jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: J. E. Gubernatis | Turab Lookman
Source: Physical Review Materials (article, 2018-12-20)
Field: Materials Science — Materials Chemistry

Machine learning in materials design and discovery: Examples from the present and suggestions for the future Much is being currently written about machine learning applied to materials science, but, what is machine learning? It is certainly not physics, chemistry, or materials science, in which case how do these sciences enter? In this Research Update the authors examine what machine learning is and is not, review several applications of machine learning methods for predicting new materials, noting some of the cases where the predictions have been experimentally validated, and illustrate the spectrum of applications possible. The emphasis is on the broader picture where they discuss some newer methods and more importantly reference their successes. Thus, the paper looks more towards the future than to the past, sharing some of the lessons the authors have learned from their own experience in the field. Field (mathematics) ; Artificial intelligence ; Machine learning ; Materials science ; Engineering ethics ; Computer science ; Management science ; Nanotechnology ; Engineering ; Mathematics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Zijiang Yang | Yuksel C. Yabansu | Dipendra Jha | Wei‐keng Liao | Alok Choudhary | Surya R. Kalidindi | Ankit Agrawal
Source: Acta Materialia (article, 2018-12-31)
Field: Materials Science — Materials Chemistry

Establishing structure-property localization linkages for elastic deformation of three-dimensional high contrast composites using deep learning approaches Microscale chemistry ; Salient ; Materials science ; Artificial intelligence ; Feature (linguistics) ; Deep learning ; Field (mathematics) ; Feature engineering ; Computer science ; Characterization (materials science) ; Property (philosophy) ; Machine learning ; Mechanical engineering ; Nanotechnology ; Engineering Machine Learning in Materials Science ; Composite Material Mechanics ; Concrete and Cement Materials Research
Authors: Takashi Toyao | Keisuke Suzuki | Shoma Kikuchi | Satoru Takakusagi | Ken‐ichi Shimizu | Ichigaku Takigawa
Source: The Journal of Physical Chemistry C (article, 2018-03-26)
Field: Materials Science — Materials Chemistry

Toward Effective Utilization of Methane: Machine Learning Prediction of Adsorption Energies on Metal Alloys The process employed to discover new materials for specific applications typically utilizes screening of large compound libraries. In this approach, the performance of a compound is correlated to the properties of elements referred to as descriptors. In the effort described below, we developed a simple and efficient machine learning (ML) model for predicting adsorption energies of CH4 related species, namely, CH3, CH2, CH, C, and H on the Cu-based alloys. The developed ML model predicted the DFT-calculated adsorption energies with 12 descriptors, which are readily available values for the selected elements. The predictive accuracy of four regression methods (ordinary linear regression by least-squares (OLR), random forest regression (RFR), gradient boosting regression (GBR), and extra tree regression (ETR)) with different numbers of descriptors and different test-set/training-set ratios was quantitatively evaluated using statistical cross validations. Among four types of regression methods, we have found that ETR gave the best performance in predicting the adsorption energies with the average root mean squared errors (RMSEs) below 0.3 eV. Strikingly, despite its simplicity and low computational cost, this model can predict the adsorption energies on a range of Cu-based alloy models (46 in total number) as calculated by using DFT. In addition, we show the ML prediction for the differences in the adsorption energies of CH3 and CH2 on the same surface. This would be of great importance especially when designing the selective catalytic reaction processes to suppress the undesired over-reactions. The accuracy and simplicity of the developed system suggest that adsorption energies can be readily predicted without time-consuming DFT calculations, and eventually, this would allow us to predict the catalytic performances of the solid catalysts. Adsorption ; Linear regression ; Regression ; Gradient boosting ; Regression analysis ; Test set ; Decision tree ; Computer science ; Random forest ; Chemistry ; Materials science ; Mathematics ; Biological system ; Machine learning ; Statistics ; Physical chemistry Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Ying Wang | Bingbing Zhang | Zhihua Yang | Shilie Pan
Source: Angewandte Chemie (article, 2018-01-10)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Cation‐Tuned Synthesis of Fluorooxoborates: Towards Optimal Deep‐Ultraviolet Nonlinear Optical Materials The development of new nonlinear optical (NLO) materials for deep-ultraviolet (DUV) applications is in great demand. However, the synthesis of an ideal DUV NLO crystal is a serious challenge. Herein, three new alkali-metal fluorooxoborates, AB<sub>4</sub> O<sub>6</sub> F (A=K, Rb, and Cs, and a mixed cation between two of them), were successfully synthesized by cation regulation. It is found that all reported compounds exhibit short UV absorption edges (<190 nm), and show second harmonic generation (SHG) responses ranging from 0.8 to 1.9 KH<sub>2</sub> PO<sub>4</sub> (KDP). Interestingly, by judicious selection of the A-site alkali-metal cations, the arrangement of NLO-active structural units is fine-tuned to an optimal configuration, which contributes to large SHG responses. Ultraviolet ; Alkali metal ; Second-harmonic generation ; Nonlinear optical ; Nonlinear optics ; Chemistry ; Absorption (acoustics) ; Ideal (ethics) ; Crystal (programming language) ; Metal ; Materials science ; Optoelectronics ; Nanotechnology ; Optics ; Nonlinear system ; Organic chemistry ; Physics ; Laser Crystal Structures and Properties ; Inorganic Fluorides and Related Compounds ; Layered Double Hydroxides Synthesis and Applications
Authors: Chien Ing Yeo | Kah Kooi Ooi | Edward R. T. Tiekink
Source: Molecules (review, 2018-06-11)
Field: Materials Science — Biomaterials

