jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: James B. Stahley | Mehdi B. Zanjani
Source: ECS Meeting Abstracts (article, 2021-05-30)
Field: Materials Science — Materials Chemistry

(Invited) Thermal Transport in Multi-component Nanoparticle Superstructures Engineering nanoscale structures and interfaces through self-assembly of quantum dots and nanoparticles is a powerful approach in material design and processing. Nanoscale building blocks, with a variety of shapes, sizes, and compositions, can self-assemble into different structures with desirable electronic, photonic, and phononic properties. Recent advances in synthesis and characterization of complex nano-scale units has provided a large library of multi-component building blocks that can significantly expand the current scope of colloidal self-assembly. However, the complexity of these building blocks makes it significantly harder to predict the resulting self-assembled structures and their transport properties. Therefore, developing predictive tools based on computational and machine learning approaches becomes a necessity for successful implementation of new material design procedures. In this work, we utilize a combination of Molecular Dynamics simulations and Machine Learning-based approaches to investigate the self-assembly process of multi-component nanoparticles. We establish a computational framework to help identify feasible multi-component superstructures, and investigate various designs as a function of shape and size of the building blocks. Additionally, our approach transforms the high-dimensional energy landscapes of nanoparticles into low-dimensional parameter spaces by using Manifold Learning techniques. This illustration of the phase space compresses the full energy profile of the system into a single snapshot that contains important information regarding the assembly process and allows the comparison of the behavior of constructs with different geometric features in the same abstract space. This approach also facilitates the discovery of intuitive self-assembly pathways based on the energy footprint of nanoparticles. Furthermore, we develop a computational framework to predict transport properties of the identified multi-component superstructures. Band structure calculations are performed to capture the influence of key parameters such as building block size/shape on the photonic and phononic properties of various structural designs. Finally, we present a Genetic Algorithm (GA)-based platform to implement an inverse material-design process where superstructures with desired transport properties are obtained from the available building blocks. Our results provide new insight into designing multi-component nano-scale structures and will help pave the way for developing powerful tools for material discovery and identification. Computer science ; Component (thermodynamics) ; Nanotechnology ; Distributed computing ; Materials science ; Computational science Thermal properties of materials ; Advanced Physical and Chemical Molecular Interactions ; Chemical and Physical Properties of Materials
Authors: Alberto Fabrizio | Ksenia R. Briling | Clémence Corminbœuf
Source: arXiv (Cornell University) (preprint, 2021-10-25)
Field: Materials Science — Materials Chemistry

SPA$^\mathrm{H}$M: the Spectrum of Approximated Hamiltonian Matrices representations Physics-inspired molecular representations are the cornerstone of similarity-based learning applied to solve chemical problems. Despite their conceptual and mathematical diversity, this class of descriptors shares a common underlying philosophy: they all rely on the molecular information that determines the form of the electronic Schrödinger equation. Existing representations take the most varied forms, from non-linear functions of atom types and positions to atom densities and potential, up to complex quantum chemical objects directly injected into the ML architecture. In this work, we present the Spectrum of Approximated Hamiltonian Matrices (SPA$^\mathrm{H}$M) as an alternative pathway to construct quantum machine learning representations through leveraging the foundation of the electronic Schrödinger equation itself: the electronic Hamiltonian. As the Hamiltonian encodes all quantum chemical information at once, SPA$^\mathrm{H}$M representations not only distinguish different molecules and conformations, but also different spin, charge, and electronic states. As a proof of concept, we focus here on efficient SPA$^\mathrm{H}$M representations built from the eigenvalues of a hierarchy of well-established and readily-evaluated "guess" Hamiltonians. These SPA$^\mathrm{H}$M representations are particularly compact and efficient for kernel evaluation and their complexity is independent of the number of different atom types in the database. Hamiltonian (control theory) ; Invariant (physics) ; Computer science ; Physics ; Artificial intelligence ; Theoretical physics ; Theoretical computer science ; Algorithm ; Mathematics ; Quantum mechanics ; Mathematical optimization Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture ; Computational Drug Discovery Methods
Authors: Felipe J. Villaseñor-Cavazos | Daniel Torres-Valladares | Omar Lozano
Source: arXiv (Cornell University) (preprint, 2021-11-10)
Field: Materials Science — Materials Chemistry

