jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Pranath Reddy | Aranya B. Bhattacherjee
Source: Machine Learning Science and Technology (article, 2020-12-17)
Field: Materials Science — Materials Chemistry

A hybrid quantum regression model for the prediction of molecular atomization energies Abstract Quantum machine learning is a relatively new research field that aims to combine the dramatic performance advantage offered by quantum computing and the ability of machine learning algorithms to learn complex distributions of high-dimensional data. The primary focus of this domain is the implementation of classical machine learning algorithms in the quantum mechanical domain and study of the speedup due to quantum parallelism, which could enable the development of novel techniques for solving problems such as quantum phase recognition and quantum error correction optimization. In this paper, we propose a hybrid quantum machine learning pipeline for predicting the atomization energies of various molecules using the nuclear charges and atomic positions of the constituent atoms. Firstly, we will be using a deep convolutional auto-encoder model for the feature extraction of data constructed from the eigenvalues and eigenvector centralities of the pairwise distance matrix calculated from atomic positions and the unrolled upper triangle of each Coulomb matrix calculated from nuclear charges, and we will then be using a quantum regression algorithm such as quantum linear regression, quantum radial basis function neural network and, a quantum neural network for estimating the atomization energy. The hybrid quantum neural network models do not seem to provide any speedup over their classical counterparts. Before implementing a quantum algorithm, we will also be using state-of-the-art classical machine learning and deep learning models such as XGBoost, multilayer perceptron, deep convolutional neural network, and a long short-term memory network to study the correlation between the extracted features and corresponding atomization energies of molecules. Quantum machine learning ; Quantum algorithm ; Computer science ; Speedup ; Algorithm ; Convolutional neural network ; Restricted Boltzmann machine ; Quantum phase estimation algorithm ; Deep learning ; Quantum ; Quantum computer ; Artificial intelligence ; Autoencoder ; Quantum error correction ; Quantum mechanics ; Physics ; Parallel computing Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture ; Spectroscopy and Quantum Chemical Studies
Authors: Ignacio Migliaro | Thomas R. Cundari
Source: Journal of Chemical Information and Modeling (article, 2020-09-16)
Field: Chemistry — Organic Chemistry

Density Functional Study of Methane Activation by Frustrated Lewis Pairs with Group 13 Trihalides and Group 15 Pentahalides and a Machine Learning Analysis of Their Barrier Heights Frustrated Lewis pairs (FLP) are an important advancement in metal-free catalysis. FLPs activate a variety of small molecules, notably dihydrogen. Methane activation, however, has not been reported despite it being an abundant chemical feedstock. Density functional theory calculations were utilized to elucidate the reaction mechanism of methane activation by triel trihalide and pnictogen pentahalide-ammonia Lewis pairs. Two reaction mechanisms were modeled for methane activation: proton abstraction and hydride abstraction. In all cases, deprotonation was thermodynamically and kinetically favored versus hydride abstraction. The use of heavier pnictogens and bigger triels were calculated to be more favorable for the activation of methane. To discern factors affecting the activation energies, different descriptors were correlated-ground state thermodynamics, orbital energies, transition state strain energies, etc.-but no consistent patterns were identified. Thus, machine learning methods were used to correlate ground state parameters to barrier heights. A neural network was used to correlate ground state descriptors (global electrophilicity index, bond dissociation energies, reaction energies) to activation free energies (R<sup>2</sup> = 0.90). Trihalide ; Chemistry ; Density functional theory ; Frustrated Lewis pair ; Deprotonation ; Hydride ; Computational chemistry ; Dissociation (chemistry) ; Lewis acids and bases ; Ground state ; Methane ; Bond-dissociation energy ; Catalysis ; Hybrid functional ; Electrophile ; Physical chemistry ; Organic chemistry ; Metal ; Atomic physics ; Physics Organoboron and organosilicon chemistry ; Synthesis and characterization of novel inorganic/organometallic compounds ; Crystallography and molecular interactions
Authors: Rulin Liu | Junjie Hao | Jiagen Li | Shujie Wang | Haochen Liu | Ziming Zhou | Marie‐Hélène Delville | Jiaji Cheng | Kai Wang | Xi Zhu
Source: The Journal of Physical Chemistry Letters (article, 2020-08-13)
Field: Materials Science — Materials Chemistry

