jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jake Burner | Ludwig Schwiedrzik | Mykhaylo Krykunov | Jun Luo | Peter G. Boyd | Tom K. Woo
Source: The Journal of Physical Chemistry C (article, 2020-12-09)
Field: Chemistry — Inorganic Chemistry

High-Performing Deep Learning Regression Models for Predicting Low-Pressure CO<sub>2</sub> Adsorption Properties of Metal–Organic Frameworks Metal–organic frameworks (MOFs) have garnered interest as potential solid sorbent materials for postcombustion CO2 capture. With a seemingly infinite design space, high-throughput computational screening of MOFs has developed into an effective tool for the development of new materials. In this work, machine learning (ML) has been used to develop accurate quantitative structure–property relationship (QSPR) models to rapidly predict the CO2 working capacity and CO2/N2 selectivity at the low-pressure conditions relevant to postcombustion carbon capture (0.15 bar CO2, 0.85 bar N2). A database of over 340 000 MOFs constructed from hundreds of types of building units arranged in over 1000 net topologies was used to train and test the models. Neural network ML models were optimized using six geometric descriptors along with three so-called chemical descriptors, namely, the atomic property-weighted radial distribution function (AP-RDF) and some variants thereof, the bag-of-atoms, and the chemical motif density descriptors. The ML models built using geometric descriptors alone resulted in test set correlation R2 values of only 0.71 and 0.75 for CO2 working capacity and CO2/N2 selectivity, respectively. ML models built with a single type of chemical descriptor all outperformed the geometry-only models giving R2 values ranging from 0.83 to 0.94 with the AP-RDF model being the most accurate. Overall, the best model was built using a combination of AP-RDF, chemical motif, and geometric descriptors (R2 = 0.96 when predicting the CO2 working capacity and R2 = 0.95 for the selectivity). To date, these are the most accurate ML models for predicting low-pressure gas uptake of MOFs. The combined model was able to capture 994 of the true top 1000 MOFs (from a test set of ∼70 000) within the top 5000 MOFs as predicted by the model with CO2 working capacity as the target. Thus, if the ML model were used to prescreen materials for more compute intensive GCMC simulations, then it would result in a greater than 10 times speed up while still capturing >99% of high-performing materials. These results highlight the importance of chemical descriptors in predicting low-pressure gas adsorption properties in nanoporous materials. Molecular descriptor ; Artificial neural network ; Selectivity ; Quantitative structure–activity relationship ; Artificial intelligence ; Test set ; Regression ; Machine learning ; Chemical space ; Adsorption ; Linear regression ; Chemistry ; Computer science ; Mathematics ; Biological system ; Statistics ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies ; Carbon dioxide utilization in catalysis
Authors: Zachary M. Sherman | Michael P. Howard | Beth A. Lindquist | Ryan B. Jadrich | Thomas M. Truskett
Source: The Journal of Chemical Physics (article, 2020-04-09)
Field: Materials Science — Materials Chemistry

Inverse methods for design of soft materials Functional soft materials, comprising colloidal and molecular building blocks that self-organize into complex structures as a result of their tunable interactions, enable a wide array of technological applications. Inverse methods provide a systematic means for navigating their inherently high-dimensional design spaces to create materials with targeted properties. While multiple physically motivated inverse strategies have been successfully implemented in silico, their translation to guiding experimental materials discovery has thus far been limited to a handful of proof-of-concept studies. In this perspective, we discuss recent advances in inverse methods for design of soft materials that address two challenges: (1) methodological limitations that prevent such approaches from satisfying design constraints and (2) computational challenges that limit the size and complexity of systems that can be addressed. Strategies that leverage machine learning have proven particularly effective, including methods to discover order parameters that characterize complex structural motifs and schemes to efficiently compute macroscopic properties from the underlying structure. We also highlight promising opportunities to improve the experimental realizability of materials designed computationally, including discovery of materials with functionality at multiple thermodynamic states, design of externally directed assembly protocols that are simple to implement in experiments, and strategies to improve the accuracy and computational efficiency of experimentally relevant models. Realizability ; Leverage (statistics) ; Soft materials ; Inverse ; Simple (philosophy) ; Limit (mathematics) ; Design methods ; Bridging (networking) Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Surface Chemistry and Catalysis
Authors: Wei Han | Hua Bao | Xiulin Ruan
Source: Nano Energy (article, 2020-02-15)
Field: Materials Science — Materials Chemistry

Genetic algorithm-driven discovery of unexpected thermal conductivity enhancement by disorder Materials science ; Thermal conductivity ; Genetic algorithm ; Conductivity ; Algorithm ; Nanotechnology ; Computer science ; Machine learning ; Composite material ; Physics Thermal properties of materials ; Advanced Thermoelectric Materials and Devices ; Thermography and Photoacoustic Techniques
Authors: Akira Takahashi | Yu Kumagai | Jun Miyamoto | Yasuhide Mochizuki | Fumiyasu Oba
Source: Physical Review Materials (article, 2020-10-09)
Field: Materials Science — Materials Chemistry

Machine learning models for predicting the dielectric constants of oxides based on high-throughput first-principles calculations Prediction models of both the electronic and ionic contributions to the static dielectric constants have been constructed using data from density functional perturbation theory calculations of approximately 1200 metal oxides via supervised machine learning. We developed two types of random forest regression models for oxides with the ground-state crystal structures: one model requires only compositional information and the other model also uses structural information. Although the training data included various atomic frameworks, the prediction models performed well even when only compositional information was used as feature descriptors. In prediction of the electronic contributions to the dielectric constants, the accuracies of the regression models with and without structural information were comparable, while the structural descriptors more clearly improved the prediction accuracy for the ionic contributions. We also analyzed the feature importance for prediction of the dielectric constants. The mean atomic mass and mass density were determined to be significant features in prediction of the electronic contributions without and with structural information, respectively. The standard deviation of the principal quantum number and mean neighbor distance variation were found to be important for the respective prediction models of the ionic contributions. The correlations between the dielectric constants and these features are discussed, along with the underlying physical mechanisms. Dielectric ; Materials science ; Ionic bonding ; Density functional theory ; Electronic structure ; Artificial intelligence ; Machine learning ; Statistical physics ; Computer science ; Computational chemistry ; Chemistry ; Physics ; Ion ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Catalysis and Oxidation Reactions
Authors: Min Zhu | Dan Huang | Xinjun Hu | Wenhua Tong | Baolin Han | Jianping Tian | Huibo Luo
Source: Food Science & Nutrition (review, 2020-09-03)
Field: Chemistry — Analytical Chemistry

Application of hyperspectral technology in detection of agricultural products and food: A Review Food is the foundation of human survival. With the development and progress of society, people increasingly focus on the problems of food quality and safety, which is closely related to human's health. Thus, the whole industrial chain from farmland to dining table need to be strictly controlled. Traditional detection methods are time-consuming, laborious, and destructive. In recent years, hyperspectral technology has been more and more applied to food safety and quality detection, because the technology can achieve rapid and nondestructive detection of food, and the requirement to experimental condition is low; operability is strong. In this paper, hyperspectral imaging technology was briefly introduced, and its application in agricultural products and food detection in recent years was systematically summarized, and the key points in the research process were deeply discussed. This work lays a solid foundation for the peers to the following in-depth research and application of this technology. Hyperspectral imaging ; Operability ; Agriculture ; Food safety ; Process (computing) ; Computer science ; Risk analysis (engineering) ; Quality (philosophy) ; Human health ; Biotechnology ; Business ; Artificial intelligence ; Geography ; Medicine ; Environmental health Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Identification and Quantification in Food
Authors: D. B. Albert | Bobby Antony | Yaye Awa Ba | Yuri L. Babikov | Philippe Bollard | Vincent Boudon | F. Delahaye | G. Del Zanna | M. S. Dimitrijević | Brian J. Drouin | Marie-Lise Dubernet | Felix Duensing | M. Emoto | Christian Endres | А. З. Фазлиев | Jean-Michel Glorian | Iouli E. Gordon | P. Gratier | C. Hill | Darko Jevremović | C. Joblin | Duck-Hee Kwon | Roman V. Kochanov | E. Krishnakumar | G. Leto | P. A. Loboda | A.A. Lukashevskaya | O.M. Lyulin | Bratislav P. Marinković | A. J. Markwick | T. Marquart | N. J. Mason | C. Mendoza | T. J. Millar | Nicolas Moreau | Serguei V. Morozov | T. Möller | H. S. P. Müller | G. Mulas | I. Murakami | Yu. V. Pakhomov | P. Palmeri | Julien Penguen | В. П. Перевалов | N. Piskunov | Johannes Postler | Alexey Privezentsev | P. Quinet | Yuri Ralchenko | Yongjoo Rhee | C. Richard | Guy Rixon | Laurence S. Rothman | E. Roueff | T. Ryabchikova | S. Sahal−Bréchot | P. Scheier | P. Schilke | Stephan Śchlemmer | K. Smith | B. Schmitt | I. Yu. Skobelev | Vladimir A. Srécković | Eric Stempels | S. A. Tashkun | Jonathan Tennyson | Vladimir G. Tyuterev | C. Vastel | Veljko Vujčić | Valentine Wakelam | N. A. Walton | C. J. Zeippen | Carlo Maria Zwölf
Source: Atoms (article, 2020-10-21)
Field: Chemistry — Spectroscopy

A Decade with VAMDC: Results and Ambitions This paper presents an overview of the current status of the Virtual Atomic and Molecular Data Centre (VAMDC) e-infrastructure, including the current status of the VAMDC-connected (or to be connected) databases, updates on the latest technological development within the infrastructure and a presentation of some application tools that make use of the VAMDC e-infrastructure. We analyse the past 10 years of VAMDC development and operation, and assess their impact both on the field of atomic and molecular (A&amp;M) physics itself and on heterogeneous data management in international cooperation. The highly sophisticated VAMDC infrastructure and the related databases developed over this long term make them a perfect resource of sustainable data for future applications in many fields of research. However, we also discuss the current limitations that prevent VAMDC from becoming the main publishing platform and the main source of A&amp;M data for user communities, and present possible solutions under investigation by the consortium. Several user application examples are presented, illustrating the benefits of VAMDC in current research applications, which often need the A&amp;M data from more than one database. Finally, we present our vision for the future of VAMDC. Computer science ; Presentation (obstetrics) ; Data science ; Field (mathematics) ; Resource (disambiguation) ; Data management ; Systems engineering ; World Wide Web ; Database ; Engineering Spectroscopy and Laser Applications ; Atmospheric Ozone and Climate ; Scientific Computing and Data Management
Authors: Airan Li | Chenguang Fu | Xinbing Zhao | Tiejun Zhu
Source: Research (review, 2020-01-01)
Field: Materials Science — Materials Chemistry

High-Performance Mg <sub>3</sub> Sb <sub> 2- <i>x</i> </sub> Bi <i> <sub>x</sub> </i> Thermoelectrics: Progress and Perspective Since the first successful implementation of <i>n</i>-type doping, low-cost Mg<sub>3</sub>Sb<sub>2-<i>x</i></sub> Bi <i><sub>x</sub></i> alloys have been rapidly developed as excellent thermoelectric materials in recent years. An average figure of merit <i>zT</i> above unity over the temperature range 300-700 K makes this new system become a promising alternative to the commercially used <i>n</i>-type Bi<sub>2</sub>Te<sub>3-<i>x</i></sub> Se <i><sub>x</sub></i> alloys for either refrigeration or low-grade heat power generation near room temperature. In this review, with the structure-property-application relationship as the mainline, we first discuss how the crystallographic, electronic, and phononic structures lay the foundation of the high thermoelectric performance. Then, optimization strategies, including the physical aspects of band engineering with Sb/Bi alloying and carrier scattering mechanism with grain boundary modification and the chemical aspects of Mg defects and aliovalent doping, are extensively reviewed. Mainstream directions targeting the improvement of <i>zT</i> near room temperature are outlined. Finally, device applications and related engineering issues are discussed. We hope this review could help to promote the understanding and future developments of low-cost Mg<sub>3</sub>Sb<sub>2-<i>x</i></sub> Bi <i><sub>x</sub></i> alloys for practical thermoelectric applications. Thermoelectric effect ; Materials science ; Doping ; Thermoelectric materials ; Figure of merit ; Bismuth ; Engineering physics ; Condensed matter physics ; Computer science ; Thermodynamics ; Physics ; Optoelectronics ; Metallurgy Advanced Thermoelectric Materials and Devices ; Thermal Expansion and Ionic Conductivity ; Heusler alloys: electronic and magnetic properties
Authors: Atanu Patra | Lukas J. Patalag | Peter G. Jones | Daniel B. Werz
Source: Angewandte Chemie International Edition (article, 2020-10-06)
Field: Materials Science — Materials Chemistry

Extended Benzene‐Fused Oligo‐BODIPYs: In Three Steps to a Series of Large, Arc‐Shaped, Near‐Infrared Dyes We present a straightforward, three-step synthesis engaging an oligomerization and subsequent one-pot oxidation step to form fully conjugated, benzene-fused oligo-BODIPYs from simple BODIPY precursors. FeCl<sub>3</sub> serves as an efficient, bifunctional oxidant for a (multiple) cyclization/desaturation process, applied to ethylene-bridged dimeric, trimeric and oligomeric species to transform linking ethano units into stiff benzene fusions between unsubstituted β-positions of each BODIPY unit. The structural integrity was verified by X-ray crystallography, and all target compounds were studied in detail by photophysical, electrochemical and computational means. The main S<sub>1</sub> excited state gradually converges to a structure-specific excitation limit, displaying a strong shift of the absorption event from about 500 nm (BODIPY monomer) to 955 nm (octamer) with attenuation coefficients up to ca. 500 000 M<sup>-1</sup> cm<sup>-1</sup> . BODIPY ; Chemistry ; Photochemistry ; Benzene ; Monomer ; Bifunctional ; Excited state ; Oligomer ; Infrared ; Molecule ; Fluorescence ; Polymer chemistry ; Polymer ; Organic chemistry ; Optics Luminescence and Fluorescent Materials ; Porphyrin and Phthalocyanine Chemistry ; Nanoplatforms for cancer theranostics
Authors: Mingjian Wen | Samuel M. Blau | Evan Walter Clark Spotte‐Smith | Shyam Dwaraknath | Kristin A. Persson
Source: Chemical Science (article, 2020-12-09)
Field: Materials Science — Materials Chemistry

BonDNet: a graph neural network for the prediction of bond dissociation energies for charged molecules A broad collection of technologies, including <i>e.g.</i> drug metabolism, biofuel combustion, photochemical decontamination of water, and interfacial passivation in energy production/storage systems rely on chemical processes that involve bond-breaking molecular reactions. In this context, a fundamental thermodynamic property of interest is the bond dissociation energy (BDE) which measures the strength of a chemical bond. Fast and accurate prediction of BDEs for arbitrary molecules would lay the groundwork for data-driven projections of complex reaction cascades and hence a deeper understanding of these critical chemical processes and, ultimately, how to reverse design them. In this paper, we propose a chemically inspired graph neural network machine learning model, BonDNet, for the rapid and accurate prediction of BDEs. BonDNet maps the difference between the molecular representations of the reactants and products to the reaction BDE. Because of the use of this difference representation and the introduction of global features, including molecular charge, it is the first machine learning model capable of predicting both homolytic and heterolytic BDEs for molecules of any charge. To test the model, we have constructed a dataset of both homolytic and heterolytic BDEs for neutral and charged (-1 and +1) molecules. BonDNet achieves a mean absolute error (MAE) of 0.022 eV for unseen test data, significantly below chemical accuracy (0.043 eV). Besides the ability to handle complex bond dissociation reactions that no previous model could consider, BonDNet distinguishes itself even in only predicting homolytic BDEs for neutral molecules; it achieves an MAE of 0.020 eV on the PubChem BDE dataset, a 20% improvement over the previous best performing model. We gain additional insight into the model's predictions by analyzing the patterns in the features representing the molecules and the bond dissociation reactions, which are qualitatively consistent with chemical rules and intuition. BonDNet is just one application of our general approach to representing and learning chemical reactivity, and it could be easily extended to the prediction of other reaction properties in the future. Dissociation (chemistry) ; Molecule ; Bond graph ; Artificial neural network ; Computational chemistry ; Chemistry ; Graph ; Computer science ; Chemical physics ; Artificial intelligence ; Theoretical computer science ; Mathematics ; Organic chemistry ; Combinatorics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Ruiyang Li | Zeyu Liu | Andrew Rohskopf | Kiarash Gordiz | Asegun Henry | Eungkyu Lee | Tengfei Luo
Source: Applied Physics Letters (article, 2020-10-12)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A deep neural network interatomic potential for studying thermal conductivity of <b> <i>β</i> </b>-Ga2O3 β-Ga2O3 is a wide-bandgap semiconductor of significant technological importance for electronics, but its low thermal conductivity is an impeding factor for its applications. In this work, an interatomic potential is developed for β-Ga2O3 based on a deep neural network model to predict the thermal conductivity and phonon transport properties. Our potential is trained by the ab initio energy surface and atomic forces, which reproduces phonon dispersion in good agreement with first-principles calculations. We are able to use molecular dynamics (MD) simulations to predict the anisotropic thermal conductivity of β-Ga2O3 with this potential, and the calculated thermal conductivity values agree well with experimental results from 200 to 500 K. Green–Kubo modal analysis is performed to quantify the contributions of different phonon modes to the thermal transport, showing that optical phonon modes play a critical role in the thermal transport. This work provides a high-fidelity machine learning-based potential for MD simulation of β-Ga2O3 and serves as a good example of exploring thermal transport physics of complex semiconductor materials. Thermal conductivity ; Phonon ; Condensed matter physics ; Interatomic potential ; Semiconductor ; Work (physics) ; Materials science ; Anisotropy ; Molecular dynamics ; Physics ; Chemistry ; Thermodynamics ; Optoelectronics ; Optics ; Computational chemistry Ga2O3 and related materials ; Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides
Authors: Bing He | Anjiang Ye | Shuting Chi | Penghui Mi | Yunbing Ran | Liwen Zhang | Xinxin Zou | Bowei Pu | Qian Zhao | Zheyi Zou | Da Wang | Wenqing Zhang | Jing‐Tai Zhao | Maxim Avdeev | Siqi Shi
Source: Scientific Data (article, 2020-05-22)
Field: Materials Science — Materials Chemistry

CAVD, towards better characterization of void space for ionic transport analysis Geometric crystal structure analysis using three-dimensional Voronoi tessellation provides intuitive insights into the ionic transport behavior of metal-ion electrode materials or solid electrolytes by mapping the void space in a framework onto a network. The existing tools typically consider only the local voids by mapping them with Voronoi polyhedra vertices and then define the mobile ions pathways using the Voronoi edges connecting these vertices. We show that in some structures mobile ions are located on Voronoi polyhedra faces and thus cannot be located by a standard approach. To address this deficiency, we extend the method to include Voronoi faces in the constructed network. This method has been implemented in the CAVD python package. Its effectiveness is demonstrated by 99% recovery rate for the lattice sites of mobile ions in 6,955 Li-, Na-, Mg- and Al-containing ionic compounds extracted from the Inorganic Crystal Structure Database. In addition, various quantitative descriptors of the network can be used to identify and rank the materials and further used in materials databases for machine learning. Voronoi diagram ; Polyhedron ; Computer science ; Ion ; Void (composites) ; Ionic bonding ; Lattice (music) ; Materials science ; Chemical physics ; Crystallography ; Geometry ; Mathematics ; Chemistry ; Physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Edwin Bedolla | Luis Carlos Padierna | Ramón Castañeda-Priego
Source: Journal of Physics Condensed Matter (article, 2020-09-15)
Field: Materials Science — Materials Chemistry

Machine learning for condensed matter physics Condensed matter physics (CMP) seeks to understand the microscopic interactions of matter at the quantum and atomistic levels, and describes how these interactions result in both mesoscopic and macroscopic properties. CMP overlaps with many other important branches of science, such as chemistry, materials science, statistical physics, and high-performance computing. With the advancements in modern machine learning (ML) technology, a keen interest in applying these algorithms to further CMP research has created a compelling new area of research at the intersection of both fields. In this review, we aim to explore the main areas within CMP, which have successfully applied ML techniques to further research, such as the description and use of ML schemes for potential energy surfaces, the characterization of topological phases of matter in lattice systems, the prediction of phase transitions in off-lattice and atomistic simulations, the interpretation of ML theories with physics-inspired frameworks and the enhancement of simulation methods with ML algorithms. We also discuss in detail the main challenges and drawbacks of using ML methods on CMP problems, as well as some perspectives for future developments. Mesoscopic physics ; Intersection (aeronautics) ; Interpretation (philosophy) ; Characterization (materials science) ; Phase transition ; Lattice (music) Machine Learning in Materials Science ; Quantum many-body systems ; Artificial Intelligence in Healthcare and Education