jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Weike Ye | Chi Chen | Zhenbin Wang | Iek‐Heng Chu | Shyue Ping Ong
Source:  (article, 2018-09-18)
Field: Materials Science — Materials Chemistry

Deep neural networks for accurate predictions of crystal stability. Predicting the stability of crystals is one of the central problems in materials science. Today, density functional theory (DFT) calculations remain comparatively expensive and scale poorly with system size. Here we show that deep neural networks utilizing just two descriptors-the Pauling electronegativity and ionic radii-can predict the DFT formation energies of C3A2D3O12 garnets and ABO3 perovskites with low mean absolute errors (MAEs) of 7-10 meV atom-1 and 20-34 meV atom-1, respectively, well within the limits of DFT accuracy. Further extension to mixed garnets and perovskites with little loss in accuracy can be achieved using a binary encoding scheme, addressing a critical gap in the extension of machine-learning models from fixed stoichiometry crystals to infinite universe of mixed-species crystals. Finally, we demonstrate the potential of these models to rapidly transverse vast chemical spaces to accurately identify stable compositions, accelerating the discovery of novel materials with potentially superior properties. Electronegativity ; Density functional theory ; Stability (learning theory) ; Computer science ; Curse of dimensionality ; Crystal structure prediction ; Artificial neural network ; Crystal (programming language) ; Atom (system on chip) ; Artificial intelligence ; Algorithm ; Statistical physics ; Materials science ; Machine learning ; Computational chemistry ; Crystal structure ; Physics ; Chemistry ; Quantum mechanics ; Crystallography Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Daniel C. Elton | Zois Boukouvalas | Mark S. Butrico | Mark D. Fuge | Peter W. Chung
Source: Scientific Reports (article, 2018-06-07)
Field: Materials Science — Materials Chemistry

Applying machine learning techniques to predict the properties of energetic materials We present a proof of concept that machine learning techniques can be used to predict the properties of CNOHF energetic molecules from their molecular structures. We focus on a small but diverse dataset consisting of 109 molecular structures spread across ten compound classes. Up until now, candidate molecules for energetic materials have been screened using predictions from expensive quantum simulations and thermochemical codes. We present a comprehensive comparison of machine learning models and several molecular featurization methods - sum over bonds, custom descriptors, Coulomb matrices, Bag of Bonds, and fingerprints. The best featurization was sum over bonds (bond counting), and the best model was kernel ridge regression. Despite having a small data set, we obtain acceptable errors and Pearson correlations for the prediction of detonation pressure, detonation velocity, explosive energy, heat of formation, density, and other properties out of sample. By including another dataset with ≈300 additional molecules in our training we show how the error can be pushed lower, although the convergence with number of molecules is slow. Our work paves the way for future applications of machine learning in this domain, including automated lead generation and interpreting machine learning models to obtain novel chemical insights. Detonation ; Convergence (economics) ; Explosive material ; Kernel (algebra) ; Work (physics) ; Focus (optics) ; Coulomb explosion ; Experimental data Machine Learning in Materials Science ; Energetic Materials and Combustion ; Computational Drug Discovery Methods
Authors: Andrew J. Medford | M. Ross Kunz | Sarah Ewing | Tammie L. Borders | R Fushimi
Source: ACS Catalysis (article, 2018-06-22)
Field: Materials Science — Materials Chemistry

Extracting Knowledge from Data through Catalysis Informatics Catalysis informatics is a distinct subfield that lies at the intersection of cheminformatics and materials informatics but with distinctive challenges arising from the dynamic, surface-sensitive, and multiscale nature of heterogeneous catalysis. The ideas behind catalysis informatics can be traced back decades, but the field is only recently emerging due to advances in data infrastructure, statistics, machine learning, and computational methods. In this work, we review the field from early works on expert systems and knowledge engines to more recent approaches utilizing machine-learning and uncertainty quantification. The data–information–knowledge hierarchy is introduced and used to classify various developments. The chemical master equation and microkinetic models are proposed as a quantitative representation of catalysis knowledge, which can be used to generate explanative and predictive hypotheses for the understanding and discovery of catalytic materials. We discuss future prospects for the field, including improved quantitative coupling of experiment/theory, advanced microkinetic models, and the development of open-source software tools. Ultimately, integration of existing chemical and physical models with emerging statistical and computational tools presents a promising route toward the automated design, discovery, and optimization of heterogeneous catalytic processes. Cheminformatics ; Computer science ; Data science ; Field (mathematics) ; Hierarchy ; Informatics ; Materials informatics ; Intersection (aeronautics) ; Health informatics ; Management science ; Artificial intelligence ; Engineering informatics ; Chemistry ; Engineering ; Computational chemistry ; Mathematics Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalytic Processes in Materials Science
Authors: Jiequn Han | Linfeng Zhang | Roberto Car null | Weinan E
Source: Communications in Computational Physics (article, 2018-06-01)
Field: Materials Science — Materials Chemistry

Deep Potential: A General Representation of a Many-Body Potential Energy Surface We present a simple, yet general, end-to-end deep neural network representation of the potential energy surface for atomic and molecular systems. This methodology, which we call Deep Potential, is "first-principle" based, in the sense that no ad hoc approximations or empirical fitting functions are required. The neural network structure naturally respects the underlying symmetries of the systems. When tested on a wide variety of examples, Deep Potential is able to reproduce the original model, whether empirical or quantum mechanics based, within chemical accuracy. The computational cost of this new model is not substantially larger than that of empirical force fields. In addition, the method has promising scalability properties. This brings us one step closer to being able to carry out molecular simulations with accuracy comparable to that of quantum mechanics models and computational cost comparable to that of empirical potentials. Representation (politics) ; Homogeneous space ; Surface (topology) ; Quantum ; Artificial neural network ; Energy (signal processing) ; Potential energy Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions ; Quantum many-body systems
Authors: Wei Li | Ryan Jacobs | Dane Morgan
Source: Computational Materials Science (article, 2018-04-25)
Field: Materials Science — Materials Chemistry

Predicting the thermodynamic stability of perovskite oxides using machine learning models Mean squared error ; Stability (learning theory) ; Perovskite (structure) ; Density functional theory ; Machine learning ; Phase (matter) ; Atom (system on chip) ; Convex hull ; Materials science ; Computer science ; Algorithm ; Chemistry ; Mathematics ; Computational chemistry ; Regular polygon ; Statistics ; Crystallography Machine Learning in Materials Science ; Advancements in Solid Oxide Fuel Cells ; Electrocatalysts for Energy Conversion
Authors: Harinarayanan Puliyalil | Damjan Lašič Jurković | Venkata D. B. C. Dasireddy | Blaž Likozar
Source: RSC Advances (review, 2018-01-01)
Field: Materials Science — Materials Chemistry

