jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hideo Doi | Kazuaki Z. Takahashi | Kenji Tagashira | Jun‐ichi Fukuda | Takeshi Aoyagi
Source: Scientific Reports (article, 2019-11-08)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine learning-aided analysis for complex local structure of liquid crystal polymers Abstract Elucidation of mesoscopic structures of molecular systems is of considerable scientific and technological interest for the development and optimization of advanced materials. Molecular dynamics simulations are a promising means of revealing macroscopic physical properties of materials from a microscopic viewpoint, but analysis of the resulting complex mesoscopic structures from microscopic information is a non-trivial and challenging task. In this study, a Machine Learning-aided Local Structure Analyzer (ML-LSA) is developed to classify the complex local mesoscopic structures of molecules that have not only simple atomistic group units but also rigid anisotropic functional groups such as mesogens. The proposed ML-LSA is applied to classifying the local structures of liquid crystal polymer (LCP) systems, which are of considerable scientific and technological interest because of their potential for sensors and soft actuators. A machine learning (ML) model is constructed from small, and thus computationally less costly, monodomain LCP trajectories. The ML model can distinguish nematic- and smectic-like monodomain structures with high accuracy. The ML-LSA is applied to large, complex quenched LCP structures, and the complex local structures are successfully classified as either nematic- or smectic-like. Furthermore, the results of the ML-LSA suggest the best order parameter for distinguishing the two mesogenic structures. Our ML model enables automatic and systematic analysis of the mesogenic structures without prior knowledge, and thus can overcome the difficulty of manually determining the specific order parameter required for the classification of complex structures. Mesoscopic physics ; Mesogen ; Liquid crystal ; Computer science ; Complex system ; Biological system ; Materials science ; Artificial intelligence ; Molecular dynamics ; Anisotropy ; Statistical physics ; Physics ; Chemistry ; Liquid crystalline ; Optics ; Computational chemistry ; Condensed matter physics Liquid Crystal Research Advancements ; Material Dynamics and Properties ; Advanced Materials and Mechanics
Authors: Paulius Mikulskis | Morgan R. Alexander | David A. Winkler
Source: Advanced Intelligent Systems (article, 2019-09-27)
Field: Materials Science — Materials Chemistry

Toward Interpretable Machine Learning Models for Materials Discovery Machine learning (ML) and artificial intelligence (AI) methods for modeling useful materials properties are now important technologies for rational design and optimization of bespoke functional materials. Although these methods make good predictions of the properties of new materials, current modeling methods use efficient but rather arcane (difficult‐to‐interpret) mathematical features (descriptors) to characterize materials. Data‐driven ML models are considerably more useful if more chemically interpretable descriptors are used to train them, as long as these models also accurately recapitulate the properties of materials in training and test sets used to generate and validate the models. Herein, how a particular type of molecular fragment descriptor, the signature descriptor, achieves these joint aims of accuracy and interpretability is described. Seven different types of materials properties are modeled, and the performance of models generated from signature descriptors is compared with those generated by widely used Dragon descriptors. The key descriptors in the model represent functionalities that make chemical sense. Mapping these fragments back on to exemplar materials provides a useful guide to chemists wishing to modify promising lead materials to improve their properties. This is one of the first applications of signature descriptors to the modeling of complex materials properties. Interpretability ; Computer science ; Bespoke ; Artificial intelligence ; Machine learning ; Signature (topology) ; Key (lock) ; Data mining ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Shaoping Xiao | Renjie Hu | Zhen Li | Siamak Attarian | Kaj-Mikael Björk | Amaury Lendasse
Source: Neural Computing and Applications (article, 2019-09-18)
Field: Materials Science — Materials Chemistry

A machine-learning-enhanced hierarchical multiscale method for bridging from molecular dynamics to continua Multiscale modeling ; Bridging (networking) ; Computer science ; Computational Science and Engineering ; Molecular dynamics ; Artificial intelligence ; Artificial neural network ; Support vector machine ; Computational model ; Machine learning ; Physics ; Bioinformatics Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Force Microscopy Techniques and Applications
Authors: Hidemaro Suwa | Justin S. Smith | Nicholas Lubbers | Cristian D. Batista | Gia-Wei Chern | Kipton Barros
Source: Physical review. B./Physical review. B (article, 2019-04-08)
Field: Materials Science — Materials Chemistry

