jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ardavan Farahvash | Chee‐Kong Lee | Qiming Sun | Liang Shi | Adam P. Willard
Source: The Journal of Chemical Physics (article, 2020-08-20)
Field: Materials Science — Materials Chemistry

Machine learning Frenkel Hamiltonian parameters to accelerate simulations of exciton dynamics In this manuscript, we develop multiple machine learning (ML) models to accelerate a scheme for parameterizing site-based models of exciton dynamics from all-atom configurations of condensed phase sexithiophene systems. This scheme encodes the details of a system's specific molecular morphology in the correlated distributions of model parameters through the analysis of many single-molecule excited-state electronic-structure calculations. These calculations yield excitation energies for each molecule in the system and the network of pair-wise intermolecular electronic couplings. Here, we demonstrate that the excitation energies can be accurately predicted using a kernel ridge regression (KRR) model with Coulomb matrix featurization. We present two ML models for predicting intermolecular couplings. The first one utilizes a deep neural network and bi-molecular featurization to predict the coupling directly, which we find to perform poorly. The second one utilizes a KRR model to predict unimolecular transition densities, which can subsequently be analyzed to compute the coupling. We find that the latter approach performs excellently, indicating that an effective, generalizable strategy for predicting simple bimolecular properties is through the indirect application of ML to predict higher-order unimolecular properties. Such an approach necessitates a much smaller feature space and can incorporate the insight of well-established molecular physics. Statistical physics ; Excited state ; Hamiltonian (control theory) ; Intermolecular force ; Excitation ; Exciton ; Coupling (piping) ; Physics ; Coulomb ; Computer science ; Molecule ; Quantum mechanics ; Materials science ; Mathematics Machine Learning in Materials Science ; Molecular Junctions and Nanostructures ; Spectroscopy and Quantum Chemical Studies
Authors: Luis A. Miccio | Gustavo A. Schwartz
Source: Polymer (article, 2020-07-16)
Field: Materials Science — Materials Chemistry

Localizing and quantifying the intra-monomer contributions to the glass transition temperature using artificial neural networks Monomer ; Glass transition ; Polymer ; Artificial neural network ; Materials science ; Polymer science ; Biological system ; Set (abstract data type) ; Polymer chemistry ; Computer science ; Artificial intelligence ; Composite material Machine Learning in Materials Science ; Epoxy Resin Curing Processes ; Thermal and Kinetic Analysis
Authors: Alessandro Lunghi | Stefano Sanvito
Source: The Journal of Physical Chemistry C (article, 2020-02-13)
Field: Materials Science — Materials Chemistry

Surfing Multiple Conformation-Property Landscapes via Machine Learning: Designing Single-Ion Magnetic Anisotropy Computational statistical disciplines, such as machine learning, are leading to a paradigm shift in the way we conceive the design of new compounds, offering a way to directly design the best compound for specific applications. This approach, known as reverse engineering, requires the construction of models able to efficiently predict continuous structure–property maps. Here, we show that machine learning offers such a possibility by designing a model that predicts both the energy and magnetic properties as a function of the molecular structure of a single-ion magnet. This model is then used to explore the molecular conformational landscapes in search of structures that maximize magnetic anisotropy. We find that a 5% change in one of the coordination angles leads to a ∼50% increase in the anisotropy. This approach can be applied to any structure–property relation and paves the way for a machine-learning-driven optimization of chemical compounds. Anisotropy ; Property (philosophy) ; Computer science ; Function (biology) ; Artificial intelligence ; Magnetic anisotropy ; Energy (signal processing) ; Magnet ; Ion ; Anisotropy energy ; Machine learning ; Algorithm ; Mechanical engineering ; Physics ; Engineering ; Magnetic field ; Magnetization ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Marc O. J. Jäger | Yashasvi S. Ranawat | Filippo Federici Canova | Eiaki V. Morooka | Adam S. Foster
Source: ACS Combinatorial Science (article, 2020-11-04)
Field: Materials Science — Materials Chemistry

Efficient Machine-Learning-Aided Screening of Hydrogen Adsorption on Bimetallic Nanoclusters Nanoclusters add an additional dimension in which to look for promising catalyst candidates, since catalytic activity of materials often changes at the nanoscale. However, the large search space of relevant atomic sites exacerbates the challenge for computational screening methods and requires the development of new techniques for efficient exploration. We present an automated workflow that systematically manages simulations from the generation of nanoclusters through the submission of production jobs, to the prediction of adsorption energies. The presented workflow was designed to screen nanoclusters of arbitrary shapes and size, but in this work the search was restricted to bimetallic icosahedral clusters and the adsorption was exemplified on the hydrogen evolution reaction. We demonstrate the efficient exploration of nanocluster configurations and screening of adsorption energies with the aid of machine learning. The results show that the maximum of the <i>d</i>-band Hilbert-transform ϵ<sub><i>u</i></sub> is correlated strongly with adsorption energies and could be a useful screening property accessible at the nanocluster level. Nanoclusters ; Bimetallic strip ; Workflow ; Adsorption ; Chemistry ; Icosahedral symmetry ; Nanotechnology ; Catalysis ; Chemical physics ; Computer science ; Materials science ; Physical chemistry ; Crystallography ; Organic chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Advanced Chemical Physics Studies
Authors: Thanh-Dung Le | Rita Noumeir | Huu Luong Quach | Ji Hyung Kim | Jung Ho Kim | Ho Min Kim
Source: IEEE Transactions on Applied Superconductivity (article, 2020-02-04)
Field: Materials Science — Materials Chemistry

Critical Temperature Prediction for a Superconductor: A Variational Bayesian Neural Network Approach Much research in recent years has focused on using empirical machine learning approaches to extract useful insights on the structure-property relationships of superconductor material. Notably, these approaches are bringing extreme benefits when superconductivity data often come from costly and arduously experimental work. However, this assessment cannot be based solely on an open black-box machine learning, which is not fully interpretable, because it can be counter-intuitive to understand why the model may give an appropriate response to a set of input data for superconductivity characteristic analyses, e.g., critical temperature. The purpose of this study is to describe and examine an alternative approach for predicting the superconducting transition temperature T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</sub> from SuperCon database obtained by Japan's National Institute for Materials Science. We address a generative machine-learning framework called Variational Bayesian Neural Network using superconductors chemical elements and formula to predict T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</sub> . In such a context, the importance of the paper in focus is twofold. First, to improve the interpretability, we adopt a variational inference to approximate the distribution in latent parameter space for the generative model. It statistically captures the mutual correlation of superconductor compounds and; then, gives the estimation for the T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</sub> . Second, a stochastic optimization algorithm, which embraces a statistical inference named Monte Carlo sampler, is utilized to optimally approximate the proposed inference model, ultimately determine and evaluate the predictive performance. As a result, in comparison with the standard evaluation metrics, the results are promising and also agree with the existing models prevalent in the field. The R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> value obtained is very close to the best model (0.94), whereas a considerable improvement is seen in the RMSE value (3.83 K). Notably, the proposed model is known as the first of its kind for predicting a superconductor's T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</sub> . Artificial intelligence ; Machine learning ; Interpretability ; Artificial neural network ; Computer science ; Inference ; Context (archaeology) Machine Learning in Materials Science ; Topic Modeling ; X-ray Diffraction in Crystallography
Authors: Markku Tiitta | Valtteri Tiitta | Jorma Heikkinen | Reijo Lappalainen | Laura Tomppo
Source: Sensors (article, 2020-02-17)
Field: Chemistry — Organic Chemistry