Causal Inference Machine Learning Leads Original Experimental Discovery in CdSe/CdS Core/Shell Nanoparticles The synthesis of CdSe/CdS core/shell nanoparticles was revisited with the help of a causal inference machine learning framework. The tadpole morphology with 1-2 tails was experimentally discovered. The causal inference model revealed the causality between the oleic acid (OA), octadecylphosphonic acid (ODPA) ligands, and the detailed tail shape of the tadpole morphology. Further, with the identified causality, a neural network was provided to predict and directly lead to the original experimental discovery of new tadpole-shaped structures. An entropy-driven nucleation theory was developed to understand both the ligand and temperature dependent experimental data and the causal inference from the machine learning framework. This work provided a vivid example of how the artificial intelligence technology, including machine learning, could benefit the materials science research for the discovery. Shell (structure) ; Nanoparticle ; Core (optical fiber) ; Inference ; Causal inference ; Nanotechnology ; Materials science ; Computer science ; Artificial intelligence ; Mathematics ; Composite material Quantum Dots Synthesis And Properties ; Chalcogenide Semiconductor Thin Films ; Machine Learning in Materials Science
Authors: Kevin Tran | Willie Neiswanger | Junwoong Yoon | Qingyang Zhang | Eric P. Xing | Zachary W. Ulissi
Source: Machine Learning Science and Technology (preprint, 2020-03-09)
Field: Materials Science — Materials Chemistry

Methods for comparing uncertainty quantifications for material property predictions Abstract Data science and informatics tools have been proliferating recently within the computational materials science and catalysis fields. This proliferation has spurned the creation of various frameworks for automated materials screening, discovery, and design. Underpinning these frameworks are surrogate models with uncertainty estimates on their predictions. These uncertainty estimates are instrumental for determining which materials to screen next, but the computational catalysis field does not yet have a standard procedure for judging the quality of such uncertainty estimates. Here we present a suite of figures and performance metrics derived from the machine learning community that can be used to judge the quality of such uncertainty estimates. This suite probes the accuracy, calibration, and sharpness of a model quantitatively. We then show a case study where we judge various methods for predicting density-functional-theory-calculated adsorption energies. Of the methods studied here, we find that the best performer is a model where a convolutional neural network is used to supply features to a Gaussian process regressor, which then makes predictions of adsorption energies along with corresponding uncertainty estimates. Uncertainty quantification ; Suite ; Computer science ; Calibration ; Field (mathematics) ; Property (philosophy) ; Convolutional neural network ; Gaussian process ; Gaussian ; Process (computing) ; Toolbox ; Underpinning ; Quality (philosophy) ; Machine learning ; Artificial intelligence ; Data mining ; Mathematics ; Chemistry ; Statistics ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Keishu Utimula | Rutchapon Hunkao | Masao Yano | Hiroyuki Kimoto | Kenta Hongo | Shogo Kawaguchi | Sujin Suwanna | Ryo Maezono
Source: Advanced Theory and Simulations (article, 2020-06-05)
Field: Materials Science — Materials Chemistry

Machine‐Learning Clustering Technique Applied to Powder X‐Ray Diffraction Patterns to Distinguish Compositions of ThMn<sub>12</sub>‐Type Alloys Abstract A clustering technique is applied using dynamic‐time‐wrapping (DTW) analysis to X‐ray diffraction (XRD) spectrum patterns in order to identify the microscopic structures of substituents introduced into the main phase of magnetic alloys. The clustering technique is found to perform well, identifying the concentrations of the substituents with success rates of ≈90%. This level of performance is attributed to the capability of DTW processing to filter out irrelevant information such as the peak intensities (due to the uncontrollability of diffraction conditions in polycrystalline samples) and the uniform shift of peak positions (due to the thermal expansion of lattices). The established framework is not limited to the system treated in this work, but is widely applicable to systems the properties of which are to be tuned by atomic substitutions within a phase. The framework has a broader potential to predict properties such as magnetic moments, optical spectra etc.) from observed XRD patterns, by predicting such properties evaluated from predicted microscopic local structure. Crystallite ; Diffraction ; Cluster analysis ; Filter (signal processing) ; Phase (matter) ; Powder diffraction ; X-ray crystallography ; Spectral line ; Thermal X-ray Diffraction in Crystallography ; Metallic Glasses and Amorphous Alloys ; Machine Learning in Materials Science
Authors: Michael D. Taylor | Tzuhsiung Yang | Sean Xu Qi Lin | Aditya Nandy | Jon Paul Janet | Chenru Duan | Heather J. Kulik
Source: ChemRxiv (preprint, 2020-02-21)
Field: Materials Science — Materials Chemistry

Seeing is Believing: Experimental Spin States from Machine Learning Model Structure Predictions &lt;p&gt;Determination of ground-state spins of open-shell transition metal complexes is critical to understanding catalytic and materials properties but also challenging with approximate electronic structure methods. As an alternative approach, we demonstrate how structure alone can be used to guide assignment of ground-state spin from experimentally determined crystal structures of transition metal complexes. We first identify the limits of distance-based heuristics from distributions of metal–ligand bond lengths of over 2,000 unique mononuclear Fe(II)/Fe(III) transition metal complexes. To overcome these limits, we employ artificial neural networks (ANNs) to predict spin-state-dependent metal–ligand bond lengths and classify experimental ground state spins based on agreement of experimental structures with the ANN predictions. Although the ANN is trained on hybrid density functional theory data, we exploit the method-insensitivity of geometric properties to enable assignment of ground states for the majority (ca. 80-90%) of structures. We demonstrate the utility of the ANN by data-mining the literature for spin-crossover (SCO) complexes, which have experimentally-observed temperature-dependent geometric structure changes, by correctly assigning almost all (&gt; 95%) spin states in the 46 Fe(II) SCO complex set. This approach represents a promising complement to more conventional energy-based spin-state assignment from electronic structure theory at the low cost of a machine learning model. &lt;/p&gt; Spins ; Ground state ; Heuristics ; Electronic structure ; Density functional theory ; Artificial neural network ; Crystal structure prediction ; Spin states ; Spin (aerodynamics) ; Bond length ; Chemistry ; Machine learning ; Artificial intelligence ; Materials science ; Statistical physics ; Physics ; Computer science ; Computational chemistry ; Condensed matter physics ; Atomic physics ; Mathematics ; Crystallography ; Thermodynamics ; Crystal structure ; Mathematical optimization Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Electrocatalysts for Energy Conversion
Authors: Andrés Díaz Lantada | Francisco Franco Martínez | Stefan Hengsbach | Florian Rupp | Richard Thelen | Klaus J. Bade
Source: Nanomaterials (article, 2020-11-18)
Field: Materials Science — Surfaces, Coatings and Films

Artificial Intelligence Aided Design of Microtextured Surfaces: Application to Controlling Wettability Artificial intelligence (AI) has emerged as a powerful set of tools for engineering innovative materials. However, the AI-aided design of materials textures has not yet been researched in depth. In order to explore the potentials of AI for discovering innovative biointerfaces and engineering materials surfaces, especially for biomedical applications, this study focuses on the control of wettability through design-controlled hierarchical surfaces, whose design is supported and its performance predicted thanks to adequately structured and trained artificial neural networks (ANN). The authors explain the creation of a comprehensive library of microtextured surfaces with well-known wettability properties. Such a library is processed and employed for the generation and training of artificial neural networks, which can predict the actual wetting performance of new design biointerfaces. The present research demonstrates that AI can importantly support the engineering of innovative hierarchical or multiscale surfaces when complex-to-model properties and phenomena, such as wettability and wetting, are involved. Wetting ; Artificial neural network ; Computer science ; Set (abstract data type) ; Artificial intelligence ; Nanotechnology ; Materials science Surface Modification and Superhydrophobicity ; Additive Manufacturing and 3D Printing Technologies ; Adhesion, Friction, and Surface Interactions
Authors: Amin Sokhansanj | S. Majid Abdoli | Mohammad Zabihi
Source: Process Safety and Environmental Protection (article, 2020-05-28)
Field: Materials Science — Materials Chemistry

Insight into simultaneous catalytic oxidation of benzene and toluene in air over the nano-catalyst: Experimental and modeling via CFD-ANN hybrid method Toluene ; Benzene ; Catalysis ; Materials science ; Computational fluid dynamics ; Chemical engineering ; Multiphysics ; Catalytic oxidation ; Chemistry ; Organic chemistry ; Thermodynamics Catalytic Processes in Materials Science ; Gas Sensing Nanomaterials and Sensors ; Industrial Gas Emission Control
Authors: Yida Liu | Run Hu | Yan Wang | Jinglong Ma | Zhangcan Yang | Xiaobing Luo
Source: Energy and AI (article, 2020-12-30)
Field: Materials Science — Materials Chemistry

Big-data-accelerated aperiodic Si/Ge superlattice prediction for quenching thermal conduction via pattern analysis Thermal conductivity of material is one of the basic physical properties and plays an important role in manipulating thermal energy. In order to accelerate the prediction of material structure with desired thermal property, machine learning algorithm has been widely adopted. However, in the optimization of multivariable material structure such as different lengths or proportions, the machine learning algorithm may be required to be reconducted again and again for each variable, which will consume a lot of computing resources. Recently, it has been found that the thermal conductivity of aperiodic superlattices is closely related to the degree of the structural randomness, which can also be reflected in their local pattern structures. Inspired by these, we propose a new pattern analysis method, in which machine learning only needs to be carried out for one time, and through which the optimal structure of different variables with low thermal conductivity can be obtained. To verify the method, we compare the thermal conductivities of the structure obtained by pattern analysis, conventional machine learning, and previous literature, respectively. The pattern analysis method is validated to greatly reduce the prediction time of multivariable structure with high enough accuracy and may promote further development of material informatics. Thermal conductivity ; Aperiodic graph ; Randomness ; Thermal conduction ; Computer science ; Multivariable calculus ; Superlattice ; Thermal ; Algorithm ; Artificial intelligence ; Materials science ; Mathematics ; Engineering ; Statistics ; Optoelectronics ; Thermodynamics ; Physics ; Composite material ; Control engineering Thermal properties of materials ; Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices
Authors: O. A. Yakovtseva | М. Н. Ситкина | Ahmed O. Mosleh | Anastasia V. Mikhaylovskaya
Source: Materials (article, 2020-05-01)
Field: Materials Science — Materials Chemistry

High Strain Rate Superplasticity in Al-Zn-Mg-Based Alloy: Microstructural Design, Deformation Behavior, and Modeling Increasing the strain rate at superplastic forming is a challenging technical and economic task of aluminum forming manufacturing. New aluminum sheets exhibiting high strain rate superplasticity at strain rates above 0.01 s<sup>-1</sup> are required. This study describes the microstructure and the superplasticity properties of a new high-strength Al-Zn-Mg-based alloy processed by a simple thermomechanical treatment including hot and cold rolling. The new alloy contains Ni to form Al<sub>3</sub>Ni coarse particles and minor additions of Zr (0.19 wt.%) and Sc (0.06 wt.%) to form nanoprecipitates of the L1<sub>2</sub>-Al<sub>3</sub> (Sc,Zr) phase. The design of chemical and phase compositions of the alloy provides superplasticity with an elongation of 600-800% in a strain rate range of 0.01 to 0.6/s and residual cavitation less than 2%. A mean elongation-to-failure of 400% is observed at an extremely high constant strain rate of 1 s<sup>-1</sup>. The strain-induced evolution of the grain and dislocation structures as well as the L1<sub>2</sub> precipitates at superplastic deformation is studied. The dynamic recrystallization at superplastic deformation is confirmed. The superplastic flow behavior of the proposed alloy is modeled via a mathematical Arrhenius-type constitutive model and an artificial neural network model. Both models exhibit good predictability at low and high strain rates of superplastic deformation. Superplasticity ; Materials science ; Strain rate ; Alloy ; Metallurgy ; Deformation (meteorology) ; Microstructure ; Deformation mechanism ; Composite material Microstructure and mechanical properties ; Aluminum Alloys Composites Properties ; Aluminum Alloy Microstructure Properties
Authors: Wil Gardner | David A. Winkler | Davide Ballabio | Benjamin W. Muir | Paul J. Pigram
Source: Biointerphases (article, 2020-11-01)
Field: Chemistry — Analytical Chemistry