Machine learning for molecular dynamics with strongly correlated electrons We use machine learning to enable large-scale molecular dynamics (MD) of a correlated electron model under the Gutzwiller approximation scheme. This model exhibits a Mott transition as a function of on-site Coulomb repulsion $U$. The repeated solution of the Gutzwiller self-consistency equations would be prohibitively expensive for large-scale MD simulations. We show that machine learning models of the Gutzwiller potential energy can be remarkably accurate. The models, which are trained with $N=33$ atoms, enable highly accurate MD simulations at much larger scales ($N\ensuremath{\gtrsim}{10}^{3}$). We investigate the physics of the smooth Mott crossover in the fluid phase. Crossover ; Coulomb ; Physics ; Statistical physics ; Electron ; Molecular dynamics ; Consistency (knowledge bases) ; Strongly correlated material ; Scale (ratio) ; Function (biology) ; Hubbard model ; Quantum mechanics ; Computer science ; Machine learning ; Artificial intelligence Machine Learning in Materials Science ; Quantum, superfluid, helium dynamics ; Advanced Chemical Physics Studies
Authors: Joshua P. McClure | Jonathan Boltersdorf | David R. Baker | Thomas G. Farinha | Nicholas Dzuricky | Cesar E. P. Villegas | Alexandre Reily Rocha | Marina S. Leite
Source: ACS Applied Materials & Interfaces (article, 2019-05-02)
Field: Materials Science — Materials Chemistry

Structure–Property-Performance Relationship of Ultrathin Pd–Au Alloy Catalyst Layers for Low-Temperature Ethanol Oxidation in Alkaline Media Pd-containing alloys are promising materials for catalysis. Yet, the relationship of the structure-property performance strongly depends on their chemical composition, which is currently not fully resolved. Herein, we present a physical vapor deposition methodology for developing Pd<sub><i>x</i></sub>Au<sub>1-<i>x</i></sub> alloys with fine control over the chemical composition. We establish direct correlations between the composition and these materials' structural and electronic properties with its catalytic activity in an ethanol (EtOH) oxidation reaction. By combining X-ray diffraction (XRD) and X-ray photelectron spectroscopy (XPS) measurements, we validate that the Pd content within both bulk and surface compositions can be finely controlled in an ultrathin-film regime. Catalytic oxidation of EtOH on the Pd<sub><i>x</i></sub>Au<sub>1-<i>x</i></sub> electrodes presents the largest forward-sweeping current density for <i>x</i> = 0.73 at ∼135 mA cm<sup>-2</sup>, with the lowest onset potential and largest peak activity of 639 A g<sub>Pd</sub><sup>-1</sup> observed for <i>x</i> = 0.58. Density functional theory (DFT) calculations and XPS measurements demonstrate that the valence band of the alloys is completely dominated by Pd particularly near the Fermi level, regardless of its chemical composition. Moreover, DFT provides key insights into the Pd<sub><i>x</i></sub>Au<sub>1-<i>x</i></sub> ligand effect, with relevant chemisorption activity descriptors probed for a large number of surface arrangements. These results demonstrate that alloys can outperform pure metals in catalytic processes, with fine control of the chemical composition being a powerful tuning knob for the electronic properties and, therefore, the catalytic activity of ultrathin Pd<sub><i>x</i></sub>Au<sub>1-<i>x</i></sub> catalysts. Our high-throughput experimental methodology, in connection with DFT calculations, provides a unique foundation for further materials' discovery, including machine-learning predictions for novel alloys, the development of Pd-alloyed membranes for the purification of reformate gases, binder-free ultrathin electrocatalysts for fuel cells, and room temperature lithography-based development of nanostructures for optically driven processes. Materials science ; Catalysis ; Alloy ; Ethanol ; Chemical engineering ; Inorganic chemistry ; Metallurgy ; Organic chemistry Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion ; Catalysts for Methane Reforming
Authors: Mathias Jørgensen | Henrik Lund Mortensen | Søren Ager Meldgaard | Esben L. Kolsbjerg | T.L. Jacobsen | Knud H. Sørensen | Bjørk Hammer
Source: The Journal of Chemical Physics (article, 2019-08-06)
Field: Materials Science — Materials Chemistry