Gold-Based Medicine: A Paradigm Shift in Anti-Cancer Therapy? A new era of metal-based drugs started in the 1960s, heralded by the discovery of potent platinum-based complexes, commencing with cisplatin [(H₃N)₂PtCl₂], which are effective anti-cancer chemotherapeutic drugs. While clinical applications of gold-based drugs largely relate to the treatment of rheumatoid arthritis, attention has turned to the investigation of the efficacy of gold(I) and gold(III) compounds for anti-cancer applications. This review article provides an account of the latest research conducted during the last decade or so on the development of gold compounds and their potential activities against several cancers as well as a summary of possible mechanisms of action/biological targets. The promising activities and increasing knowledge of gold-based drug metabolism ensures that continued efforts will be made to develop gold-based anti-cancer agents. Paradigm shift ; Cancer ; Cancer Medicine ; Cancer therapy ; Medicine ; Internal medicine ; Philosophy ; Epistemology Nanoparticle-Based Drug Delivery
Authors: Wenkai Chen | Xiangyang Liu | Wei‐Hai Fang | Pavlo O. Dral | Ganglong Cui
Source: The Journal of Physical Chemistry Letters (article, 2018-11-07)
Field: Materials Science — Materials Chemistry

Deep Learning for Nonadiabatic Excited-State Dynamics In this work we show that deep learning (DL) can be used for exploring complex and highly nonlinear multistate potential energy surfaces of polyatomic molecules and related nonadiabatic dynamics. Our DL is based on deep neural networks (DNNs), which are used as accurate representations of the CASSCF ground- and excited-state potential energy surfaces (PESs) of CH<sub>2</sub>NH. After geometries near conical intersection are included in the training set, the DNN models accurately reproduce excited-state topological structures; photoisomerization paths; and, importantly, conical intersections. We have also demonstrated that the results from nonadiabatic dynamics run with the DNN models are very close to those from the dynamics run with the pure ab initio method. The present work should encourage further studies of using machine learning methods to explore excited-state potential energy surfaces and nonadiabatic dynamics of polyatomic molecules. Conical intersection ; Excited state ; Polyatomic ion ; Potential energy ; Diabatic ; Ab initio ; Conical surface ; Potential energy surface ; Work (physics) ; Ground state ; Molecular dynamics ; Physics ; Statistical physics ; Molecule ; Atomic physics ; Quantum mechanics ; Mathematics ; Geometry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Spectroscopy and Quantum Chemical Studies
Authors: Thuong Nguyen | Eszter Székely | Giulio Imbalzano | Jörg Behler | Gábor Cśanyi | Michele Ceriotti | Andreas W. Götz | Francesco Paesani
Source: Open MIND (article, 2018-06-28)
Field: Materials Science — Materials Chemistry

