DenseNet (MSDN) substantially improves the accuracy of classification. Even with an imbalanced dataset of 108,658 individual crystals sampled from 72 space groups, our model achieves 80.12 ± 0.09% space group classification accuracy, outperforming conventional benchmark models by 17–27 percentage points (%p). The enhancement can be largely attributed to the pattern shaping strategy, through which the subtle changes in patterns between symmetrically close crystal systems (e.g., monoclinic vs. orthorhombic or trigonal vs. hexagonal) are well differentiated. We additionally find that the MSDN architecture is advantageous for capturing patterns in a richer but less redundant manner relative to conventional convolutional neural networks. The proposed protocols in regard to both input descriptor processing and DL architecture enable accurate space group classification and thus improve the practical usage of the DL approach in crystal symmetry identification. Artificial intelligence ; Symmetry (geometry) ; Orthorhombic crystal system ; Diffraction ; Convolutional neural network ; Crystal (programming language) ; Benchmark (surveying) ; Computer science ; Monoclinic crystal system ; Pattern recognition (psychology) ; Algorithm ; Physics ; Mathematics ; Crystallography ; Crystal structure ; Optics ; Geometry ; Chemistry ; Geology Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Cultural Heritage Materials Analysis
Authors: Pei‐Lin Kang | Zhi‐Pan Liu
Source: iScience (review, 2020-12-30)
Field: Materials Science — Materials Chemistry

Reaction prediction via atomistic simulation: from quantum mechanics to machine learning It is an ultimate goal in chemistry to predict reaction without recourse to experiment. Reaction prediction is not just the reaction rate determination of known reactions but, more broadly, the reaction exploration to identify new reaction routes. This review briefly overviews the theory on chemical reaction and the current methods for computing/estimating reaction rate and exploring reaction space. We particularly focus on the atomistic simulation methods for reaction exploration, which are benefited significantly by recently emerged machine learning potentials. We elaborate the stochastic surface walking global pathway sampling based on the global neural network (SSW-NN) potential, developed in our group since 2013, which can explore complex reactions systems unbiasedly and automatedly. Two examples, molecular reaction and heterogeneous catalytic reactions, are presented to illustrate the current status for reaction prediction using SSW-NN. Computer science ; Reaction rate ; Reaction dynamics ; Artificial neural network ; Chemical reaction ; Reaction mechanism ; Biochemical engineering ; Statistical physics ; Chemistry ; Computational chemistry ; Catalysis ; Artificial intelligence ; Physics ; Molecule ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Lakshmi Sujeeun | Nowsheen Goonoo | Honita Ramphul | Itisha Chummun Phul | Fanny Gimié | Shakuntala Baichoo | Archana Bhaw‐Luximon
Source: Royal Society Open Science (article, 2020-12-01)
Field: Materials Science — Biomaterials

Correlating <i>in vitro</i> performance with physico-chemical characteristics of nanofibrous scaffolds for skin tissue engineering using supervised machine learning algorithms The engineering of polymeric scaffolds for tissue regeneration has known a phenomenal growth during the past decades as materials scientists seek to understand cell biology and cell-material behaviour. Statistical methods are being applied to physico-chemical properties of polymeric scaffolds for tissue engineering (TE) to guide through the complexity of experimental conditions. We have attempted using experimental <i>in vitro</i> data and physico-chemical data of electrospun polymeric scaffolds, tested for skin TE, to model scaffold performance using machine learning (ML) approach. Fibre diameter, pore diameter, water contact angle and Young's modulus were used to find a correlation with 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay of L929 fibroblasts cells on the scaffolds after 7 days. Six supervised learning algorithms were trained on the data using Seaborn/Scikit-learn Python libraries. After hyperparameter tuning, random forest regression yielded the highest accuracy of 62.74%. The predictive model was also correlated with <i>in vivo</i> data. This is a first preliminary study on ML methods for the prediction of cell-material interactions on nanofibrous scaffolds. Machine learning ; Tissue engineering ; Algorithm ; Scaffold ; Computer science ; Python (programming language) ; Artificial intelligence ; Hyperparameter ; Biomedical engineering ; Materials science ; Nanotechnology ; Engineering ; Database Electrospun Nanofibers in Biomedical Applications ; Additive Manufacturing and 3D Printing Technologies ; 3D Printing in Biomedical Research
Authors: Paul Rossener Regonia | Christian Mark Pelicano | Ryosuke Tani | Atsushi Ishizumi | Hisao Yanagi | Kazushi Ikeda
Source: Optik (article, 2020-02-26)
Field: Materials Science — Materials Chemistry

Predicting the band gap of ZnO quantum dots via supervised machine learning models Mean squared error ; Computer science ; Artificial neural network ; Machine learning ; Artificial intelligence ; Mean absolute error ; Regression ; Kernel (algebra) ; Band gap ; Supervised learning ; Algorithm ; Materials science ; Mathematics ; Optoelectronics ; Statistics ZnO doping and properties ; Machine Learning in Materials Science ; Gas Sensing Nanomaterials and Sensors
Authors: Valentin Goussard | François Duprat | Jean-Luc Ploix | Gérard Dreyfus | Véronique Nardello‐Rataj | Jean‐Marie Aubry
Source: Journal of Chemical Information and Modeling (article, 2020-04-06)
Field: Chemistry — Spectroscopy

A New Machine-Learning Tool for Fast Estimation of Liquid Viscosity. Application to Cosmetic Oils The viscosities of pure liquids are estimated at 25 °C, from their molecular structures, using three modeling approaches: group contributions, COSMO-RS σ-moment-based neural networks, and graph machines. The last two are machine-learning methods, whereby models are designed and trained from a database of viscosities of 300 molecules at 25 °C. Group contributions and graph machines make use of the 2D-structures only (the SMILES codes of the molecules), while neural networks estimations are based on a set of five descriptors: COSMO-RS σ-moments. For the first time, leave-one-out is used for graph machine selection, and it is shown that it can be replaced with the much faster virtual leave-one-out algorithm. The database covers a wide diversity of chemical structures, namely, alkanes, ethers, esters, ketones, carbonates, acids, alcohols, silanes, and siloxanes, as well as different chemical backbone, i.e., straight, branched, or cyclic chains. A comparison of the viscosities of liquids of an independent set of 22 cosmetic oils shows that the graph machine approach provides the most accurate results given the available data. The results obtained by the neural network based on sigma-moments and by the graph machines can be duplicated easily by using a demonstration tool based on the Docker technology, available for download as explained in the Supporting Information. This demonstration also allows the reader to predict, at 25 °C, the viscosity of any liquid of moderate molecular size (<i>M</i> < 600 Da) that contains C, H, O, or Si atoms, starting either from its SMILES code or from its σ-moments computed with the COSMOtherm software. Artificial neural network ; Graph ; Computer science ; Boltzmann machine ; Viscosity ; Algorithm ; Machine learning ; Set (abstract data type) ; Artificial intelligence ; Materials science ; Theoretical computer science Analytical Chemistry and Chromatography ; Phase Equilibria and Thermodynamics ; Computational Drug Discovery Methods
Authors: Mauro Bracconi | Matteo Maestri
Source: Chemical Engineering Journal (article, 2020-05-14)
Field: Materials Science — Materials Chemistry

Training set design for machine learning techniques applied to the approximation of computationally intensive first-principles kinetic models We propose a design procedure for the generation of the training set for Machine Learning algorithms with a specific focus on the approximation of computationally-intensive first-principles kinetic models in catalysis. The procedure is based on the function topology and behavior, by means of the calculation of the discrete gradient, and on the relative importance of the independent variables. We apply the proposed methodology to the tabulation and regression of mean-field and kinetic Monte Carlo models aiming at their coupling with reactor simulations. Our tests – in the context both of mean-field kinetics and kinetic Monte Carlo simulations – show that the procedure is able to design a dataset that requires between 60 and 80% fewer data points to achieve the same approximation accuracy than the one obtained with an evenly distributed grid. This strong reduction in the number of points results in a significant computational gain and a concomitant boost of the approximation efficiency. The Machine Learning algorithms trained with the results of the procedure are then included in both macroscopic reactor models and computational fluid dynamics (CFD) simulations. First, a Plug Flow Reactor is employed to carry out a direct comparison with the solution of the full first-principles kinetic model. The results show an excellent agreement within 0.2% between the models. Then, the CFD simulation of complex tridimensional geometry is carried out by using a tabulated kMC model for CO oxidation on Ruthenium oxide, thus providing a showcase of the capability of the approach in making possible the multiscale simulation of complex chemical reactors. Monte Carlo method ; Computer science ; Context (archaeology) ; Computational fluid dynamics ; Kinetic Monte Carlo ; Mathematical optimization ; Simulation ; Algorithm ; Machine learning ; Mathematics ; Mechanics ; Physics Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: Wataru Hashimoto | Yuta Tsuji | Kazunari Yoshizawa
Source: The Journal of Physical Chemistry C (article, 2020-04-14)
Field: Materials Science — Materials Chemistry

Optimization of Work Function via Bayesian Machine Learning Combined with First-Principles Calculation Work function is one of the most fundamental and important physical quantities in surface science. Materials with either lower work function or higher work function would find various applications, such as electronic devices and high-performance catalysts. However, it would be challenging to find a material with the optimal work function exploiting the all-search or random approach, whether it is based on an experimental or theoretical method. In this paper, we use the Bayesian optimization (BO) approach, which is one of the most powerful machine-learning tools for optimization, in order to effectively explore a candidate material with a higher or lower work function value out of hundreds of thousands of materials registered in a material database. We introduce a quick measure of the work function based on the depth of the Fermi level calculated from the first-principles computation for the crystalline bulk structure of a material. We call this the approximate work function, treating it as the objective function of our BO scheme. Since we do not need any time-consuming surface calculation with the slab model for the evaluation of the approximate work function, a quick search of a material with the highest or the lowest work function is achieved. As input variables for our BO implementation, we employ some bulk-specific properties of materials, which can be fetched from the database. The demonstration of our BO-based exploration of the database shows that materials with both low and high limits of the approximate work function can be discovered more efficiently in BO than a random exploration. The top 10 lowest work function materials thus found are in line with our chemical intuition in that all of them include either alkali or alkaline earth metal. On the other hand, we found the top 10 highest work function materials with amazement because they also include either alkali or alkaline earth metal and a lanthanide element. Work function ; Function (biology) ; Work (physics) ; Bayesian optimization ; Computer science ; Measure (data warehouse) ; Mathematical optimization ; Algorithm ; Machine learning ; Mechanical engineering ; Data mining ; Mathematics ; Materials science ; Engineering ; Nanotechnology Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Behnam Parsaeifard | Deb Sankar De | Anders S Christensen | Felix A Faber | Emir Kocer | Sandip De | Jörg Behler | O Anatole von Lilienfeld | Stefan Goedecker
Source: Machine Learning Science and Technology (article, 2020-08-24)
Field: Materials Science — Materials Chemistry

An assessment of the structural resolution of various fingerprints commonly used in machine learning Abstract Atomic environment fingerprints are widely used in computational materials science, from machine learning potentials to the quantification of similarities between atomic configurations. Many approaches to the construction of such fingerprints, also called structural descriptors, have been proposed. In this work, we compare the performance of fingerprints based on the overlap matrix, the smooth overlap of atomic positions, Behler–Parrinello atom-centered symmetry functions, modified Behler–Parrinello symmetry functions used in the ANI-1ccx potential and the Faber–Christensen–Huang–Lilienfeld fingerprint under various aspects. We study their ability to resolve differences in local environments and in particular examine whether there are certain atomic movements that leave the fingerprints exactly or nearly invariant. For this purpose, we introduce a sensitivity matrix whose eigenvalues quantify the effect of atomic displacement modes on the fingerprint. Further, we check whether these displacements correlate with the variation of localized physical quantities such as forces. Finally, we extend our examination to the correlation between molecular fingerprints obtained from the atomic fingerprints and global quantities of entire molecules. Fingerprint (computing) ; Symmetry (geometry) ; Pattern recognition (psychology) ; Sensitivity (control systems) ; Displacement (psychology) ; Eigenvalues and eigenvectors ; Matrix (chemical analysis) ; Resolution (logic) Machine Learning in Materials Science ; Forensic Fingerprint Detection Methods ; History and advancements in chemistry
Authors: Mohamed Ali Boussaidi | Owen Ren | Dmitry Voytsekhovsky | Sergei Manzhos
Source: The Journal of Physical Chemistry A (article, 2020-08-20)
Field: Materials Science — Materials Chemistry

Random Sampling High Dimensional Model Representation Gaussian Process Regression (RS-HDMR-GPR) for Multivariate Function Representation: Application to Molecular Potential Energy Surfaces We present an approach combining a representation of a multivariate function using subdimensional functions with machine learning based representation of component functions: Random sampling high dimensional model representation Gaussian process regression (RS-HDMR-GPR). The use of Gaussian process regressions to represent component functions allows nonparametric (unbiased) representation and the possibility to work only with functions of desired dimensionality, obviating the need to build an expansion over orders of coupling. All component functions are determined from a single set of samples. The method is tested by fitting six- and 15-dimensional potential energy surfaces (PES) of polyatomic molecules as well as by computing vibrational spectra for a four-atomic molecule. Kriging ; Gaussian process ; Representation (politics) ; Curse of dimensionality ; Gaussian ; Nonparametric regression ; Gaussian function ; Multivariate statistics ; Component (thermodynamics) ; Sampling (signal processing) ; Mathematics ; Computer science ; Nonparametric statistics ; Biological system ; Statistical physics ; Artificial intelligence ; Computational chemistry ; Statistics ; Chemistry ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Mass Spectrometry Techniques and Applications
Authors: Chenru Duan | Fang Liu | Aditya Nandy | Heather J. Kulik
Source: Journal of Chemical Theory and Computation (article, 2020-06-14)
Field: Materials Science — Materials Chemistry

Data-Driven Approaches Can Overcome the Cost–Accuracy Trade-Off in Multireference Diagnostics High-throughput computational screening typically employs methods (i.e., density functional theory or DFT) that can fail to describe challenging molecules, such as those with strongly correlated electronic structure. In such cases, multireference (MR) correlated wavefunction theory (WFT) would be the appropriate choice but remains more challenging to carry out and automate than single-reference (SR) WFT or DFT. Numerous diagnostics have been proposed for identifying when MR character is likely to have an effect on the predictive power of SR calculations, but conflicting conclusions about diagnostic performance have been reached on small data sets. We compute 15 MR diagnostics, ranging from affordable DFT-based to more costly MR-WFT-based diagnostics, on a set of 3165 equilibrium and distorted small organic molecules containing up to six heavy atoms. Conflicting MR character assignments and low pairwise linear correlations among diagnostics are also observed over this set. We evaluate the ability of existing diagnostics to predict the percent recovery of the correlation energy, %<i>E</i><sub>corr</sub>. None of the DFT-based diagnostics are nearly as predictive of %<i>E</i><sub>corr</sub> as the best WFT-based diagnostics. To overcome the limitation of this cost-accuracy trade-off, we develop machine learning (ML, i.e., kernel ridge regression) models to predict WFT-based diagnostics from a combination of DFT-based diagnostics and a new, size-independent 3D geometric representation. The ML-predicted diagnostics correlate as well with MR effects as their computed (i.e., with WFT) values, significantly improving over the DFT-based diagnostics on which the models were trained. These ML models thus provide a promising approach to improve upon DFT-based diagnostic accuracy while remaining suitably low cost for high-throughput screening. Computer science ; Pairwise comparison ; Density functional theory ; Artificial intelligence ; Machine learning ; Algorithm ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Francis Doherty | Hui Wang | Ming Yang | Bryan R. Goldsmith
Source: Catalysis Science & Technology (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Nanocluster and single-atom catalysts for thermocatalytic conversion of CO and CO<sub>2</sub> We highlight different aspects of single-atom and nanocluster catalysts for CO<sub>2</sub>reduction and CO oxidation, including synthesis, dynamic restructuring, and trends in activity and selectivity. Catalysis ; Selectivity ; Atom (system on chip) ; Restructuring ; Chemistry ; Chemical engineering ; Materials science ; Nanotechnology ; Business ; Computer science ; Organic chemistry ; Engineering ; Parallel computing Catalytic Processes in Materials Science ; CO2 Reduction Techniques and Catalysts ; Catalysis and Oxidation Reactions
Authors: Catarina S. Miranda | Ana R. M. Ribeiro | Natália C. Homem | Helena P. Felgueiras
Source: Antibiotics (review, 2020-04-12)
Field: Materials Science — Biomaterials

Spun Biotextiles in Tissue Engineering and Biomolecules Delivery Systems Nowadays, tissue engineering is described as an interdisciplinary field that combines engineering principles and life sciences to generate implantable devices to repair, restore and/or improve functions of injured tissues. Such devices are designed to induce the interaction and integration of tissue and cells within the implantable matrices and are manufactured to meet the appropriate physical, mechanical and physiological local demands. Biodegradable constructs based on polymeric fibers are desirable for tissue engineering due to their large surface area, interconnectivity, open pore structure, and controlled mechanical strength. Additionally, biodegradable constructs are also very sought-out for biomolecule delivery systems with a target-directed action. In the present review, we explore the properties of some of the most common biodegradable polymers used in tissue engineering applications and biomolecule delivery systems and highlight their most important uses. Interconnectivity ; Tissue engineering ; Biomolecule ; Nanotechnology ; Biodegradable polymer ; Computer science ; Materials science ; Biomedical engineering ; Engineering ; Polymer ; Artificial intelligence Electrospun Nanofibers in Biomedical Applications ; biodegradable polymer synthesis and properties ; Bone Tissue Engineering Materials