Atomistic structure learning One endeavor of modern physical chemistry is to use bottom-up approaches to design materials and drugs with desired properties. Here, we introduce an atomistic structure learning algorithm (ASLA) that utilizes a convolutional neural network to build 2D structures and planar compounds atom by atom. The algorithm takes no prior data or knowledge on atomic interactions but inquires a first-principles quantum mechanical program for thermodynamical stability. Using reinforcement learning, the algorithm accumulates knowledge of chemical compound space for a given number and type of atoms and stores this in the neural network, ultimately learning the blueprint for the optimal structural arrangement of the atoms. ASLA is demonstrated to work on diverse problems, including grain boundaries in graphene sheets, organic compound formation, and a surface oxide structure. Atom (system on chip) ; Reinforcement learning ; Convolutional neural network ; Artificial neural network ; Computer science ; Blueprint ; Materials science ; Stability (learning theory) ; Oxide ; Space (punctuation) ; Nanotechnology ; Artificial intelligence ; Machine learning ; Engineering ; Mechanical engineering ; Parallel computing ; Metallurgy Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Ferroelectric and Negative Capacitance Devices
Authors: Dmitrij Rappoport | Alán Aspuru‐Guzik
Source: Journal of Chemical Theory and Computation (article, 2019-06-04)
Field: Materials Science — Materials Chemistry

Predicting Feasible Organic Reaction Pathways Using Heuristically Aided Quantum Chemistry Studying organic reaction mechanisms using quantum chemical methods requires from the researcher an extensive knowledge of both organic chemistry and first-principles computation. The need for empirical knowledge arises because any reasonably complete exploration of the potential energy surfaces (PES) of organic reactions is computationally prohibitive. We have previously introduced the heuristically-aided quantum chemistry (HAQC) approach to modeling complex chemical reactions, which abstracts the empirical knowledge in terms of chemical heuristics-simple rules guiding the PES exploration-and combines them with structure optimizations using quantum chemical methods. The HAQC approach makes use of heuristic kinetic criteria for selecting reaction paths that are not only plausible, that is, consistent with the empirical rules of organic reactivity, but also feasible under the reaction conditions. In this work, we develop heuristic kinetic feasibility criteria, which correctly predict feasible reaction pathways for a wide range of simple polar (substitutions, additions, and eliminations) and pericyclic organic reactions (cyclizations, sigmatropic shifts, and cycloadditions). In contrast to knowledge-based reaction mechanism prediction methods, the same kinetic heuristics are successful in classifying reaction pathways as feasible or infeasible across this diverse set of reaction mechanisms. We discuss the energy profiles of HAQC and their potential applications in machine learning of chemical reactivity. Heuristics ; Pericyclic reaction ; Computer science ; Heuristic ; Chemistry ; Biochemical engineering ; Quantum ; Computational chemistry ; Quantum chemical ; Biological system ; Artificial intelligence ; Organic chemistry ; Molecule ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Devin Adams | David T. Fullwood | R. H. Wagoner | Eric R. Homer
Source: Computational Materials Science (article, 2019-04-15)
Field: Materials Science — Materials Chemistry

Atomistic survey of grain boundary-dislocation interactions in FCC nickel Dislocation ; Burgers vector ; Materials science ; Grain boundary ; Peierls stress ; Condensed matter physics ; Molecular dynamics ; Crystallite ; Shear stress ; Crystallography ; Dislocation creep ; Nickel ; Slip (aerodynamics) ; Metallurgy ; Composite material ; Physics ; Microstructure ; Thermodynamics ; Chemistry ; Computational chemistry Microstructure and mechanical properties ; Hydrogen embrittlement and corrosion behaviors in metals ; Microstructure and Mechanical Properties of Steels
Authors: Vishwesh Venkatraman | Amsalu Efrem Yemene | John C. de Mello
Source: Scientific Reports (article, 2019-11-18)
Field: Materials Science — Materials Chemistry