Modelling and optimization of nanovector synthesis for applications in drug delivery systems Nanovectors (NVs), based on nanostructured matter such as nanoparticles (NPs), have proven to perform as excellent drug delivery systems. However, due to the great variety of potential NVs, including NPs materials and their functionalization, in addition to the plethora of molecules that could transport, this fields presents a great challenge in terms of resources to find NVs with the most optimal physicochemical properties such as particle size and drug loading, where most of efforts rely on trial and error experimentation. In this regard, Artificial intelligence (AI) and metaheuristic algorithms offer efficient of the state-of-the-art modelling and optimization, respectively. This review focuses, through a systematic search, on the use of artificial intelligence and metaheuristic algorithms for nanoparticle synthesis in drug delivery systems. The main findings are: neural networks are better at modelling NVs properties than linear regression algorithms and response surface methodology, there is a very limited number of studies comparing AI or metaheuristic algorithm, and there is no information regarding the appropriateness of calculations of the sample size. Based on these findings, multilayer perceptron artificial neural network and adaptive neuro fuzzy inference system were tested for their modelling performance with a NV dataset; finding the latter the better algorithm. For metaheuristic algorithms, benchmark functions were optimized with cuckoo search, firefly algorithm, genetic algorithm and symbiotic organism search; finding cuckoo search and symbiotic organism search with the best performance. Finally, methods to estimate appropriate sample size for AI algorithms are discussed. Cuckoo search ; Metaheuristic ; Computer science ; Artificial neural network ; Parallel metaheuristic ; Artificial intelligence ; Machine learning ; Benchmark (surveying) ; Algorithm ; Particle swarm optimization ; Meta-optimization Nanoparticles: synthesis and applications ; Laser-Ablation Synthesis of Nanoparticles ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Michel Côté | Olivier Malenfant-Thuot | Kevin Ryczko | Arnab Majumdar | Isaac Tamblyn
Source: ECS Meeting Abstracts (article, 2021-05-30)
Field: Materials Science — Materials Chemistry

(Invited) Machine Learned Deep Neural Networks to Simulate Raman Spectrum of Defective Graphene Systems Raman spectrum is a common spectroscopic tool used in materials synthesis and characterization. The width and shift of Raman peaks are commonly used in characterization of graphene samples. In this presentation, I will highlight our efforts in using machine learned models to develop an efficient and accurate methodology for simulating accurate spectroscopic signatures in graphene. We are using deep neural networks trained with density functional theory calculations to take advantage of the accuracy of this approach while being able to simulate large atomic systems corresponding to doped graphene layers. We are then able to evaluate the empirical models presently used to assess the defect levels in graphene and compare them to state-of-the-art calculation on such systems. Graphene ; Raman spectroscopy ; Characterization (materials science) ; Artificial neural network ; Computer science ; Materials science ; Artificial intelligence ; Density functional theory ; Doping ; Nanotechnology ; Physics ; Optoelectronics ; Quantum mechanics Machine Learning in Materials Science ; Graphene research and applications
Authors: Nicodemo Di Pasquale | Joshua D. Elliott | Panagiotis Hadjidoukas | Paola Carbone
Source: arXiv (Cornell University) (preprint, 2021-03-30)
Field: Materials Science — Materials Chemistry

Dynamically polarisable force-fields for surface simulations via multi-output classification Neural Networks We present a general procedure to introduce electronic polarization into classical Molecular Dynamics (MD) force-fields using a Neural Network (NN) model. We apply this framework to the simulation of a solid-liquid interface where the polarization of the surface is essential to correctly capture the main features of the system. By introducing a multi-input, multi-output NN and treating the surface polarization as a discrete classification problem, for which NNs are known to excel, we are able to obtain very good accuracy in terms of quality of predictions. Through the definition of a custom loss function we are able to impose a physically motivated constraint within the NN itself making this model extremely versatile, especially in the modelling of different surface charge states. The NN is validated considering the redistribution of electronic charge density within a graphene based electrode in contact with aqueous electrolyte solution, a system highly relevant to the development of next generation low-cost supercapacitors. We compare the performances of our NN/MD model against Quantum Mechanics/Molecular dynamics simulations where we obtain a most satisfactorily agreement. Artificial neural network ; Computer science ; Polarization (electrochemistry) ; Molecular dynamics ; Statistical physics ; Charge density ; Artificial intelligence ; Physics ; Chemistry ; Quantum mechanics ; Physical chemistry Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Advanced Memory and Neural Computing
Authors: Fabian L. Thiemann | P.N. Rowe | Andrea Zen | Erich A. Müller | Angelos Michaelides
Source: arXiv (Cornell University) (preprint, 2021-07-02)
Field: Materials Science — Materials Chemistry

Defect-Dependent Corrugation in Graphene Graphene's intrinsically corrugated and wrinkled topology fundamentally influences its electronic, mechanical, and chemical properties. Experimental techniques allow the manipulation of pristine graphene and the controlled production of defects which allows one to control the atomic out-of-plane fluctuations and thus tune graphene's properties. Here, we perform large scale machine learning-driven molecular dynamics simulations to understand the impact of defects on the structure of graphene. We find that defects cause significantly higher corrugation leading to a strongly wrinkled surface. The magnitude of this structural transformation strongly depends on the defect concentration and specific type of defect. Analyzing the atomic neighborhood of the defects reveals that the extent of these morphological changes depends on the preferred geometrical orientation and the interactions between defects. While our work highlights that defects can strongly affect graphene's morphology, it also emphasizes the differences between distinct types by linking the global structure to the local environment of the defects. Graphene ; Materials science ; Atomic units ; Molecular dynamics ; Nanotechnology ; Topological defect ; Chemical physics ; Transformation (genetics) ; Morphology (biology) ; Work (physics) ; Crystallographic defect ; Condensed matter physics ; Physics ; Chemistry ; Computational chemistry Graphene research and applications ; Machine Learning in Materials Science ; Advanced Memory and Neural Computing
Authors: Litty V. Thekkekara | Shamini P. Baby | Jeffery C. Chan | Ivan Cole
Source: Advanced Materials Science and Technology (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning for Next-generation Printed Technologies Modern science advances towards the development of lightweight wearable and portable applications for the promotion of human-machine interfaces. Among them, the most beneficial ones include the technologies for healthcare, telecommunications, and energy resources. Recent developments in the additive manufacturing otherwise 3D printing sector are promising for largescale applications. It promotes cost-effective production of technologies like sensors, lab on chips, solar cells, and energy storage. However, these applications' efficiency is lower to technologies fabricated using other methods like chemical approaches due to the non-optimized parameters involved in the fabrication and characterization phases. Machine learning on the other hand expands its science and engineering capabilities. It has a broader opportunity to support 3D printing to develop the potentials and efficiency through effective prediction methods for printing methods and design aspects. In this review, we discuss the use of machine learning prediction algorithms for technologies using 3D printing. 3D printing ; Computer science ; Wearable computer ; Manufacturing engineering ; Wearable technology ; Efficient energy use ; Promotion (chess) ; Systems engineering ; Process engineering ; Nanotechnology ; Engineering ; Embedded system ; Mechanical engineering ; Materials science ; Electrical engineering Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Stephen Xie | Yundi Quan | Ajinkya C. Hire | Laura Fanfarillo | G. R. Stewart | J. J. Hamlin | Richard G. Hennig | P. J. Hirschfeld
Source: arXiv (Cornell University) (preprint, 2021-04-22)
Field: Materials Science — Materials Chemistry