Analyzing 3D hyperspectral TOF-SIMS depth profile data using self-organizing map-relational perspective mapping The advantages of applying multivariate analysis to mass spectrometry imaging (MSI) data have been thoroughly demonstrated in recent decades. The identification and visualization of complex relationships between pixels in a hyperspectral data set can provide unique insights into the underlying surface chemistry. It is now recognized that most MSI data contain nonlinear relationships, which has led to increased application of machine learning approaches. Previously, we exemplified the use of the self-organizing map (SOM), a type of artificial neural network, for analyzing time-of-flight secondary ion mass spectrometry (TOF-SIMS) hyperspectral images. Recently, we developed a novel methodology, SOM-relational perspective mapping (RPM), which incorporates the algorithm RPM to improve visualization of the SOM for 2D TOF-SIMS images. Here, we use SOM-RPM to characterize and interpret 3D TOF-SIMS depth profile data, voxel-by-voxel. An organic Irganox<sup>™</sup> multilayer standard sample was depth profiled using TOF-SIMS, and SOM-RPM was used to create 3D similarity maps of the depth-profiled sample, in which the mass spectral similarity of individual voxels is modeled with color similarity. We used this similarity map to segment the data into spatial features, demonstrating that the unsupervised method meaningfully differentiated between Irganox-3114 and Irganox-1010 nanometer-thin multilayer films. The method also identified unique clusters at the surface associated with environmental exposure and sample degradation. Key fragment ions characteristic of each cluster were identified, tying clusters to their underlying chemistries. SOM-RPM has the demonstrable ability to reduce vast data sets to simple 3D visualizations that can be used for clustering data and visualizing the complex relationships within. Hyperspectral imaging ; Self-organizing map ; Similarity (geometry) ; Pattern recognition (psychology) ; Voxel ; Artificial intelligence ; Visualization ; Sample (material) ; Computer science ; Artificial neural network ; Chemistry ; Chromatography ; Image (mathematics) Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Remote-Sensing Image Classification
Authors: Jiyoung Yang | Matthias J. Knape | Oliver Burkert | Virginia Mazzini | Alexander Jung | Vincent S. J. Craig | Ramón Alain Miranda‐Quintana | Erich Bluhmki | Jens Smiatek
Source: Physical Chemistry Chemical Physics (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Artificial neural networks for the prediction of solvation energies based on experimental and computational data The knowledge of thermodynamic properties for novel electrolyte formulations is of fundamental interest for industrial applications as well as academic research. Herewith, we present an artificial neural networks (ANN) approach for the prediction of solvation energies and entropies for distinct ion pairs in various protic and aprotic solvents. The considered feed-forward ANN is trained either by experimental data or computational results from conceptual density functional theory calculations. The proposed concept of mapping computed values to experimental data lowers the amount of time-consuming and costly experiments and helps to overcome certain limitations. Our findings reveal high correlation coefficients between predicted and experimental values which demonstrate the validity of our approach. Solvation ; Artificial neural network ; Computer science ; Artificial intelligence ; Statistical physics ; Machine learning ; Computational chemistry ; Chemistry ; Physics ; Molecule ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Free Radicals and Antioxidants
Authors: Imane Almi | Salah Belaıdı | Enfale Zerroug | Mebarka Alloui | Ridha Ben Said | Roberto Linguerri | M. Hochlaf
Source: Journal of Molecular Structure (article, 2020-03-17)
Field: Chemistry — Organic Chemistry

QSAR investigations and structure-based virtual screening on a series of nitrobenzoxadiazole derivatives targeting human glutathione-S-transferases Quantitative structure–activity relationship ; Chemistry ; In silico ; Applicability domain ; Molecular descriptor ; Virtual screening ; Linear regression ; Computational biology ; Biological system ; Machine learning ; Stereochemistry ; Computational chemistry ; Computer science ; Molecular dynamics ; Biochemistry Synthesis and biological activity ; Computational Drug Discovery Methods ; Fungal Plant Pathogen Control