Prediction of Absorption Spectrum Shifts in Dyes Adsorbed on Titania Dye adsorption on metal-oxide films often results in small to substantial absorption shifts relative to the solution phase, with undesirable consequences for the performance of dye-sensitized solar cells and optical sensors. While density functional theory is frequently used to model such behaviour, it is too time-consuming for rapid assessment. In this paper, we explore the use of supervised machine learning to predict whether dye adsorption on titania is likely to induce a change in its absorption characteristics. The physicochemical features of each dye were encoded as a numeric vector whose elements are the counts of molecular fragments and topological indices. Various classification models were subsequently trained to predict the type of absorption shift i.e. blue, red or unchanged (|Δλ| ≤ 10 nm). The models were able to predict the nature of the shift with a good likelihood (~80%) of success when applied to unseen data. Adsorption ; Absorption (acoustics) ; Support vector machine ; Materials science ; Density functional theory ; Computer science ; Absorption spectroscopy ; Oxide ; Biological system ; Chemical physics ; Chemistry ; Analytical Chemistry (journal) ; Machine learning ; Computational chemistry ; Optics ; Physical chemistry ; Physics ; Chromatography ; Biology ; Composite material Machine Learning in Materials Science ; Electrochemical Analysis and Applications ; Analytical Chemistry and Sensors
Authors: Petar Žuvela | Jonathan David | Xin Yang | Dejian Huang | Ming Wah Wong
Source: International Journal of Molecular Sciences (article, 2019-05-10)
Field: Chemistry — Organic Chemistry

Non-Linear Quantitative Structure–Activity Relationships Modelling, Mechanistic Study and In-Silico Design of Flavonoids as Potent Antioxidants In this work, we developed quantitative structure-activity relationships (QSAR) models for prediction of oxygen radical absorbance capacity (ORAC) of flavonoids. Both linear (partial least squares-PLS) and non-linear models (artificial neural networks-ANNs) were built using parameters of two well-established antioxidant activity mechanisms, namely, the hydrogen atom transfer (HAT) mechanism defined with the minimum bond dissociation enthalpy, and the sequential proton-loss electron transfer (SPLET) mechanism defined with proton affinity and electron transfer enthalpy. Due to pronounced solvent effects within the ORAC assay, the hydration energy was also considered. The four-parameter PLS-QSAR model yielded relatively high root mean square errors (RMSECV = 0.783, RMSEE = 0.668, RMSEP = 0.900). Conversely, the ANN-QSAR model yielded considerably lower errors (RMSEE = 0.180 ± 0.059, RMSEP1 = 0.164 ± 0.128, and RMSEP2 = 0.151 ± 0.114) due to the inherent non-linear relationships between molecular structures of flavonoids and ORAC values. Five-fold cross-validation was found to be unsuitable for the internal validation of the ANN-QSAR model with a high RMSECV of 0.999 ± 0.253; which is due to limited sample size where resampling with replacement is a considerably better alternative. Chemical domains of applicability were defined for both models confirming their reliability and robustness. Based on the PLS coefficients and partial derivatives, both models were interpreted in terms of the HAT and SPLET mechanisms. Theoretical computations based on density functional theory at ωb97XD/6-311++G(d,p) level of theory were also carried out to further shed light on the plausible mechanism of anti-peroxy radical activity. Calculated energetics for simplified models (genistein and quercetin) with peroxyl radical derived from 2,2'-azobis (2-amidino-propane) dihydrochloride suggested that both SPLET and single electron transfer followed by proton loss (SETPL) mechanisms are competitive and more favorable than HAT in aqueous medium. The finding is in good accord with the ANN-based QSAR modelling results. Finally, the strongly predictive ANN-QSAR model was used to predict antioxidant activities for a series of 115 flavonoids designed combinatorially with flavone as a template. Structural trends were analyzed, and general guidelines for synthesis of new flavonoid derivatives with potentially potent antioxidant activities were given. Quantitative structure–activity relationship ; Chemistry ; Partial least squares regression ; Loo ; Applicability domain ; Computational chemistry ; Stereochemistry ; Machine learning ; Computer science Free Radicals and Antioxidants ; Computational Drug Discovery Methods ; Phytochemicals and Antioxidant Activities
Authors: Victor Deklerck | Thomas Mortier | Nathalie Goeders | Robert B. Cody | Willem Waegeman | Edgard O. Espinoza | Joris Van Acker | Jan Van den Bulcke | Hans Beeckman
Source: Wood Science and Technology (article, 2019-06-20)
Field: Chemistry — Organic Chemistry

A protocol for automated timber species identification using metabolome profiling Swietenia macrophylla ; Random forest ; Adulterant ; Pattern recognition (psychology) ; Meliaceae ; Artificial intelligence ; Mathematics ; Statistics ; Computer science ; Chemistry ; Biology ; Ecology ; Botany Wood and Agarwood Research ; Cultural Heritage Materials Analysis ; Biological Stains and Phytochemicals
Authors: Shizhuang Weng | Wenxiu Zhu | Pan Li | Hecai Yuan | Xueyan Zhang | Ling Zheng | Jinling Zhao | Linsheng Huang | Ping Han
Source: Food Chemistry (article, 2019-11-07)
Field: Chemistry — Analytical Chemistry

Dynamic surface-enhanced Raman spectroscopy for the detection of acephate residue in rice by using gold nanorods modified with cysteamine and multivariant methods Acephate ; Raman spectroscopy ; Nanorod ; Residue (chemistry) ; Analytical Chemistry (journal) ; Chemistry ; Partial least squares regression ; Mean squared error ; Biological system ; Chromatography ; Materials science ; Mathematics ; Nanotechnology ; Biochemistry ; Optics ; Statistics ; Biology Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Identification and Quantification in Food
Authors: Taoyi Chen | Thomas A. Manz
Source: RSC Advances (article, 2019-01-01)
Field: Chemistry — Inorganic Chemistry

A collection of forcefield precursors for metal–organic frameworks A host of important performance properties for metal-organic frameworks (MOFs) and other complex materials can be calculated by modeling statistical ensembles. The principle challenge is to develop accurate and computationally efficient interaction models for these simulations. Two major approaches are (i) <i>ab initio</i> molecular dynamics in which the interaction model is provided by an exchange-correlation theory (<i>e.g.</i>, DFT + dispersion functional) and (ii) molecular mechanics in which the interaction model is a parameterized classical force field. The first approach requires further development to improve computational speed. The second approach requires further development to automate accurate forcefield parameterization. Because of the extreme chemical diversity across thousands of MOF structures, this problem is still mostly unsolved today. For example, here we show structures in the 2014 CoRE MOF database contain more than 8 thousand different atom types based on first and second neighbors. Our results showed that atom types based on both first and second neighbors adequately capture the chemical environment, but atom types based on only first neighbors do not. For 3056 MOFs, we used density functional theory (DFT) followed by DDEC6 atomic population analysis to extract a host of important forcefield precursors: partial atomic charges; atom-in-material (AIM) C<sub>6</sub>, C<sub>8</sub>, and C<sub>10</sub> dispersion coefficients; AIM dipole and quadrupole moments; various AIM polarizabilities; quantum Drude oscillator parameters; AIM electron cloud parameters; <i>etc.</i> Electrostatic parameters were validated through comparisons to the DFT-computed electrostatic potential. These forcefield precursors should find widespread applications to developing MOF force fields. Density functional theory ; Ab initio ; Atom (system on chip) ; Statistical physics ; Molecular dynamics ; Force field (fiction) ; Dipole ; Dispersion (optics) ; Computational chemistry ; Chemistry ; Chemical physics ; Physics ; Computer science ; Quantum mechanics Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Crystallography and molecular interactions
Authors: Stephen Wu | G. Lambard | Chang Liu | H. Yamada | Ryo Yoshida
Source: Molecular Informatics (article, 2019-11-05)
Field: Materials Science — Materials Chemistry

iQSPR in XenonPy: A Bayesian Molecular Design Algorithm iQSPR is an inverse molecular design algorithm based on Bayesian inference that was developed in our previous study. Here, the algorithm is integrated in Python as a new module called iQSPR-X in the all-in-one materials informatics platform XenonPy. Our new software provides a flexible, easy-to-use, and extensible platform for users to build customized molecular design algorithms using pre-set modules and a pre-trained model library in XenonPy. In this paper, we describe key features of iQSPR-X and provide guidance on its use, illustrated by an application to a polymer design that targets a specific range of bandgap and dielectric constant. Python (programming language) ; Computer science ; Extensibility ; Bayesian probability ; Algorithm ; Software ; Bayesian inference ; Inverse ; Inference ; Key (lock) ; Data mining ; Artificial intelligence ; Programming language ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics