jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Chinonso Ugwumadu | R. Thapa | Y. Al-Majali | J. Trembly | D. A. Drabold
Source: arXiv (Cornell University) (preprint, 2022-10-10)
Field: Materials Science — Materials Chemistry

Formation of amorphous carbon multi-walled nanotubes from random initial configurations Amorphous carbon nanotubes (a-CNT) with up to four walls and sizes ranging from 200 to 3200 atoms have been simulated, starting from initial random configurations and using the Gaussian Approximation Potential [Phys. Rev. B 95, 094203 (2017)]. The important variables (like density, height, and diameter) required to successfully simulate a-CNTs, were predicted with a machine learning random forest technique. The models were validated $\textit{ex post facto}$ using density functional codes. The a-CNT models ranged from 0.55 nm - 2 nm wide with an average inter-wall spacing of 0.31 nm. The topological defects in a-CNTs were discussed and new defect configurations were observed. The electronic density of states and localization in these phases were discussed and delocalized electrons in the $π$ subspace were identified as an important factor for inter-layer cohesion. Spatial projection of the electronic conductivity favors axial transport along connecting hexagons, while non-hexagonal parts of the network either hinder or bifurcate the electronic transport. A vibrational density of states was calculated and is potentially an experimentally testable fingerprint of the material and the appearance of a low-frequency radial breathing mode was discussed. The thermal conductivity at 300 K was calculated using the Green-Kubo formula. Materials science ; Carbon nanotube ; Delocalized electron ; Amorphous solid ; Gaussian ; Subspace topology ; Density of states ; Molecular physics ; Condensed matter physics ; Nanotechnology ; Physics ; Mathematics ; Mathematical analysis ; Crystallography ; Chemistry ; Quantum mechanics Carbon Nanotubes in Composites ; Machine Learning in Materials Science ; Graphene research and applications
Authors: Stephan Thaler | Maximilian Stupp | Julija Zavadlav
Source: arXiv (Cornell University) (preprint, 2022-08-22)
Field: Materials Science — Materials Chemistry

Deep Coarse-grained Potentials via Relative Entropy Minimization Neural network (NN) potentials are a natural choice for coarse-grained (CG) models. Their many-body capacity allows highly accurate approximations of the potential of mean force, promising CG simulations at unprecedented accuracy. CG NN potentials trained bottom-up via force matching (FM), however, suffer from finite data effects: They rely on prior potentials for physically sound predictions outside the training data domain and the corresponding free energy surface is sensitive to errors in transition regions. The standard alternative to FM for classical potentials is relative entropy (RE) minimization, which has not yet been applied to NN potentials. In this work, we demonstrate for benchmark problems of liquid water and alanine dipeptide that RE training is more data efficient due to accessing the CG distribution during training, resulting in improved free energy surfaces and reduced sensitivity to prior potentials. In addition, RE learns to correct time integration errors, allowing larger time steps in CG molecular dynamics simulation while maintaining accuracy. Thus, our findings support the use of training objectives beyond FM as a promising direction for improving CG NN potential accuracy and reliability. Computer science ; Minification ; Artificial neural network ; Benchmark (surveying) ; Energy minimization ; Entropy (arrow of time) ; Kullback–Leibler divergence ; Training set ; Algorithm ; Artificial intelligence ; Physics ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Protein Structure and Dynamics ; Quantum, superfluid, helium dynamics
Authors: Laura E. Ratcliff | Takayoshi Oshima | Felix Nippert | Benjamin M. Janzen | Elias Kluth | R. Goldhahn | Martin Feneberg | Piero Mazzolini | Oliver Bierwagen | Charlotte Wouters | Musbah Nofal | M. Albrecht | Jack E. N. Swallow | Leanne A. H. Jones | P. Thakur | Tien‐Lin Lee | Curran Kalha | Christoph Schlueter | T. D. Veal | Joel B. Varley | Markus Robert Wagner | Anna Regoutz
Source: arXiv (Cornell University) (preprint, 2022-05-09)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Tackling Disorder in $γ$-Ga$_2$O$_3$ Ga$_2$O$_3$ and its polymorphs are attracting increasing attention. The rich structural space of polymorphic oxide systems such as Ga$_2$O$_3$ offers potential for electronic structure engineering, which is of particular interest for a range of applications, such as power electronics. $γ$-Ga$_2$O$_3$ presents a particular challenge across synthesis, characterisation, and theory due to its inherent disorder and resulting complex structure -- electronic structure relationship. Here, density functional theory is used in combination with a machine learning approach to screen nearly one million potential structures, thereby developing a robust atomistic model of the $γ$-phase. Theoretical results are compared with surface and bulk sensitive soft and hard X-ray photoelectron spectroscopy, X-ray absorption spectroscopy, spectroscopic ellipsometry, and photoluminescence excitation spectroscopy experiments representative of the occupied and unoccupied states of $γ$-Ga$_2$O$_3$. The first onset of strong absorption at room temperature is found at 5.1 eV from spectroscopic ellipsometry, which agrees well with the excitation maximum at 5.17 eV obtained by PLE spectroscopy, where the latter shifts to 5.33 eV at 5 K. This work presents a leap forward in the treatment of complex, disordered oxides and is a crucial step towards exploring how their electronic structure can be understood in terms of local coordination and overall structure. X-ray photoelectron spectroscopy ; Electronic structure ; Density functional theory ; Spectroscopy ; Absorption spectroscopy ; Materials science ; Absorption (acoustics) ; Photoluminescence ; Chemical physics ; Ellipsometry ; Excitation ; Condensed matter physics ; Chemistry ; Nanotechnology ; Optoelectronics ; Computational chemistry ; Physics ; Thin film ; Optics ; Nuclear magnetic resonance Ga2O3 and related materials ; Machine Learning in Materials Science ; Advanced Photocatalysis Techniques
Source: Focus on Powder Coatings (article, 2022-12-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Study confirms - hygienic coatings prevent the spread of bacteria safely Supercapacitor ; Graphene ; Materials science ; Electrode ; Capacitance ; Oxide ; Electrochemistry ; Chemical engineering ; Nanotechnology ; Microstructure ; Composite material ; Chemistry ; Metallurgy Supercapacitor Materials and Fabrication ; Conducting polymers and applications ; Electrochemical sensors and biosensors
Authors: Farzaneh Marahel
Source: SHILAP Revista de lepidopterología (article, 2022-12-01)
Field: Chemistry — Analytical Chemistry

Designed a Spectrophotometric Method for the Determination of Tartrazine Residual in Different Drinks and Foodstuffs by Using Mandarin leaves-capped Gold Nanoparticles Tartrazine color is a synthetic organic food dye that can be found in common food products such as bakery products, dairy products, candies, and beverages, the presence and content of tartrazine color must be controlled in food products due to their potential harmfulness to human beings. Although the liquid chromatographic, and other methods for tartrazine color has advantages such as excellent accuracy and reproducibility, it has limitations such as long-time measurement, and high equipment costs. In this study, to determine tartrazine color in the solution we used a prepared from Mandarin Leaves-capped AuNPs sensor and kinetic spectrophotometric method. The calibration curve was linear in the range of (0.02 to 12.0 µg/L). The standard deviation of (3.0%), and detection limit of the method (0.02 µg/L in time 7 min, 385 nm) were obtained for sensor level response Mandarin Leaves-capped AuNPs with (95%) confidence evaluated. The observed outcomes confirmed the very low detection limit for measuring the tartrazine color in food samples. The artificial neural network model was used as a tool very low for determining mean square error (MSE = 0.515) for tartrazine color by Mandarin Leaves-capped AuNPs sensor. The chemical Mandarin Leaves-capped AuNPs sensor made it possible as an excellent sensor with reproducibility. Tartrazine ; Detection limit ; Mandarin Chinese ; Calibration curve ; Residual ; Colloidal gold ; Food products ; Food additive Dye analysis and toxicity ; Saffron Plant Research Studies ; Electrochemical sensors and biosensors
Authors: Ioannis Tsaknakis | Bhavya Kailkhura | Sijia Liu | Donald Loveland | James Diffenderfer | Anna M. Hiszpanski | Mingyi Hong
Source: arXiv (Cornell University) (preprint, 2022-06-04)
Field: Materials Science — Materials Chemistry

Zeroth-Order SciML: Non-intrusive Integration of Scientific Software with Deep Learning Using deep learning (DL) to accelerate and/or improve scientific workflows can yield discoveries that are otherwise impossible. Unfortunately, DL models have yielded limited success in complex scientific domains due to large data requirements. In this work, we propose to overcome this issue by integrating the abundance of scientific knowledge sources (SKS) with the DL training process. Existing knowledge integration approaches are limited to using differentiable knowledge source to be compatible with first-order DL training paradigm. In contrast, our proposed approach treats knowledge source as a black-box in turn allowing to integrate virtually any knowledge source. To enable an end-to-end training of SKS-coupled-DL, we propose to use zeroth-order optimization (ZOO) based gradient-free training schemes, which is non-intrusive, i.e., does not require making any changes to the SKS. We evaluate the performance of our ZOO training scheme on two real-world material science applications. We show that proposed scheme is able to effectively integrate scientific knowledge with DL training and is able to outperform purely data-driven model for data-limited scientific applications. We also discuss some limitations of the proposed method and mention potentially worthwhile future directions. Workflow ; Computer science ; Process (computing) ; Scheme (mathematics) ; Data science ; Deep learning ; Software ; Order (exchange) ; Artificial intelligence ; Programming language ; Mathematics Machine Learning in Materials Science ; Machine Learning and Data Classification ; Scientific Computing and Data Management
Authors: Elke Pahl
Source:  (preprint, 2022-09-22)
Field: Materials Science — Materials Chemistry

Exploring Potential Energy Surfaces: Development of a Tool Kit in Programming Language Julia We are interested in investigating the thermodynamic behaviour, in particular melting, of nano-clusters and bulk materials by computer simulations. As basic ingredients the knowledge of the complex potential energy surfaces (PES) for these systems is needed. During Monte Carlo simulations the PES are explored allowing us to extract the thermodynamic properties of interest like the internal energy or heat capacity. Systems explored at present include neon in strong magnetic fields, small gallium and copper/brass clusters. The PES of these systems are computed using a dimer expansion of the energy, density-functional calculations and atomistic machine-learning techniques, respectively. Further, we are interested in understanding the PES better to get insight into the melting mechanisms - here cluster analysis tools and basin-hopping algorithms are explored. The diversity of the applications means that we have to be able to deal with different potential energy models, boundary conditions and statistical ensembles in our Monte Carlo implementation. Our goal is to write a comprehensive computer program that allows for straight-forward extension to new systems and for easy method development. For this reason we decided to switch from a Fortran implementation to the relatively modern programming language Julia. In particular, the intricate type system and multiple dispatch are helpful in achieving the goal of a single, but flexible code while maintaining the possibility to write fast code due to just-in-time compilation. Computer science ; Fortran ; Monte Carlo method ; Computational science ; Energy (signal processing) ; Software ; Statistical physics ; Theoretical computer science ; Programming language ; Physics ; Mathematics Machine Learning in Materials Science ; Theoretical and Computational Physics ; Advanced Physical and Chemical Molecular Interactions
Authors: Oliver G. Nicholls | Dillion Frost | Vidur Tuli | J. Smutna | M.R. Wenman | Patrick A. Burr
Source: arXiv (Cornell University) (preprint, 2022-07-07)
Field: Materials Science — Materials Chemistry

Transferability of Zr-Zr interatomic potentials Tens of Zr inter-atomic potentials (force fields) have been developed to enable atomic-scale simulations of Zr alloys. These can provide critical insight in the in-reactor behaviour of nuclear fuel cladding and structural components exposed, but the results are strongly sensitive to the choice of potential. We provide a comprehensive comparison of 13 popular Zr potentials, and assess their ability to reproduce key physical, mechanical, structural and thermodynamic properties of Zr. We assess the lattice parameters, thermal expansion, melting point, volume-energy response, allotropic phase stability, elastic properties, and point defect energies, and compare them to experimental and ab-initio values. No potential was found to outperform all others on all aspects, but for every metric considered here, at least one potential was found to provide reliable results. Older embedded-atom method (EAM) potentials tend to excel in 2-3 metrics each, but at the cost of poorer transferability. The two highest-performing potentials overall, with complementary strengths and weaknesses, were the 2021 angular-dependent potential of Smirnova and Starikov (Comp. Mater. Sci. 197, 110581) and the 2019 embedded-atom method potential of Wimmer et al (J. Nucl. Mater. 532, 152055). All potentials trained through machine learning algorithms proved to have lower overall accuracy, and less transferability, than simpler and computationally faster potentials available. Point defect structures and energies is where the greatest divergence and least accuracy is observed. We created maps that will help modellers select the most suitable potential for a specific application, and which may help identify areas of improvement in future potentials. Transferability ; Interatomic potential ; Lattice (music) ; Atom (system on chip) ; Melting point ; Materials science ; Computer science ; Atomic units ; Metric (unit) ; Potential energy ; Statistical physics ; Molecular dynamics ; Atomic physics ; Physics ; Computational chemistry ; Machine learning ; Chemistry Nuclear Materials and Properties ; Machine Learning in Materials Science ; Nuclear reactor physics and engineering
Authors: Rishi E. Kumar | Moses Kodur | Arun Kumar Mannodi Kannakithodi | David P. Fenning
Source: 2022 IEEE 49th Photovoltaics Specialists Conference (PVSC) (article, 2022-06-05)
Field: Materials Science — Materials Chemistry

No Time to Waste: Quickly Optimizing Perovskite Composition with Off-the-Shelf Active Learning Methods The compositional flexibility of halide perovskite presents both opportunity for discovery of tailored materials and risk of extraneous effort. Active learning - a class of machine learning methods that alternates between modeling a response surface and suggesting interesting points to be tested next - enables optimization of a system while testing just a small fraction of the total search space, and can alleviate the cost of large compositional searches. We demonstrate that a single generic active learning algorithm is effective across five typical tasks in halide perovskite development under the three research paradigms of hand-done, high-throughput robotic, and computational experiments. These tasks involve tuning the composition at one, two, or all three of the A,B, and X sites. Moving beyond generic active learning, representing ABX3 compositions by their physical properties at each site – which, in practice, entails simply looking up and averaging tabulated values – further improves the rate of composition optimization by 1-1.4x. The experimental budget of a typical successful active learning optimization was about 15 samples – well within the budget of tedious manual experiments, and a drop in the bucket for high-throughput automated or computational studies. Our findings resonate with the growing body of active learning demonstrations across the scientific literature, and advocate for the integration of active learning into composition optimization efforts in halide perovskites. Active learning (machine learning) ; Flexibility (engineering) ; Computer science ; Throughput ; Halide ; Perovskite (structure) ; Active layer ; Machine learning ; Artificial intelligence ; Materials science ; Nanotechnology ; Chemistry ; Engineering ; Mathematics ; Chemical engineering ; Layer (electronics) Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Ferroelectric and Negative Capacitance Devices
Authors: Kithmi Gunawardana | M.P.N. Deshan | M.G.S.P. Hemachandra | Devanshi Ganegoda | Nisansala Hettiarachchi | Lokesha Weerasinghe
Source:  (article, 2022-12-09)
Field: Chemistry — Inorganic Chemistry

Cocopal - A Deep Learning Based Intelligent System to Certify and Standardize the Quality of Coconut Based Products The procedure of certifying and standardizing the quality of the coconut-based products is done manually in Sri Lanka at precent. It is a time consuming and labor-intensive task and is conducted by experts. In most cases, the quality is decided solely by visual inspections by buyers and suppliers, with no scientific basis. The paper reports the capacity of bringing modern technology solutions such as Artificial Intelligence (AI), Machine Learning (ML), Image Processing (IP), and decentralized storage to aid in the certification and standardization of the quality of raw materials.Results showed that the accuracy of the proposed system is in the 86% to 90% range and showed that this technique could beimproved and used as an alternative to manual techniques. Certification ; Standardization ; Quality (philosophy) ; Computer science ; Task (project management) ; Manufacturing engineering ; Engineering management ; Artificial intelligence ; Engineering ; Systems engineering ; Operating system ; Management Coconut Research and Applications ; Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI
Authors: Kirill Shmilovich | Marc Stieffenhofer | Nicholas E. Charron | Moritz Hoffmann
Source: arXiv (Cornell University) (preprint, 2022-05-11)
Field: Materials Science — Materials Chemistry

Temporally coherent backmapping of molecular trajectories from coarse-grained to atomistic resolution Coarse-graining offers a means to extend the achievable time and length scales of molecular dynamics simulations beyond what is practically possible in the atomistic regime. Sampling molecular configurations of interest can be done efficiently using coarse-grained simulations, from which meaningful physicochemical information can be inferred if the corresponding all-atom configurations are reconstructed. However, this procedure of backmapping to reintroduce the lost atomistic detail into coarse-grain structures has proven a challenging task due to the many feasible atomistic configurations that can be associated with one coarse-grain structure. Existing backmapping methods are strictly frame-based, relying on either heuristics to replace coarse-grain particles with atomic fragments and subsequent relaxation, or parameterized models to propose atomic coordinates separately and independently for each coarse-grain structure. These approaches neglect information from previous trajectory frames that is critical to ensuring temporal coherence of the backmapped trajectory, while also offering information potentially helpful to produce higher-fidelity atomic reconstructions. In this work we present a deep learning-enabled data-driven approach for temporally coherent backmapping that explicitly incorporates information from preceding trajectory structures. Our method trains a conditional variational autoencoder to non-deterministically reconstruct atomistic detail conditioned on both the target coarse-grain configuration and the previously reconstructed atomistic configuration. We demonstrate our backmapping approach on two exemplar biomolecular systems: alanine dipeptide and the miniprotein chignolin. We show that our backmapped trajectories accurately recover the structural, thermodynamic, and kinetic properties of the atomistic trajectory data. Granularity ; Statistical physics ; Computer science ; Trajectory ; Algorithm ; Molecular dynamics ; Physics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Enzyme Structure and Function
Source: Indian Journal of Ecology (article, 2022-08-09)
Field: Materials Science — Materials Chemistry

Comparison of ANN &amp; RSM Approaches for Optimum Production of -Decalactone Decalactone is an important flavor compound widely used in food, diary and fragrances.Previously it was produced from fruits γ through chemical synthesis.Due to increase in demand for natural products by consumers, it has gained interest for its production through biotechnological way.The present study focusses on fermentation variables optimization for the production of -decalactone using γ Sporidiobolus salmonicolor via Response surface methodologies (RSM) and Artificial neural networks (ANN) using castor oil as substrate.The prediction abilities of RSM and ANN were compared based on error parameters namely Mean absolute error (MAE), Root mean square error (RMSE), chi-square (χ ) and correlation coefficient (R ) to suggest the best approach for modeling.The response variable (γ-decalactone 2 2 production) was modelled and optimized as a function of four input variables (castor oil percentage, pH, incubation time and temperature).Training of ANN network was performed using a multilayer feed forward architecture with same experimental datasets used in RSM.Model predictions of both approaches were compared with the experimental values and reported that these are in good close agreement.The highest production of -decalactone 72.73 mg/l obtained at an optimum conditions of castor oil -29.68 %, pH -5.32, incubation time -99.89 h and γ temperature -23.22 C. Hence, results were beneficial in using appropriately trained ANN over RSM for nonlinear fermentation systems. Production (economics) ; Response surface methodology ; Biochemical engineering ; Pulp and paper industry ; Biological system ; Chemistry ; Engineering ; Economics ; Chromatography ; Microeconomics ; Biology Mesoporous Materials and Catalysis