Comparison of permutationally invariant polynomials, neural networks, and Gaussian approximation potentials in representing water interactions through many-body expansions. The accurate representation of multidimensional potential energy surfaces is a necessary requirement for realistic computer simulations of molecular systems. The continued increase in computer power accompanied by advances in correlated electronic structure methods nowadays enables routine calculations of accurate interaction energies for small systems, which can then be used as references for the development of analytical potential energy functions (PEFs) rigorously derived from many-body (MB) expansions. Building on the accuracy of the MB-pol many-body PEF, we investigate here the performance of permutationally invariant polynomials (PIPs), neural networks, and Gaussian approximation potentials (GAPs) in representing water two-body and three-body interaction energies, denoting the resulting potentials PIP-MB-pol, Behler-Parrinello neural network-MB-pol, and GAP-MB-pol, respectively. Our analysis shows that all three analytical representations exhibit similar levels of accuracy in reproducing both two-body and three-body reference data as well as interaction energies of small water clusters obtained from calculations carried out at the coupled cluster level of theory, the current gold standard for chemical accuracy. These results demonstrate the synergy between interatomic potentials formulated in terms of a many-body expansion, such as MB-pol, that are physically sound and transferable, and machine-learning techniques that provide a flexible framework to approximate the short-range interaction energy terms. Invariant (physics) ; Gaussian ; Artificial neural network ; Statistical physics ; Representation (politics) ; Range (aeronautics) ; Energy (signal processing) ; Computer science ; Physics ; Artificial intelligence ; Quantum mechanics ; Materials science Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Spectroscopy and Quantum Chemical Studies
Authors: Linfeng Zhang | Jiequn Han | Han Wang | Roberto Car | Weinan E
Source: The Journal of Chemical Physics (article, 2018-07-16)
Field: Materials Science — Materials Chemistry

DeePCG: Constructing coarse-grained models via deep neural networks We introduce a general framework for constructing coarse-grained potential models without ad hoc approximations such as limiting the potential to two- and/or three-body contributions. The scheme, called the Deep Coarse-Grained Potential (abbreviated DeePCG), exploits a carefully crafted neural network to construct a many-body coarse-grained potential. The network is trained with full atomistic data in a way that preserves the natural symmetries of the system. The resulting model is very accurate and can be used to sample the configurations of the coarse-grained variables in a much faster way than with the original atomistic model. As an application, we consider liquid water and use the oxygen coordinates as the coarse-grained variables, starting from a full atomistic simulation of this system at the ab initio molecular dynamics level. We find that the two-body, three-body, and higher-order oxygen correlation functions produced by the coarse-grained and full atomistic models agree very well with each other, illustrating the effectiveness of the DeePCG model on a rather challenging task. Artificial neural network ; Construct (python library) ; Limiting ; Ab initio ; Molecular dynamics ; Network model ; Exploit ; Work (physics) Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Quantum many-body systems
Authors: Daniele Padula | Jack D. Simpson | Alessandro Troisi
Source: Materials Horizons (article, 2018-10-30)
Field: Materials Science — Materials Chemistry

Combining electronic and structural features in machine learning models to predict organic solar cells properties Combining electronic and structural similarity between organic donors in kernel based machine learning methods allows to predict photovoltaic efficiencies reliably. Organic solar cell ; Similarity (geometry) ; Photovoltaic system ; Artificial intelligence ; Kernel (algebra) ; Materials science ; Machine learning ; Computer science ; Biological system ; Pattern recognition (psychology) ; Engineering ; Mathematics ; Electrical engineering Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Chalcogenide Semiconductor Thin Films
Authors: Xiu Jin | Jie Lu | Shuai Wang | Hai Qi | Shao Li
Source: Remote Sensing (article, 2018-03-04)
Field: Chemistry — Analytical Chemistry

Classifying Wheat Hyperspectral Pixels of Healthy Heads and Fusarium Head Blight Disease Using a Deep Neural Network in the Wild Field Classification of healthy and diseased wheat heads in a rapid and non-destructive manner for the early diagnosis of Fusarium head blight disease research is difficult. Our work applies a deep neural network classification algorithm to the pixels of hyperspectral image to accurately discern the disease area. The spectra of hyperspectral image pixels in a manually selected region of interest are preprocessed via mean removal to eliminate interference, due to the time interval and the environment. The generalization of the classification model is considered, and two improvements are made to the model framework. First, the pixel spectra data are reshaped into a two-dimensional data structure for the input layer of a Convolutional Neural Network (CNN). After training two types of CNNs, the assessment shows that a two-dimensional CNN model is more efficient than a one-dimensional CNN. Second, a hybrid neural network with a convolutional layer and bidirectional recurrent layer is reconstructed to improve the generalization of the model. When considering the characteristics of the dataset and models, the confusion matrices that are based on the testing dataset indicate that the classification model is effective for background and disease classification of hyperspectral image pixels. The results of the model show that the two-dimensional convolutional bidirectional gated recurrent unit neural network (2D-CNN-BidGRU) has an F1 score and accuracy of 0.75 and 0.743, respectively, for the total testing dataset. A comparison of all the models shows that the hybrid neural network of 2D-CNN-BidGRU is the best at preventing over-fitting and optimize the generalization. Our results illustrate that the hybrid structure deep neural network is an excellent classification algorithm for healthy and Fusarium head blight diseased classification in the field of hyperspectral imagery. Hyperspectral imaging ; Convolutional neural network ; Pixel ; Computer science ; Artificial intelligence ; Pattern recognition (psychology) ; Artificial neural network ; Deep learning ; Generalization ; Mathematics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Remote-Sensing Image Classification
Authors: Jerome M. Fox | Mengxia Zhao | M. Fink | Kyungtae Kang | George M. Whitesides
Source: Annual Review of Biophysics (review, 2018-03-05)
Field: Chemistry — Physical and Theoretical Chemistry

The Molecular Origin of Enthalpy/Entropy Compensation in Biomolecular Recognition Biomolecular recognition can be stubborn; changes in the structures of associating molecules, or the environments in which they associate, often yield compensating changes in enthalpies and entropies of binding and no net change in affinities. This phenomenon-termed enthalpy/entropy (H/S) compensation-hinders efforts in biomolecular design, and its incidence-often a surprise to experimentalists-makes interactions between biomolecules difficult to predict. Although characterizing H/S compensation requires experimental care, it is unquestionably a real phenomenon that has, from an engineering perspective, useful physical origins. Studying H/S compensation can help illuminate the still-murky roles of water and dynamics in biomolecular recognition and self-assembly. This review summarizes known sources of H/ S compensation (real and perceived) and lays out a conceptual framework for understanding and dissecting-and, perhaps, avoiding or exploiting-this phenomenon in biophysical systems. Phenomenon ; Biomolecule ; Surprise ; Entropy (arrow of time) ; Statistical physics ; Compensation (psychology) ; Enthalpy ; Nanotechnology ; Computer science ; Physics ; Thermodynamics ; Materials science ; Psychology ; Communication thermodynamics and calorimetric analyses ; Computational Drug Discovery Methods ; Origins and Evolution of Life
Authors: Michela F. Picardi | Anatoly V. Zayats | Francisco J. Rodríguez‐Fortuño
Source: Physical Review Letters (article, 2018-03-15)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Janus and Huygens Dipoles: Near-Field Directionality Beyond Spin-Momentum Locking Unidirectional scattering from circularly polarized dipoles has been demonstrated in near-field optics, where the quantum spin-Hall effect of light translates into spin-momentum locking. By considering the whole electromagnetic field, instead of its spin component alone, near-field directionality can be achieved beyond spin-momentum locking. This unveils the existence of the Janus dipole, with side-dependent topologically protected coupling to waveguides, and reveals the near-field directionality of Huygens dipoles, generalizing Kerker's condition. Circular dipoles, together with Huygens and Janus sources, form the complete set of all possible directional dipolar sources in the far- and near-field. This allows the designing of directional emission, scattering, and waveguiding, fundamental for quantum optical technology, integrated nanophotonics, and new metasurface designs. Physics ; Dipole ; Spin (aerodynamics) ; Field (mathematics) ; Momentum (technical analysis) ; Nanophotonics ; Scattering ; Janus ; Quantum optics ; Discrete dipole approximation ; Optics ; Quantum electrodynamics ; Quantum mechanics Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Orbital Angular Momentum in Optics
Authors: Patrick Bleiziffer | Kay Schaller | Sereina Riniker
Source: Journal of Chemical Information and Modeling (article, 2018-02-20)
Field: Materials Science — Materials Chemistry

Machine Learning of Partial Charges Derived from High-Quality Quantum-Mechanical Calculations Parametrization of small organic molecules for classical molecular dynamics simulations is not trivial. The vastness of the chemical space makes approaches using building blocks challenging. The most common approach is therefore an individual parametrization of each compound by deriving partial charges from semiempirical or ab initio calculations and inheriting the bonded and van der Waals (Lennard-Jones) parameters from a (bio)molecular force field. The quality of the partial charges generated in this fashion depends on the level of the quantum-chemical calculation as well as on the extraction procedure used. Here, we present a machine learning (ML) based approach for predicting partial charges extracted from density functional theory (DFT) electron densities. The training set was chosen with the goal to provide a broad coverage of the known chemical space of druglike molecules. In addition to the speed of the approach, the partial charges predicted by ML are not dependent on the three-dimensional conformation in contrast to the ones obtained by fitting to the electrostatic potential (ESP). To assess the quality and compatibility with standard force fields, we performed benchmark calculations for the free energy of hydration and liquid properties such as density and heat of vaporization. Partial charge ; Force field (fiction) ; Ab initio ; van der Waals force ; Density functional theory ; Parametrization (atmospheric modeling) ; Chemistry ; Molecule ; Molecular dynamics ; Computational chemistry ; Statistical physics ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Petronela Pascariu | Ioan Valentin Tudose | Mirela Petruţa Şuchea | E. Koudoumas | Nicușor Fifere | Anton Airinei
Source: Applied Surface Science (article, 2018-04-16)
Field: Materials Science — Materials Chemistry

Preparation and characterization of Ni, Co doped ZnO nanoparticles for photocatalytic applications Photocatalysis ; Materials science ; Rhodamine B ; Wurtzite crystal structure ; Photoluminescence ; Nanoparticle ; Dopant ; Doping ; Scanning electron microscope ; Visible spectrum ; Transmission electron microscopy ; Nanomaterials ; Analytical Chemistry (journal) ; Chemical engineering ; Nuclear chemistry ; Nanotechnology ; Zinc ; Optoelectronics ; Catalysis ; Metallurgy ; Chemistry ; Composite material ZnO doping and properties ; Gas Sensing Nanomaterials and Sensors ; Copper-based nanomaterials and applications
Authors: Deping Hu | Yu Xie | Xusong Li | Lingyue Li | Zhenggang Lan
Source: The Journal of Physical Chemistry Letters (article, 2018-05-07)
Field: Materials Science — Materials Chemistry

Inclusion of Machine Learning Kernel Ridge Regression Potential Energy Surfaces in On-the-Fly Nonadiabatic Molecular Dynamics Simulation We discuss a theoretical approach that employs machine learning potential energy surfaces (ML-PESs) in the nonadiabatic dynamics simulation of polyatomic systems by taking 6-aminopyrimidine as a typical example. The Zhu-Nakamura theory is employed in the surface hopping dynamics, which does not require the calculation of the nonadiabatic coupling vectors. The kernel ridge regression is used in the construction of the adiabatic PESs. In the nonadiabatic dynamics simulation, we use ML-PESs for most geometries and switch back to the electronic structure calculations for a few geometries either near the S<sub>1</sub>/S<sub>0</sub> conical intersections or in the out-of-confidence regions. The dynamics results based on ML-PESs are consistent with those based on CASSCF PESs. The ML-PESs are further used to achieve the highly efficient massive dynamics simulations with a large number of trajectories. This work displays the powerful role of ML methods in the nonadiabatic dynamics simulation of polyatomic systems. Ridge ; Molecular dynamics ; Kernel (algebra) ; On the fly ; Inclusion (mineral) ; Dynamics (music) ; Regression ; Energy (signal processing) ; Statistical physics ; Computer science ; Artificial intelligence ; Machine learning ; Physics ; Computational chemistry ; Mathematics ; Chemistry ; Geology ; Statistics ; Quantum mechanics ; Pure mathematics ; Acoustics ; Thermodynamics ; Paleontology Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Spectroscopy and Quantum Chemical Studies