Classification of Wood Chips Using Electrical Impedance Spectroscopy and Machine Learning Wood chips are extensively utilised as raw material for the pulp and bio-fuel industry, and advanced material analyses may improve the processes in utilizing these products. Electrical impedance spectroscopy (EIS) combined with machine learning was used in order to analyse heartwood content of pine chips and bark content of birch chips. A novel electrode system integrated in a sampling container was developed for the testing using frequency range 42 Hz-5 MHz. Three electrode pairs were used to measure the samples in x-, y- and z-direction. Three machine learning methods were used: K-nearest neighbor (KNN), decision tree (DT) and support vector machines (SVM). The heartwood content of pine chips and bark content of birch chips were classified with an accuracy of 91% using EIS from pure materials combined with a k-nearest neighbour classifier. When using mixed materials and multiple classes, 73% correct classification for pine heartwood content (four groups) and 64% for birch bark content (five groups) were achieved. Dielectric spectroscopy ; Spectroscopy ; Electrical impedance ; Materials science ; Computer science ; Engineering ; Electrical engineering ; Chemistry ; Physics ; Electrochemistry Wood and Agarwood Research ; Industrial Vision Systems and Defect Detection ; Wood Treatment and Properties
Authors: G. P. Purja Pun | V. Yamakov | J. Hickman | E. H. Glaessgen | Y. Mishin
Source: Physical Review Materials (article, 2020-11-19)
Field: Materials Science — Materials Chemistry

Development of a general-purpose machine-learning interatomic potential for aluminum by the physically informed neural network method Interatomic potentials are the key components of large-scale atomistic simulations of materials. The recently proposed physically-informed neural network (PINN) method combines a high-dimensional regression implemented by an artificial neural network with a physics-based bond-order interatomic potential. Here, the authors develop a highly accurate and transferable PINN potential that reproduces a broad spectrum of physical properties of Al, ranging from lattice dynamics and defect energies to liquid structure and dynamic and the solid-liquid interface tension. The potential enables atomistic simulations of Al with nearly first-principles accuracy while being orders of magnitudes faster. Interatomic potential ; Artificial neural network ; Molecular dynamics ; Lattice (music) ; Aluminium ; Interface (matter) Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Quantum many-body systems
Authors: Atsuto Seko
Source: Physical review. B./Physical review. B (article, 2020-11-09)
Field: Materials Science — Materials Chemistry

Machine learning potentials for multicomponent systems: The Ti-Al binary system Machine learning potentials (MLPs) are becoming powerful tools for performing accurate atomistic simulations and crystal structure optimizations. An approach to developing MLPs employs a systematic set of polynomial invariants including high-order ones to represent the neighboring atomic density. In this study, a formulation of the polynomial invariants is extended to the case of multicomponent systems. The extended formulation is more complex than the formulation for elemental systems. This study also shows its application to the Ti-Al binary system. As a result, an MLP with the lowest error and MLPs with high computational cost performance are selected from the many MLPs developed systematically. The predictive powers of the developed MLPs for many properties, such as the formation energy, elastic constants, thermodynamic properties, and mechanical properties, are examined. The MLPs exhibit high predictive power for the properties in a wide variety of ordered structures. The present scheme should be systematically applicable to other multicomponent systems. Binary number ; Computer science ; Set (abstract data type) ; Polynomial ; Electric power system ; Computational science ; Algorithm ; Power (physics) ; Thermodynamics ; Mathematics ; Physics Machine Learning in Materials Science ; Titanium Alloys Microstructure and Properties ; X-ray Diffraction in Crystallography
Authors: Madhubanti Mukherjee | Swanti Satsangi | Abhishek K. Singh
Source: Chemistry of Materials (article, 2020-07-07)
Field: Materials Science — Materials Chemistry

A Statistical Approach for the Rapid Prediction of Electron Relaxation Time Using Elemental Representatives Efficiency of a thermoelectric material relies on a combination of electronic and thermal transport properties, which are governed by various scattering mechanisms. Explicit evaluation of temperature dependent scattering time or the electron relaxation time (τel) is thus necessary to assess the efficiency of thermoelectrics. Experimental or computational measurement of τel is very challenging due to the inherent time limitation and high computational cost. Herein, a statistical machine learning (ML) based approach has been developed to predict the experimental electrical conductivity (σ) followed by an estimation of the relaxation time (τel). By utilizing a unique mean ranking method for feature selection, simple elemental properties such as the boiling point, melting point, molar heat capacity, electron affinity, and ionization energy are identified as the potential descriptors for σ. Using a data set of 124 compounds, a Gradient Boost Regression (GBR) model is developed, which has very small root-mean-square error (rmse) of 0.22 S/cm and a high coefficient of determination (R2) of 0.98 for prediction of log-scaled σ. Utilizing the predicted σ values, τel has been calculated for a wide range of temperatures. ML predicted τel values outperform the τdef, obtained from the deformation potential model. The developed GBR model for accurate prediction of σ could accelerate the assessment of the efficiency of the thermoelectric materials with unprecedented accuracies. Mean squared error ; Relaxation (psychology) ; Materials science ; Electron ; Thermal conductivity ; Linear regression ; Thermoelectric effect ; Computational physics ; Thermodynamics ; Computer science ; Mathematics ; Machine learning ; Physics ; Statistics Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Thermal properties of materials
Authors: Hang Zhang | Hao Zeng | Arri Priimägi | Olli Ikkala
Source: Advanced Materials (review, 2020-01-31)
Field: Materials Science — Biomaterials

Viewpoint: Pavlovian Materials—Functional Biomimetics Inspired by Classical Conditioning Herein, it is discussed whether the complex biological concepts of (associative) learning can inspire responsive artificial materials. It is argued that classical conditioning, being one of the most elementary forms of learning, inspires algorithmic realizations in synthetic materials, to allow stimuli-responsive materials that learn to respond to a new stimulus, to which they are originally insensitive. Two synthetic model systems coined as "Pavlovian materials" are described, whose stimuli-responsiveness algorithmically mimics programmable associative learning, inspired by classical conditioning. The concepts minimally need a stimulus-triggerable memory, in addition to two stimuli, i.e., the unconditioned and the originally neutral stimuli. Importantly, the concept differs conceptually from the classic stimuli-responsive and shape-memory materials, as, upon association, Pavlovian materials obtain a given response using a new stimulus (the originally neutral one); i.e., the system evolves to a new state. This also enables the functionality to be described by a logic diagram. Ample room for generalization to different stimuli and memory combinations is foreseen, and opportunities to develop future adaptive materials with ever-more intelligent functions are expected. Classical conditioning ; Biomimetics ; Stimulus (psychology) ; Associative property ; Cognitive science ; Associative learning ; Generalization ; Unconditioned stimulus ; Artificial intelligence ; Computer science ; Conditioning ; Conditioned response ; Neuroscience ; Cognitive psychology ; Psychology ; Mathematics Supramolecular Self-Assembly in Materials ; Polydiacetylene-based materials and applications ; Advanced Memory and Neural Computing
Authors: Aditya Nandy | Daniel B. K. Chu | Daniel R. Harper | Chenru Duan | N. Arunachalam | Yael Cytter | Heather J. Kulik
Source: Physical Chemistry Chemical Physics (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Large-scale comparison of 3d and 4d transition metal complexes illuminates the reduced effect of exchange on second-row spin-state energetics Density functional theory (DFT) is widely used in transition-metal chemistry, yet essential properties such as spin-state energetics in transition-metal complexes (TMCs) are well known to be sensitive to the choice of the exchange-correlation functional. Increasing the amount of exchange in a functional typically shifts the preferred ground state in first-row TMCs from low-spin to high-spin by penalizing delocalization error, but the effect on properties of second-row complexes is less well known. We compare the exchange sensitivity of adiabatic spin-splitting energies in pairs of mononuclear 3d and 4d mid-row octahedral transition-metal complexes. We analyze hundreds of complexes assembled from four metals in two oxidation states with ten small monodentate ligands that span a wide range of field strengths expected to favor a variety of ground states. We observe consistently lower but proportional sensitivity to exchange fraction among 4d TMCs with respect to their isovalent 3d TMC counterparts, leading to the largest difference in sensitivities for the strongest field ligands. The combined effect of reduced exchange sensitivities and the greater low-spin bias of most 4d TMCs means that while over one-third of 3d TMCs change ground states over a modest variation (ca. 0.0-0.3) in exchange fraction, almost no 4d TMCs do. Differences in delocalization, as judged through changes in the metal-ligand bond lengths between spin states, do not explain the distinct behavior of 4d TMCs. Instead, evaluation of potential energy curves in 3d and 4d TMCs reveals that higher exchange sensitivities in 3d TMCs are likely due to the opposing effect of exchange on the low-spin and high-spin states, whereas the effect on both spin states is more comparable in 4d TMCs. Energetics ; Transition metal ; Spin states ; Scale (ratio) ; Spin (aerodynamics) ; Sensitivity (control systems) ; Chemical physics ; Chemistry ; Nanotechnology ; Materials science ; Physics ; Thermodynamics ; Inorganic chemistry ; Organic chemistry ; Engineering ; Catalysis Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; CO2 Reduction Techniques and Catalysts
Authors: Ingrid Milošev | Dolores Zimerl | Ch. Carriére | Sandrine Zanna | Antoine Seyeux | Jernej Iskra | Stojan Stavber | Fatah Chiter | Matic Poberžnik | Dominique Costa | Anton Kokalj | Philippe Marcus
Source: Journal of The Electrochemical Society (article, 2020-01-04)
Field: Materials Science — Materials Chemistry

Editors’ Choice—The Effect of Anchor Group and Alkyl Backbone Chain on Performance of Organic Compounds as Corrosion Inhibitors for Aluminum Investigated Using an Integrative Experimental-Modeling Approach An alkaline etched, superhydrophilic aluminum surface was modified using functionalized alkyl compounds selected to study the effect of their properties on adsorption on the metal surface. The thirteen organic compounds differed in alkyl chain length (eight and eighteen carbon atoms) and anchor group (azide, imidazole, thiocyanate, amino, disulfide, thiol, phosphonic, carboxylic, and benzoic). The methodology of the study integrated a complete chain of steps incorporating synthesis, electrochemical and surface analyses, and computational modeling. The corrosion resistant and superhydrophobic properties depend on the anchor group, which governs adhesion to the surface, and backbone, which is responsible for lateral cohesive interactions. The morphology and chemical composition of modified layers were studied using scanning electron microscopy, X-ray photoelectron spectroscopy and time-of-flight secondary ion mass spectrometry. Electrochemical and long-term immersion properties were investigated in 0.5 M NaCl. Calculations based on density functional theory were performed as to model the adsorption of selected anchor groups on the hydroxylated oxidized aluminum surface. Integrated results allowed the identification of the anchor groups that are able to form inhibitive adsorbed layers on Al surface regardless the alkyl chain length, and those that are not able to form adsorbed layers at all and are thus not efficient corrosion inhibitors. Alkyl ; Adsorption ; Chemistry ; X-ray photoelectron spectroscopy ; Electrochemistry ; Functional group ; Inorganic chemistry ; Polymer chemistry ; Chemical engineering ; Organic chemistry ; Physical chemistry ; Polymer Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Henry Herbol | Matthias Poloczek | Paulette Clancy
Source: Materials Horizons (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Cost-effective materials discovery: Bayesian optimization across multiple information sources Multi-information source Bayesian optimization and how it can be used to capture relevant information from cheap approximations to accelerate research in the materials sciences. Bayesian optimization ; Bayesian probability ; Prior information ; Computer science ; Data science ; Data mining ; Machine learning ; Artificial intelligence Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods