jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Avery E. Baumann | David A. Burns | Bingqian Liu | V. Sara Thoi
Source: Communications Chemistry (article, 2019-07-26)
Field: Chemistry — Inorganic Chemistry

Metal-organic framework functionalization and design strategies for advanced electrochemical energy storage devices Abstract Metal-organic frameworks (MOFs) are a class of porous materials with unprecedented chemical and structural tunability. Their synthetic versatility, long-range order, and rich host–guest chemistry make MOFs ideal platforms for identifying design features for advanced functional materials. This review addresses synthetic approaches to control MOF attributes for realizing material properties such as charge conductivity, stability, surface area, and flexibility. Along with an updated account on MOFs employed in batteries and supercapacitors, new directions are outlined for advancing MOF research in emergent technologies such as solid-state electrolytes and battery operation in extreme environments. Supercapacitor ; Flexibility (engineering) ; Nanotechnology ; Metal-organic framework ; Electrochemical energy storage ; Battery (electricity) ; Surface modification ; Materials science ; Energy storage ; Electrochemistry ; Computer science ; Engineering ; Mechanical engineering ; Electrode ; Chemistry Metal-Organic Frameworks: Synthesis and Applications ; Supercapacitor Materials and Fabrication ; Advancements in Battery Materials
Authors: Oliver T. Unke | Markus Meuwly
Source: Journal of Chemical Theory and Computation (article, 2019-05-01)
Field: Materials Science — Materials Chemistry

PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments, and Partial Charges In recent years, machine learning (ML) methods have become increasingly popular in computational chemistry. After being trained on appropriate ab initio reference data, these methods allow for accurately predicting the properties of chemical systems, circumventing the need for explicitly solving the electronic Schrödinger equation. Because of their computational efficiency and scalability to large data sets, deep neural networks (DNNs) are a particularly promising ML algorithm for chemical applications. This work introduces PhysNet, a DNN architecture designed for predicting energies, forces, and dipole moments of chemical systems. PhysNet achieves state-of-the-art performance on the QM9, MD17, and ISO17 benchmarks. Further, two new data sets are generated in order to probe the performance of ML models for describing chemical reactions, long-range interactions, and condensed phase systems. It is shown that explicitly including electrostatics in energy predictions is crucial for a qualitatively correct description of the asymptotic regions of a potential energy surface (PES). PhysNet models trained on a systematically constructed set of small peptide fragments (at most eight heavy atoms) are able to generalize to considerably larger proteins like deca-alanine (Ala<sub>10</sub>): The optimized geometry of helical Ala<sub>10</sub> predicted by PhysNet is virtually identical to ab initio results (RMSD = 0.21 Å). By running unbiased molecular dynamics (MD) simulations of Ala<sub>10</sub> on the PhysNet-PES in gas phase, it is found that instead of a helical structure, Ala<sub>10</sub> folds into a "wreath-shaped" configuration, which is more stable than the helical form by 0.46 kcal mol<sup>-1</sup> according to the reference ab initio calculations. Dipole ; Ab initio ; Artificial neural network ; Computer science ; Electrostatics ; Scalability ; Statistical physics ; Physics ; Artificial intelligence ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Jing Wei | Xuan Chu | Xiangyu Sun | Kun Xu | Hui‐Xiong Deng | Ji-Gen Chen | Zhongming Wei | Ming Lei
Source: InfoMat (article, 2019-09-01)
Field: Materials Science — Materials Chemistry

Machine learning in materials science Abstract Traditional methods of discovering new materials, such as the empirical trial and error method and the density functional theory (DFT)‐based method, are unable to keep pace with the development of materials science today due to their long development cycles, low efficiency, and high costs. Accordingly, due to its low computational cost and short development cycle, machine learning is coupled with powerful data processing and high prediction performance and is being widely used in material detection, material analysis, and material design. In this article, we discuss the basic operational procedures in analyzing material properties via machine learning, summarize recent applications of machine learning algorithms to several mature fields in materials science, and discuss the improvements that are required for wide‐ranging application. Pace ; Computer science ; Machine learning ; Artificial intelligence ; Development (topology) ; Ranging ; Industrial engineering ; Engineering ; Mathematics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Volker L. Deringer | A. Miguel | Gábor Cśanyi
Source: Advanced Materials (review, 2019-09-05)
Field: Materials Science — Materials Chemistry

Machine Learning Interatomic Potentials as Emerging Tools for Materials Science Atomic-scale modeling and understanding of materials have made remarkable progress, but they are still fundamentally limited by the large computational cost of explicit electronic-structure methods such as density-functional theory. This Progress Report shows how machine learning (ML) is currently enabling a new degree of realism in materials modeling: by "learning" electronic-structure data, ML-based interatomic potentials give access to atomistic simulations that reach similar accuracy levels but are orders of magnitude faster. A brief introduction to the new tools is given, and then, applications to some select problems in materials science are highlighted: phase-change materials for memory devices; nanoparticle catalysts; and carbon-based electrodes for chemical sensing, supercapacitors, and batteries. It is hoped that the present work will inspire the development and wider use of ML-based interatomic potentials in diverse areas of materials research. Materials science ; Interatomic potential ; Nanotechnology ; Density functional theory ; Electronic structure ; Atomic units ; Supercapacitor ; Scale (ratio) ; Nanoparticle ; Work (physics) ; Computer science ; Molecular dynamics ; Electrode ; Computational chemistry ; Physics ; Thermodynamics ; Condensed matter physics Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Advanced Memory and Neural Computing
Authors: Michael Hirscher | V.A. Yartys | Marcello Baricco | José M. Bellosta von Colbe | Didier Blanchard | R. C. Bowman | Darren P. Broom | Craig E. Buckley | Fei Chang | Ping Chen | Young Whan Cho | Jean‐Claude Crivello | Fermín Cuevas | William I. F. David | Petra E. de Jongh | R.V. Denys | Martin Dornheim | Michael Felderhoff | Yaroslav Filinchuk | George E. Froudakis | David M. Grant | Evan Gray | Bjørn C. Hauback | Teng He | Terry D. Humphries | Torben R. Jensen | Sangryun Kim | Yoshitsugu Kojima | Michel Latroche | Haiwen Li | Mykhaylo Lototskyy | Joshua W. Makepeace | Kasper T. Møller | Lubna Naheed | Peter Ngene | Dag Noréus | Magnus Moe Nygård | Shin‐ichi Orimo | Mark Paskevicius | Luca Pasquini | Dorthe Bomholdt Ravnsbæk | M. Veronica Sofianos | Terrence J. Udovic | Tejs Vegge | Gavin S. Walker | C. J. Webb | Claudia Weidenthaler | Claudia Zlotea
Source: Journal of Alloys and Compounds (article, 2019-12-31)
Field: Materials Science — Materials Chemistry

Materials for hydrogen-based energy storage – past, recent progress and future outlook Globally, the accelerating use of renewable energy sources, enabled by increased efficiencies and reduced costs, and driven by the need to mitigate the effects of climate change, has significantly increased research in the areas of renewable energy production, storage, distribution and end-use. Central to this discussion is the use of hydrogen, as a clean, efficient energy vector for energy storage. This review, by experts of Task 32, “Hydrogen-based Energy Storage” of the International Energy Agency, Hydrogen TCP, reports on the development over the last 6 years of hydrogen storage materials, methods and techniques, including electrochemical and thermal storage systems. An overview is given on the background to the various methods, the current state of development and the future prospects. The following areas are covered; porous materials, liquid hydrogen carriers, complex hydrides, intermetallic hydrides, electrochemical storage of energy, thermal energy storage, hydrogen energy systems and an outlook is presented for future prospects and research on hydrogen-based energy storage. Hydrogen storage ; Engineering physics ; Energy storage ; Materials science ; Nanotechnology ; Metallurgy ; Engineering ; Physics ; Thermodynamics ; Alloy Hydrogen Storage and Materials ; Hybrid Renewable Energy Systems ; Advanced Battery Materials and Technologies
Authors: Ralf Drautz
Source: Physical review. B./Physical review. B (article, 2019-01-08)
Field: Materials Science — Materials Chemistry

Atomic cluster expansion for accurate and transferable interatomic potentials The atomic cluster expansion is developed as a complete descriptor of the local atomic environment, including multicomponent materials, and its relation to a number of other descriptors and potentials is discussed. The effort for evaluating the atomic cluster expansion is shown to scale linearly with the number of neighbors, irrespective of the order of the expansion. Application to small Cu clusters demonstrates smooth convergence of the atomic cluster expansion to meV accuracy. By introducing nonlinear functions of the atomic cluster expansion an interatomic potential is obtained that is comparable in accuracy to state-of-the-art machine learning potentials. Because of the efficient convergence of the atomic cluster expansion relevant subspaces can be sampled uniformly and exhaustively. This is demonstrated by testing against a large database of density functional theory calculations for copper. Cluster expansion ; Cluster (spacecraft) ; Convergence (economics) ; Atomic units ; Linear subspace ; Atomic number ; Coupled cluster ; Interatomic potential ; Nonlinear system ; Atomic physics ; Physics ; Materials science ; Statistical physics ; Computer science ; Quantum mechanics ; Molecular dynamics ; Mathematics ; Molecule Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallography and molecular interactions
Authors: Jeremy B. Vines | Jee-Hyun Yoon | Na-Eun Ryu | Dong-Jin Lim | Hansoo Park
Source: Frontiers in Chemistry (review, 2019-04-05)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Gold Nanoparticles for Photothermal Cancer Therapy Gold is a multifunctional material that has been utilized in medicinal applications for centuries because it has been recognized for its bacteriostatic, anticorrosive, and antioxidative properties. Modern medicine makes routine, conventional use of gold and has even developed more advanced applications by taking advantage of its ability to be manufactured at the nanoscale and functionalized because of the presence of thiol and amine groups, allowing for the conjugation of various functional groups such as targeted antibodies or drug products. It has been shown that colloidal gold exhibits localized plasmon surface resonance (LPSR), meaning that gold nanoparticles can absorb light at specific wavelengths, resulting in photoacoustic and photothermal properties, making them potentially useful for hyperthermic cancer treatments and medical imaging applications. Modifying gold nanoparticle shape and size can change their LPSR photochemical activities, thereby also altering their photothermal and photoacoustic properties, allowing for the utilization of different wavelengths of light, such as light in the near-infrared spectrum. By manufacturing gold in a nanoscale format, it is possible to passively distribute the material through the body, where it can localize in tumors (which are characterized by leaky blood vessels) and be safely excreted through the urinary system. In this paper, we give a quick review of the structure, applications, recent advancements, and potential future directions for the utilization of gold nanoparticles in cancer therapeutics. Photothermal therapy ; Colloidal gold ; Nanotechnology ; Surface plasmon resonance ; Nanoparticle ; Materials science ; Photoacoustic imaging in biomedicine ; Plasmon ; Cancer therapy ; Cancer ; Optoelectronics ; Optics Gold and Silver Nanoparticles Synthesis and Applications ; Nanoplatforms for cancer theranostics ; Nanoparticle-Based Drug Delivery
Authors: Philippe Schwaller | Teodoro Laino | Théophile Gaudin | Peter Bolgar | Christopher A. Hunter | Costas Bekas | Alpha A. Lee
Source: ACS Central Science (article, 2019-08-30)
Field: Materials Science — Materials Chemistry

Molecular Transformer: A Model for Uncertainty-Calibrated Chemical Reaction Prediction Organic synthesis is one of the key stumbling blocks in medicinal chemistry. A necessary yet unsolved step in planning synthesis is solving the forward problem: Given reactants and reagents, predict the products. Similar to other work, we treat reaction prediction as a machine translation problem between simplified molecular-input line-entry system (SMILES) strings (a text-based representation) of reactants, reagents, and the products. We show that a multihead attention Molecular Transformer model outperforms all algorithms in the literature, achieving a top-1 accuracy above 90% on a common benchmark data set. Molecular Transformer makes predictions by inferring the correlations between the presence and absence of chemical motifs in the reactant, reagent, and product present in the data set. Our model requires no handcrafted rules and accurately predicts subtle chemical transformations. Crucially, our model can accurately estimate its own uncertainty, with an uncertainty score that is 89% accurate in terms of classifying whether a prediction is correct. Furthermore, we show that the model is able to handle inputs without a reactant-reagent split and including stereochemistry, which makes our method universally applicable. Transformer ; Benchmark (surveying) ; Key (lock) ; Synthetic data ; Experimental data ; Translation (biology) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Asymmetric Hydrogenation and Catalysis
Authors: Gabriel R. Schleder | A. C. M. Padilha | Carlos Mera Acosta | Marcio Costa | A. Fazzio
Source: Journal of Physics Materials (article, 2019-02-19)
Field: Materials Science — Materials Chemistry

From DFT to machine learning: recent approaches to materials science–a review Abstract Recent advances in experimental and computational methods are increasing the quantity and complexity of generated data. This massive amount of raw data needs to be stored and interpreted in order to advance the materials science field. Identifying correlations and patterns from large amounts of complex data is being performed by machine learning algorithms for decades. Recently, the materials science community started to invest in these methodologies to extract knowledge and insights from the accumulated data. This review follows a logical sequence starting from density functional theory as the representative instance of electronic structure methods, to the subsequent high-throughput approach, used to generate large amounts of data. Ultimately, data-driven strategies which include data mining, screening, and machine learning techniques, employ the data generated. We show how these approaches to modern computational materials science are being used to uncover complexities and design novel materials with enhanced properties. Finally, we point to the present research problems, challenges, and potential future perspectives of this new exciting field. Computer science ; Field (mathematics) ; Data science ; Raw data ; Point (geometry) ; Machine learning ; Artificial intelligence Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electronic and Structural Properties of Oxides
Authors: Kenry Kenry | Chengjian Chen | Bin Liu
Source: Nature Communications (review, 2019-05-08)
Field: Materials Science — Materials Chemistry

Enhancing the performance of pure organic room-temperature phosphorescent luminophores Once considered the exclusive property of metal complexes, the phenomenon of room-temperature phosphorescence (RTP) has been increasingly realized in pure organic luminophores recently. Using precise molecular design and synthetic approaches to modulate their weak spin-orbit coupling, highly active triplet excitons, and ultrafast deactivation, organic luminophores can be endowed with long-lived and bright RTP characteristics. This has sparked intense explorations into organic luminophores with enhanced RTP features for different applications. This Review discusses the fundamental mechanism of RTP in pure organic luminophores, followed by design principles, enhancement strategies, and formulation methods to achieve highly phosphorescent and long-lived organic RTP luminophores even in aqueous media. The current challenges and future directions of this field are also discussed in the summary and outlook. Phosphorescence ; Materials science ; Exciton ; Photochemistry ; Phosphorescent organic light-emitting diode ; Nanotechnology ; Optoelectronics ; Chemistry ; Physics ; Fluorescence ; Optics Luminescence and Fluorescent Materials ; Organic Light-Emitting Diodes Research ; Perovskite Materials and Applications
Authors: José M. Bellosta von Colbe | J.R. Ares | Jussara Barale | Marcello Baricco | Craig E. Buckley | Giovanni Capurso | Noris Gallandat | David M. Grant | Matylda N. Guzik | I. Jacob | Emil H. Jensen | Torben R. Jensen | Julian Jepsen | Thomas Klassen | Mykhaylo Lototskyy | Kandavel Manickam | Amelia Montone | Julián Puszkiel | Sabrina Sartori | Drew A. Sheppard | Alastair Stuart | Gavin S. Walker | C. J. Webb | Heena Yang | V.A. Yartys | Andreas Züttel | Martin Dornheim
Source: International Journal of Hydrogen Energy (article, 2019-02-13)
Field: Materials Science — Materials Chemistry

Application of hydrides in hydrogen storage and compression: Achievements, outlook and perspectives Metal hydrides are known as a potential efficient, low-risk option for high-density hydrogen storage since the late 1970s. In this paper, the present status and the future perspectives of the use of metal hydrides for hydrogen storage are discussed. Since the early 1990s, interstitial metal hydrides are known as base materials for Ni – metal hydride rechargeable batteries. For hydrogen storage, metal hydride systems have been developed in the 2010s [1] for use in emergency or backup power units, i. e. for stationary applications. With the development and completion of the first submarines of the U212 A series by HDW (now Thyssen Krupp Marine Systems) in 2003 and its export class U214 in 2004, the use of metal hydrides for hydrogen storage in mobile applications has been established, with new application fields coming into focus. In the last decades, a huge number of new intermetallic and partially covalent hydrogen absorbing compounds has been identified and partly more, partly less extensively characterized. In addition, based on the thermodynamic properties of metal hydrides, this class of materials gives the opportunity to develop a new hydrogen compression technology. They allow the direct conversion from thermal energy into the compression of hydrogen gas without the need of any moving parts. Such compressors have been developed and are nowadays commercially available for pressures up to 200 bar. Metal hydride based compressors for higher pressures are under development. Moreover, storage systems consisting of the combination of metal hydrides and high-pressure vessels have been proposed as a realistic solution for on-board hydrogen storage on fuel cell vehicles. In the frame of the “Hydrogen Storage Systems for Mobile and Stationary Applications” Group in the International Energy Agency (IEA) Hydrogen Task 32 “Hydrogen-based energy storage”, different compounds have been and will be scaled-up in the near future and tested in the range of 500 g to several hundred kg for use in hydrogen storage applications. Hydrogen storage ; Hydrogen ; Compression (physics) ; Thermodynamics ; Materials science ; Chemistry ; Physics Hydrogen Storage and Materials ; Fusion materials and technologies ; Spacecraft and Cryogenic Technologies
Authors: Sin‐Yi Pang | Yuen-Ting Wong | Shuoguo Yuan | Yan Liu | Ming‐Kiu Tsang | Zhibin Yang | Haitao Huang | Wing‐Tak Wong | Jianhua Hao
Source: Journal of the American Chemical Society (article, 2019-05-22)
Field: Materials Science — Materials Chemistry

Universal Strategy for HF-Free Facile and Rapid Synthesis of Two-dimensional MXenes as Multifunctional Energy Materials Two-dimensional MXenes are promising for various energy-related applications such as energy storage devices and electrocatalysis of water-splitting. MXenes prepared from hydrofluoric (HF) acid etching have been widely reported. Nonetheless, the acute toxicity of HF acid impedes the large-scale fabrication of MXenes and their wide utilization in energy-related applications. It is thus greatly encouraging to explore a more innocuous protocol for MXenes synthesis. Thereby, a universal strategy based on thermal-assisted electrochemical etching route is developed to synthesize MXenes (e.g., Ti<sub>2</sub>CT <sub>x</sub>, Cr<sub>2</sub>CT <sub>x</sub>, and V<sub>2</sub>CT <sub>x</sub>). Furthermore, the cobalt ion doped MXenes show an exceptionally enhanced capability of hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) activity, demonstrating their multifunctionalities, which is comparable to the commercialized catalysts. Moreover, we successfully exploited our MXenes as cathodes for the novel aqueous rechargeable battery, with proficient retention and excellent electrical output performance. This work paves a nontoxic and HF-free route to prepare various MXenes and demonstrates practical applications of the materials. MXenes ; Chemistry ; Electrocatalyst ; Nanotechnology ; Hydrofluoric acid ; Electrochemistry ; Materials science ; Electrode ; Inorganic chemistry MXene and MAX Phase Materials ; Advanced Memory and Neural Computing ; Advanced Photocatalysis Techniques