jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kirchner-Hall, Nicole | Zhao, Wayne | Xiong, Yihuang | Timrov, Iurii | Dabo, Ismaila
Source: Open MIND (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Extensive benchmarking of DFT+U calculations for predicting band gaps Accurate computational predictions of band gaps are of practical importance to the modeling and development of semiconductor technologies, such as (opto)electronic devices and photoelectrochemical cells. Among available electronic-structure methods, density-functional theory (DFT) with the Hubbard U correction (DFT+U) applied to band edge states is a computationally tractable approach to improve the accuracy of band gap predictions beyond that of DFT calculations based on (semi)local functionals. At variance with DFT approximations, which are not intended to describe optical band gaps and other excited-state properties, DFT+U can be interpreted as an approximate spectral-potential method when U is determined by imposing the piecewise linearity of the total energy with respect to electronic occupations in the Hubbard manifold (thus removing self-interaction errors in this subspace), thereby providing a (heuristic) justification for using DFT+U to predict band gaps. However, it is still frequent in the literature to determine the Hubbard U parameters semiempirically by tuning their values to reproduce experimental band gaps, which ultimately alters the description of other total-energy characteristics. Here, we present an extensive assessment of DFT+U band gaps computed using self-consistent ab initio U parameters obtained from density-functional perturbation theory to impose the aforementioned piecewise linearity of the total energy. The study is carried out on 20 compounds containing transition-metal or p-block (group III-IV) elements, including oxides, nitrides, sulfides, oxynitrides, and oxysulfides. By comparing DFT+U results obtained using nonorthogonalized and orthogonalized atomic orbitals as Hubbard projectors, we find that the predicted band gaps are extremely sensitive to the type of projector functions and that the orthogonalized projectors give the most accurate band gaps, in satisfactory agreement with experimental data. This work demonstrates that DFT+U may serve as a useful method for high-throughput workflows that require reliable band gap predictions at moderate computational cost. Density functional theory ; Band gap ; Ab initio ; Piecewise ; Hybrid functional ; Hubbard model ; Electronic structure ; Perturbation theory (quantum mechanics) ; Physics ; Materials science ; Statistical physics ; Quantum mechanics ; Computational physics ; Mathematics ; Mathematical analysis Machine Learning in Materials Science ; Quantum Dots Synthesis And Properties ; Electronic and Structural Properties of Oxides
Authors: Chensen Lin | Zhen Li | Lu Lu | Shengze Cai | Martin Maxey | George Em Karniadakis
Source: The Journal of Chemical Physics (article, 2021-03-10)
Field: Materials Science — Materials Chemistry

Operator learning for predicting multiscale bubble growth dynamics Simulating and predicting multiscale problems that couple multiple physics and dynamics across many orders of spatiotemporal scales is a great challenge that has not been investigated systematically by deep neural networks (DNNs). Herein, we develop a framework based on operator regression, the so-called deep operator network (DeepONet), with the long-term objective to simplify multiscale modeling by avoiding the fragile and time-consuming "hand-shaking" interface algorithms for stitching together heterogeneous descriptions of multiscale phenomena. To this end, as a first step, we investigate if a DeepONet can learn the dynamics of different scale regimes, one at the deterministic macroscale and the other at the stochastic microscale regime with inherent thermal fluctuations. Specifically, we test the effectiveness and accuracy of the DeepONet in predicting multirate bubble growth dynamics, which is described by a Rayleigh-Plesset (R-P) equation at the macroscale and modeled as a stochastic nucleation and cavitation process at the microscale by dissipative particle dynamics (DPD). First, we generate data using the R-P equation for multirate bubble growth dynamics caused by randomly time-varying liquid pressures drawn from Gaussian random fields (GRFs). Our results show that properly trained DeepONets can accurately predict the macroscale bubble growth dynamics and can outperform long short-term memory networks. We also demonstrate that the DeepONet can extrapolate accurately outside the input distribution using only very few new measurements. Subsequently, we train the DeepONet with DPD data corresponding to stochastic bubble growth dynamics. Although the DPD data are noisy and we only collect sparse data points on the trajectories, the trained DeepONet model is able to predict accurately the mean bubble dynamics for time-varying GRF pressures. Taken together, our findings demonstrate that DeepONets can be employed to unify the macroscale and microscale models of the multirate bubble growth problem, hence providing new insight into the role of operator regression via DNNs in tackling realistic multiscale problems and in simplifying modeling with heterogeneous descriptions. Microscale chemistry ; Bubble ; Operator (biology) ; Dissipative particle dynamics ; Gaussian ; Artificial neural network ; Stochastic process ; Scale (ratio) Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; nanoparticles nucleation surface interactions
Authors: Abudukeremu Kadier | R.A. Ilyas | Muhammad Huzaifah Mohd Roslim | Nani Harihastuti | S.M. Sapuan | M.M. Harussani | M. N. M. Azlin | Rustiana Yuliasni | Rushdan Ibrahim | M.S.N. Atikah | Junying Wang | K. Chandrasekhar | M. Amirul Islam | Shubham Sharma | Sneh Punia | Aruliah Rajasekar | M. R. M. Asyraf | Mohamad Ridzwan Ishak
Source: Polymers (review, 2021-09-30)
Field: Materials Science — Biomaterials

Use of Industrial Wastes as Sustainable Nutrient Sources for Bacterial Cellulose (BC) Production: Mechanism, Advances, and Future Perspectives A novel nanomaterial, bacterial cellulose (BC), has become noteworthy recently due to its better physicochemical properties and biodegradability, which are desirable for various applications. Since cost is a significant limitation in the production of cellulose, current efforts are focused on the use of industrial waste as a cost-effective substrate for the synthesis of BC or microbial cellulose. The utilization of industrial wastes and byproduct streams as fermentation media could improve the cost-competitiveness of BC production. This paper examines the feasibility of using typical wastes generated by industry sectors as sources of nutrients (carbon and nitrogen) for the commercial-scale production of BC. Numerous preliminary findings in the literature data have revealed the potential to yield a high concentration of BC from various industrial wastes. These findings indicated the need to optimize culture conditions, aiming for improved large-scale production of BC from waste streams. Cellulose ; Bacterial cellulose ; Industrial waste ; Environmental science ; Nutrient ; Waste management ; Biodegradation ; Production (economics) ; Industrial production ; Biochemical engineering ; Pulp and paper industry ; Chemistry ; Engineering Advanced Cellulose Research Studies ; Polysaccharides and Plant Cell Walls ; Biofuel production and bioconversion
Authors: Yunxing Zuo | Mingde Qin | Chi Chen | Weike Ye | Xiangguo Li | Jian Luo | Shyue Ping Ong
Source: Materials Today (article, 2021-10-01)
Field: Materials Science — Materials Chemistry

Accelerating materials discovery with Bayesian optimization and graph deep learning Bottleneck ; Bayesian optimization ; Spark plasma sintering ; Materials science ; Density functional theory ; Computer science ; Crystal (programming language) ; Algorithm ; Statistical physics ; Artificial intelligence ; Sintering ; Physics ; Computational chemistry ; Chemistry ; Composite material Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Inorganic Chemistry and Materials
Authors: Zheyong Fan
Source: Journal of Physics Condensed Matter (article, 2021-12-23)
Field: Materials Science — Materials Chemistry

Improving the accuracy of the neuroevolution machine learning potential for multi-component systems In a previous paper Fan<i>et al</i>(2021<i>Phys. Rev.</i>B<b>104</b>, 104309), we developed the neuroevolution potential (NEP), a framework of training neural network based machine-learning potentials using a natural evolution strategy and performing molecular dynamics (MD) simulations using the trained potentials. The atom-environment descriptor in NEP was constructed based on a set of radial and angular functions. For multi-component systems, all the radial functions between two atoms are multiplied by some fixed factors that depend on the types of the two atoms only. In this paper, we introduce an improved descriptor for multi-component systems, in which different radial functions are multiplied by different factors that are also optimized during the training process, and show that it can significantly improve the regression accuracy without increasing the computational cost in MD simulations. Component (thermodynamics) ; Computer science ; Process (computing) ; Artificial neural network ; Set (abstract data type) ; Artificial intelligence ; Neuroevolution ; Atom (system on chip) ; Molecular dynamics ; Machine learning ; Algorithm ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Guoxing Chen | Armin Feldhoff | Anke Weidenkaff | Claudia Li | Shaomin Liu | Xuefeng Zhu | Jaka Sunarso | Kevin Huang | Xiaoyu Wu | Ahmed F. Ghoniem | Weishen Yang | Jian Xue | Haihui Wang | Zongping Shao | Jack H. Duffy | Kyle S. Brinkman | Xiaoyao Tan | Yan Zhang | Heqing Jiang | Rémi Costa | K. Andreas Friedrich | R. Kriegel
Source: Advanced Functional Materials (article, 2021-10-23)
Field: Materials Science — Materials Chemistry

Roadmap for Sustainable Mixed Ionic‐Electronic Conducting Membranes Abstract Mixed ionic‐electronic conducting (MIEC) membranes have gained growing interest recently for various promising environmental and energy applications, such as H 2 and O 2 production, CO 2 reduction, O 2 and H 2 separation, CO 2 separation, membrane reactors for production of chemicals, cathode development for solid oxide fuel cells, solar‐driven evaporation and energy‐saving regeneration as well as electrolyzer cells for power‐to‐X technologies. The purpose of this roadmap, written by international specialists in their fields, is to present a snapshot of the state‐of‐the‐art, and provide opinions on the future challenges and opportunities in this complex multidisciplinary research field. As the fundamentals of using MIEC membranes for various applications become increasingly challenging tasks, particularly in view of the growing interdisciplinary nature of this field, a better understanding of the underlying physical and chemical processes is also crucial to enable the career advancement of the next generation of researchers. As an integrated and combined article, it is hoped that this roadmap, covering all these aspects, will be informative to support further progress in academics as well as in the industry‐oriented research toward commercialization of MIEC membranes for different applications. Commercialization ; Membrane ; Materials science ; Nanotechnology ; Multidisciplinary approach ; Fuel cells ; Clean energy ; Engineering ; Business ; Chemical engineering ; Political science Advancements in Solid Oxide Fuel Cells ; Electronic and Structural Properties of Oxides ; CO2 Reduction Techniques and Catalysts
Authors: Christina Spägele | Michele Tamagnone | Dmitry Kazakov | Marcus Ossiander | Marco Piccardo | Federico Capasso
Source: Nature Communications (article, 2021-06-18)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Multifunctional wide-angle optics and lasing based on supercell metasurfaces Metasurfaces are arrays of subwavelength spaced nanostructures that can manipulate the amplitude, phase, and polarization of light to achieve a variety of optical functions beyond the capabilities of 3D bulk optics. However, they suffer from limited performance and efficiency when multiple functions with large deflection angles are required because the non-local interactions due to optical coupling between nanostructures are not fully considered. Here we introduce a method based on supercell metasurfaces to demonstrate multiple independent optical functions at arbitrary large deflection angles with high efficiency. In one implementation the incident laser is simultaneously diffracted into Gaussian, helical and Bessel beams over a large angular range. We then demonstrate a compact wavelength-tunable external cavity laser with arbitrary beam control capabilities - including beam shaping operations and the generation of freeform holograms. Our approach paves the way to novel methods to engineer the emission of optical sources. Optics ; Lasing threshold ; Holography ; Gaussian beam ; Laser ; Bessel function ; Physics ; Polarization (electrochemistry) ; Optoelectronics ; Materials science ; Beam (structure) Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Orbital Angular Momentum in Optics
Authors: Wei‐Chih Chao | Tzu‐Hsuan Chiang | Yichun Liu | Zhi‐Xuan Huang | Chia-Chun Liao | Chen-Hsien Chu | Chih‐Hsing Wang | Huan-Wei Tseng | Wen‐Yi Hung | Pi‐Tai Chou
Source: Communications Materials (article, 2021-09-17)
Field: Materials Science — Materials Chemistry

High efficiency green InP quantum dot light-emitting diodes by balancing electron and hole mobility Abstract The industrialization of quantum dot light-emitting diodes (QLEDs) requires the use of less hazardous cadmium-free quantum dots, among which ZnSe-based blue and InP-based green and red quantum dots have received considerable attention. In comparison, the development of InP-based green QLEDs is lagging behind. Here, we prepare green InP/ZnSe/ZnS quantum dots with a diameter of 8.6 nm. We then modify the InP quantum dot emitting layer by passivation with various alkyl diamines and zinc halides, which decreases electron mobility and enhances hole transport. This, together with optimizing the electron transport layer, leads to green 545 nm InP QLEDs with a maximum quantum efficiency (EQE) of 16.3% and a current efficiency 57.5 cd/A. EQE approaches the theoretical limit of InP quantum dots, with an emission quantum yield of 86%. Quantum dot ; Optoelectronics ; Diode ; Light-emitting diode ; Quantum efficiency ; Materials science ; Quantum yield ; Passivation ; Electron ; Layer (electronics) ; Nanotechnology ; Physics ; Optics ; Fluorescence Quantum Dots Synthesis And Properties ; Semiconductor Quantum Structures and Devices ; Chalcogenide Semiconductor Thin Films
Authors: Shingo Sotoma | Chongxia Zhong | James Chen Yong Kah | Hayato Yamashita | Taras Plakhotnik | Yoshie Harada | Madoka Suzuki
Source: Science Advances (article, 2021-01-15)
Field: Materials Science — Materials Chemistry

In situ measurements of intracellular thermal conductivity using heater-thermometer hybrid diamond nanosensors Understanding heat dissipation processes at nanoscale during cellular thermogenesis is essential to clarify the relationships between the heat and biological processes in cells and organisms. A key parameter determining the heat flux inside a cell is the local thermal conductivity, a factor poorly investigated both experimentally and theoretically. Here, using a nanoheater/nanothermometer hybrid made of a polydopamine encapsulating a fluorescent nanodiamond, we measured the intracellular thermal conductivities of HeLa and MCF-7 cells with a spatial resolution of about 200 nm. The mean values determined in these two cell lines are both 0.11 ± 0.04 W m<sup>-1</sup> K<sup>-1</sup>, which is significantly smaller than that of water. Bayesian analysis of the data suggests there is a variation of the thermal conductivity within a cell. These results make the biological impact of transient temperature spikes in a cell much more feasible, and suggest that cells may use heat flux for short-distance thermal signaling. Thermal conductivity ; Materials science ; Nanodiamond ; Heat flux ; Thermal diffusivity ; Intracellular ; Nanoscopic scale ; Biophysics ; Diamond ; Analytical Chemistry (journal) ; Biological system ; Nanotechnology ; Thermodynamics ; Chemistry ; Heat transfer ; Composite material ; Biology ; Physics ; Chromatography Thermal properties of materials ; thermodynamics and calorimetric analyses ; Diamond and Carbon-based Materials Research
Authors: Yanfei Guan | S. V. Shree Sowndarya | Liliana C. Gallegos | Peter C. St. John | Robert S. Paton
Source: Chemical Science (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Real-time prediction of <sup>1</sup> H and <sup>13</sup> C chemical shifts with DFT accuracy using a 3D graph neural network Nuclear magnetic resonance (NMR) is one of the primary techniques used to elucidate the chemical structure, bonding, stereochemistry, and conformation of organic compounds. The distinct chemical shifts in an NMR spectrum depend upon each atom's local chemical environment and are influenced by both through-bond and through-space interactions with other atoms and functional groups. The <i>in silico</i> prediction of NMR chemical shifts using quantum mechanical (QM) calculations is now commonplace in aiding organic structural assignment since spectra can be computed for several candidate structures and then compared with experimental values to find the best possible match. However, the computational demands of calculating multiple structural- and stereo-isomers, each of which may typically exist as an ensemble of rapidly-interconverting conformations, are expensive. Additionally, the QM predictions themselves may lack sufficient accuracy to identify a correct structure. In this work, we address both of these shortcomings by developing a rapid machine learning (ML) protocol to predict <sup>1</sup>H and <sup>13</sup>C chemical shifts through an efficient graph neural network (GNN) using 3D structures as input. Transfer learning with experimental data is used to improve the final prediction accuracy of a model trained using QM calculations. When tested on the CHESHIRE dataset, the proposed model predicts observed <sup>13</sup>C chemical shifts with comparable accuracy to the best-performing DFT functionals (1.5 ppm) in around 1/6000 of the CPU time. An automated prediction webserver and graphical interface are accessible online at http://nova.chem.colostate.edu/cascade/. We further demonstrate the model in three applications: first, we use the model to decide the correct organic structure from candidates through experimental spectra, including complex stereoisomers; second, we automatically detect and revise incorrect chemical shift assignments in a popular NMR database, the NMRShiftDB; and third, we use NMR chemical shifts as descriptors for determination of the sites of electrophilic aromatic substitution. Chemical shift ; Chemical space ; Artificial neural network ; Chemistry ; Computer science ; Artificial intelligence ; Biological system ; Machine learning ; Drug discovery ; Physical chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Molecular spectroscopy and chirality
Authors: Sauradeep Majumdar | Seyed Mohamad Moosavi | Kevin Maik Jablonka | Daniele Ongari | Berend Smit
Source: ACS Applied Materials & Interfaces (article, 2021-12-15)
Field: Chemistry — Inorganic Chemistry

Diversifying Databases of Metal Organic Frameworks for High-Throughput Computational Screening By combining metal nodes and organic linkers, an infinite number of metal organic frameworks (MOFs) can be designed in silico. Therefore, when making new databases of such hypothetical MOFs, we need to ensure that they not only contribute toward the growth of the count of structures but also add different chemistries to the existing databases. In this study, we designed a database of ∼20,000 hypothetical MOFs, which are diverse in terms of their chemical design space─metal nodes, organic linkers, functional groups, and pore geometries. Using machine learning techniques, we visualized and quantified the diversity of these structures. We find that on adding the structures of our database, the overall diversity metrics of hypothetical databases improve, especially in terms of the chemistry of metal nodes. We then assessed the usefulness of diverse structures by evaluating their performance, using grand-canonical Monte Carlo simulations, in two important environmental applications─post-combustion carbon capture and hydrogen storage. We find that many of these structures perform better than widely used benchmark materials such as Zeolite-13X (for post-combustion carbon capture) and MOF-5 (for hydrogen storage). All the structures developed in this study, and their properties, are provided on the Materials Cloud to encourage further use of these materials for other applications. Benchmark (surveying) ; Database ; Metal-organic framework ; Computer science ; Hydrogen storage ; Chemical space ; Combustion ; Materials science ; Throughput ; Nanotechnology ; Chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Zeolite Catalysis and Synthesis
Authors: Peikun Zheng | R.I. Zubatyuk | Wei Wu | Olexandr Isayev | Pavlo O. Dral
Source: Nature Communications (article, 2021-12-02)
Field: Materials Science — Materials Chemistry

Artificial intelligence-enhanced quantum chemical method with broad applicability High-level quantum mechanical (QM) calculations are indispensable for accurate explanation of natural phenomena on the atomistic level. Their staggering computational cost, however, poses great limitations, which luckily can be lifted to a great extent by exploiting advances in artificial intelligence (AI). Here we introduce the general-purpose, highly transferable artificial intelligence-quantum mechanical method 1 (AIQM1). It approaches the accuracy of the gold-standard coupled cluster QM method with high computational speed of the approximate low-level semiempirical QM methods for the neutral, closed-shell species in the ground state. AIQM1 can provide accurate ground-state energies for diverse organic compounds as well as geometries for even challenging systems such as large conjugated compounds (fullerene C<sub>60</sub>) close to experiment. This opens an opportunity to investigate chemical compounds with previously unattainable speed and accuracy as we demonstrate by determining geometries of polyyne molecules-the task difficult for both experiment and theory. Noteworthy, our method's accuracy is also good for ions and excited-state properties, although the neural network part of AIQM1 was never fitted to these properties. Computer science ; Quantum ; Excited state ; Quantum chemical ; Ground state ; Artificial neural network ; Quantum chemistry ; Molecule ; Statistical physics ; Nanotechnology ; Artificial intelligence ; Materials science ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Catalysis and Oxidation Reactions