A review of plasma-assisted catalytic conversion of gaseous carbon dioxide and methane into value-added platform chemicals and fuels CO<sub>2</sub> and CH<sub>4</sub> contribute to greenhouse gas emissions, while the production of industrial base chemicals from natural gas resources is emerging as well. Such conversion processes, however, are energy-intensive and introducing a renewable and sustainable electric activation seems optimal, at least for intermediate-scale modular operation. The review thus analyses such valorisation by plasma reactor technologies and heterogeneous catalysis application, largely into higher hydrocarbon molecules, that is ethane, ethylene, acetylene, propane, <i>etc.</i>, and organic oxygenated compounds, <i>i.e.</i> methanol, formaldehyde, formic acid and dimethyl ether. Focus is given to reaction pathway mechanisms, related to the partial oxidation steps of CH<sub>4</sub> with O<sub>2</sub>, H<sub>2</sub>O and CO<sub>2</sub>, CO<sub>2</sub> reduction with H<sub>2</sub>, CH<sub>4</sub> or other paraffin species, and to a lesser extent, to mixtures' dry reforming to syngas. Dielectric barrier discharge, corona, spark and gliding arc sources are considered, combined with (noble) metal materials. Carbon (C), silica (SiO<sub>2</sub>) and alumina (Al<sub>2</sub>O<sub>3</sub>) as well as various catalytic supports are examined as precious critical raw materials (<i>e.g.</i> platinum, palladium and rhodium) or transition metal (<i>e.g.</i> manganese, iron, cobalt, nickel and copper) substrates. These are applied for turnover, such as that pertinent to reformer, (reverse) water-gas shift (WGS or RWGS) and CH<sub>3</sub>OH synthesis. Time-on-stream catalyst deactivation or reactivation is also overviewed from the viewpoint of individual transient moieties and their adsorption or desorption characteristics, as well as reactivity. Methane ; Carbon dioxide ; Carbon dioxide reforming ; Plasma ; Value (mathematics) ; Catalysis ; Waste management ; Environmental chemistry ; Chemical engineering ; Environmental science ; Chemistry ; Syngas ; Pulp and paper industry ; Organic chemistry ; Computer science ; Engineering ; Physics Catalytic Processes in Materials Science ; CO2 Reduction Techniques and Catalysts ; Plasma Applications and Diagnostics
Authors: Zijiang Yang | Xiaolin Li | L. Catherine Brinson | Alok Choudhary | Wei Chen | Ankit Agrawal
Source: Journal of Mechanical Design (article, 2018-09-03)
Field: Materials Science — Materials Chemistry

Microstructural Materials Design Via Deep Adversarial Learning Methodology Identifying the key microstructure representations is crucial for computational materials design (CMD). However, existing microstructure characterization and reconstruction (MCR) techniques have limitations to be applied for microstructural materials design. Some MCR approaches are not applicable for microstructural materials design because no parameters are available to serve as design variables, while others introduce significant information loss in either microstructure representation and/or dimensionality reduction. In this work, we present a deep adversarial learning methodology that overcomes the limitations of existing MCR techniques. In the proposed methodology, generative adversarial networks (GAN) are trained to learn the mapping between latent variables and microstructures. Thereafter, the low-dimensional latent variables serve as design variables, and a Bayesian optimization framework is applied to obtain microstructures with desired material property. Due to the special design of the network architecture, the proposed methodology is able to identify the latent (design) variables with desired dimensionality, as well as capturing complex material microstructural characteristics. The validity of the proposed methodology is tested numerically on a synthetic microstructure dataset and its effectiveness for microstructural materials design is evaluated through a case study of optimizing optical performance for energy absorption. Additional features, such as scalability and transferability, are also demonstrated in this work. In essence, the proposed methodology provides an end-to-end solution for microstructural materials design, in which GAN reduces information loss and preserves more microstructural characteristics, and the GP-Hedge optimization improves the efficiency of design exploration. Computer science ; Curse of dimensionality ; Generative Design ; Scalability ; Dimensionality reduction ; Machine learning ; Artificial intelligence ; Materials science Machine Learning in Materials Science ; Injection Molding Process and Properties ; Generative Adversarial Networks and Image Synthesis
Authors: Kai‐Yao Huang | Tzong-Yi Lee | Hui-Ju Kao | Chen-Tse Ma | Chao-Chun Lee | Tsai-Hsuan Lin | Wen‐Chi Chang | Hsien‐Da Huang
Source: Nucleic Acids Research (article, 2018-10-19)
Field: Chemistry — Spectroscopy

dbPTM in 2019: exploring disease association and cross-talk of post-translational modifications The dbPTM (http://dbPTM.mbc.nctu.edu.tw/) has been maintained for over 10 years with the aim to provide functional and structural analyses for posttranslational modifications (PTMs). In this update, dbPTM not only integrates more experimentally validated PTMs from available databases and through manual curation of literature but also provides PTMdisease associations based on non-synonymous single nucleotide polymorphisms (nsSNPs). The high-throughput deep sequencing technology has led to a surge in the data generated through analysis of association between SNPs and diseases, both in terms of growth amount and scope. This update thus integrated disease-associated nsSNPs from db-SNP based on genome-wide association studies. The PTM substrate sites located at a specified distance in terms of the amino acids encoded from nsSNPs were deemed to have an association with the involved diseases. In recent years, increasing evidence for crosstalk between PTMs has been reported. Although mass spectrometry-based proteomics has substantially improved our knowledge about substrate site specificity of single PTMs, the fact that the crosstalk of combinatorial PTMs may act in concert with the regulation of protein function and activity is neglected. Because of the relatively limited information about concurrent frequency and functional relevance of PTM crosstalk, in this update, the PTM sites neighboring other PTM sites in a specified window length were subjected to motif discovery and functional enrichment analysis. This update highlights the current challenges in PTM crosstalk investigation and breaks the bottleneck of how proteomics may contribute to understanding PTM codes, revealing the next level of data complexity and proteomic limitation in prospective PTM research. Biology ; Crosstalk ; Computational biology ; Single-nucleotide polymorphism ; Proteomics ; Bottleneck ; Genetics ; dbSNP ; Genetic association ; Genome-wide association study ; Bioinformatics ; Computer science ; Gene ; Genotype Advanced Proteomics Techniques and Applications ; Ubiquitin and proteasome pathways ; Peptidase Inhibition and Analysis
Authors: Alexander Ryabchun | Alexey Bobrovsky
Source: Advanced Optical Materials (article, 2018-06-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Cholesteric Liquid Crystal Materials for Tunable Diffractive Optics Abstract Modern optics and photonics constantly require break‐through materials and designs in order to achieve miniature, lightweight, highly tunable, and effective optical devices. One of the basic optical components is the diffraction grating (DG), widely used for the dispersion of light, beam steering, etc. This review gathers research efforts on diffractive optical elements based on cholesteric liquid crystal (CLC) materials with a supramolecular helical architecture. All main types and fabrication approaches of periodic diffractive structures from CLCs are classified and described. Key optical properties of DGs, their advantages and drawbacks are considered. Special attention is paid on the tunability of DGs including design principles and prospective chiral materials. The review consists of three parts divided according to the formation mechanism of diffractive structures: i) the spontaneously formed periodic structures from CLCs confined in cells with hybrid or homeotropic boundary conditions; ii) DGs generated by external electric field applied to CLCs layers; iii) light‐generated DGs (e.g., obtained by holography, mask exposure, photoalignment). The review also aims to initiate and gain collaborations between physicists, engineers and organic chemists to combine novel chiral photoswitches and molecular motors with sophisticated optical design paving the way towards novel smart optical materials. Materials science ; Homeotropic alignment ; Liquid crystal ; Fabrication ; Diffraction grating ; Optoelectronics ; Holography ; Photonics ; Optics ; Optical switch ; Grating ; Diffraction ; Cholesteric liquid crystal ; Smart material ; Nanotechnology ; Physics Liquid Crystal Research Advancements ; Photochromic and Fluorescence Chemistry ; Advanced Materials and Mechanics
Authors: Lukáš Grajciar | Christopher J. Heard | Anton A. Bondarenko | Mikhail V. Polynski | Jittima Meeprasert | Evgeny A. Pidko | Petr Nachtigall
Source: Chemical Society Reviews (review, 2018-01-01)
Field: Materials Science — Materials Chemistry

Towards<i>operando</i>computational modeling in heterogeneous catalysis An increased synergy between experimental and theoretical investigations in heterogeneous catalysis has become apparent during the last decade. Experimental work has extended from ultra-high vacuum and low temperature towards operando conditions. These developments have motivated the computational community to move from standard descriptive computational models, based on inspection of the potential energy surface at 0 K and low reactant concentrations (0 K/UHV model), to more realistic conditions. The transition from 0 K/UHV to operando models has been backed by significant developments in computer hardware and software over the past few decades. New methodological developments, designed to overcome part of the gap between 0 K/UHV and operando conditions, include (i) global optimization techniques, (ii) ab initio constrained thermodynamics, (iii) biased molecular dynamics, (iv) microkinetic models of reaction networks and (v) machine learning approaches. The importance of the transition is highlighted by discussing how the molecular level picture of catalytic sites and the associated reaction mechanisms changes when the chemical environment, pressure and temperature effects are correctly accounted for in molecular simulations. It is the purpose of this review to discuss each method on an equal footing, and to draw connections between methods, particularly where they may be applied in combination. Catalysis ; Molecular dynamics ; Ab initio ; Work (physics) ; Chemistry ; Heterogeneous catalysis ; Nanotechnology ; Computer science ; Computational chemistry ; Thermodynamics ; Materials science ; Physics Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Advanced Chemical Physics Studies
Authors: Marina R. Filip | Feliciano Giustino
Source: Proceedings of the National Academy of Sciences (article, 2018-05-07)
Field: Materials Science — Materials Chemistry

The geometric blueprint of perovskites Perovskite minerals form an essential component of the Earth's mantle, and synthetic crystals are ubiquitous in electronics, photonics, and energy technology. The extraordinary chemical diversity of these crystals raises the question of how many and which perovskites are yet to be discovered. Here we show that the "no-rattling" principle postulated by Goldschmidt in 1926, describing the geometric conditions under which a perovskite can form, is much more effective than previously thought and allows us to predict perovskites with a fidelity of 80%. By supplementing this principle with inferential statistics and internet data mining we establish that currently known perovskites are only the tip of the iceberg, and we enumerate 90,000 hitherto-unknown compounds awaiting to be studied. Our results suggest that geometric blueprints may enable the systematic screening of millions of compounds and offer untapped opportunities in structure prediction and materials design. Blueprint ; Perovskite (structure) ; Fidelity ; Nanotechnology ; Data science ; Component (thermodynamics) ; Computer science ; Chemistry ; Materials science ; Physics ; Engineering ; Mechanical engineering ; Telecommunications ; Crystallography Machine Learning in Materials Science ; Perovskite Materials and Applications ; Crystal Structures and Properties
Authors: Laura J. Fox | Robert M. Richardson | Wuge H. Briscoe
Source: Advances in Colloid and Interface Science (review, 2018-06-27)
Field: Materials Science — Polymers and Plastics

PAMAM dendrimer - cell membrane interactions PAMAM dendrimers have been conjectured for a wide range of biomedical applications due to their tuneable physicochemical properties. However, their application has been hindered by uncertainties in their cytotoxicity, which is influenced by dendrimer generation (i.e. size and surface group density), surface chemistry, and dosage, as well as cell specificity. In this review, biomedical applications of polyamidoamine (PAMAM) dendrimers and some related cytotoxicity studies are first outlined. Alongside these in vitro experiments, lipid membranes such as supported lipid bilayers (SLBs), liposomes, and Langmuir monolayers have been used as cell membrane models to study PAMAM dendrimer-membrane interactions. Related experimental and theoretical studies are summarized, and the physical insights from these studies are discussed to shed light on the fundamental understanding of PAMAM dendrimer-cell membrane interactions. We conclude with a summary of some questions that call for further investigations. Dendrimer ; Membrane ; Lipid bilayer ; Liposome ; Cytotoxicity ; Monolayer ; Biophysics ; Nanotechnology ; Cell membrane ; Chemistry ; Materials science ; In vitro ; Polymer chemistry ; Biochemistry ; Biology Dendrimers and Hyperbranched Polymers ; RNA Interference and Gene Delivery ; Advanced biosensing and bioanalysis techniques