Towards high-throughput superconductor discovery via machine learning Even though superconductivity has been studied intensively for more than a century, the vast majority of superconductivity research today is carried out in nearly the same manner as decades ago. That is, each study tends to focus on only a single material or small subset of materials, and discoveries are made more or less serendipitously. Recent increases in computing power, novel machine learning algorithms, and improved experimental capabilities offer new opportunities to revolutionize superconductor discovery. These will enable the rapid prediction of structures and properties of novel materials in an automated, high-throughput fashion and the efficient experimental testing of these predictions. Here, we review efforts to use machine learning to attain this goal. Throughput ; Superconductivity ; Computer science ; Focus (optics) ; Machine learning ; Artificial intelligence ; Data science ; Nanotechnology ; Materials science ; Physics ; Telecommunications Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Iron-based superconductors research
Authors: П. Д. Брежестовский | Д. Н. Пономарева
Source: Российский физиологический журнал им  И  М  Сеченова (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Фотохромная модуляция Цис-петельных рецептор-управляемых каналов Advances in molecular and cellular biology, the development of chemical synthesis and modern technologies, enriched the modern experimental research with new directions, where the light plays a key role as a tool for modulating biological functions. One of them is photopharmacology, a field that uses chemically synthesized light-controlled compounds that can modulate the functions of proteins. When illuminated at specific wavelengths, these photochromic modules switch between active and inactive states and change functions of receptors, ion channels and enzymes. This review briefly describes compounds that modulate the functions of ionotropic Cys-loop receptors for acetylcholine, GABA, and glycine. The nicotinic acetylcholine receptor (nAChR) is the first receptor-operated channel for which a way of modulation using light-dependent molecules has been discovered. In the 1970s - 80s, blockers and activators of nAChR were created, consisting of azobenzene (light-controlled switch) and agonists. In the current millennium, new compounds have been created to provide light-controlled modulation of nAChR activity. These new photochromes are selective to muscle and neuronal nAChR, and are promising to study the physiological role of nAChRs in the nervous system. An extensive library of photochromic compounds is available for light-controlling of GABA receptor function. Some of them modulate the activity via interaction with the agonist site, the others are light-regulated blockers of chloride-selective ion channels. Recently, the first two photochromic modulators of glycine receptor activity have also been developed. These achievements demonstrate that photopharmacology opens up unique possibilities for remote control of physiological functions, as well as for studying the processes of inhibition and excitation in neural networks and models of neuronal pathologies. Environmental science Photochromic and Fluorescence Chemistry ; bioluminescence and chemiluminescence research ; Radical Photochemical Reactions
Authors: Michael Kilgour | Lena Simine
Source: ChemRxiv (preprint, 2021-05-31)
Field: Materials Science — Materials Chemistry

Inside the Black Box: A Physical Basis for the Effectiveness of Deep Generative Models of Amorphous Materials &lt;p&gt;We have recently demonstrated an effective protocol for the simulation of amorphous molecular configurations using the PixelCNN generative model (J. Phys. Chem. Lett. 2020, 11, 20, 8532). The morphological sampling of amorphous materials via such an autoregressive generation protocol sidesteps the high computational costs associated with simulating amorphous materials at scale, enabling practically unlimited structural sampling based on only small-scale experimental or computational training samples. An important question raised but not rigorously addressed in that report was whether this machine learning approach could be considered a physical simulation in the conventional sense. Here we answer this question by detailing the inner workings of the underlying algorithm that we refer to as the Morphological Autoregression Protocol or MAP. &lt;br&gt;&lt;/p&gt; Amorphous solid ; Autoregressive model ; Generative grammar ; Protocol (science) ; Scale (ratio) ; Sampling (signal processing) ; Computer science ; Black box ; Artificial intelligence ; Algorithm ; Mathematics ; Chemistry ; Econometrics ; Medicine ; Cartography ; Computer vision ; Geography Machine Learning in Materials Science
Authors: Christian Emeka Okafor
Source: SSRN Electronic Journal (article, 2021-05-07)
Field: Materials Science — Polymers and Plastics

Evaluation of Machine Learning Methods in Predicting Optimum Tensile Strength of Microwave Post-Cured Composite Tailored for Weight-Sensitive Applications The study evaluated the performance of various machine learning methods in predicting tensile strength of microwave post-cured composite tailored for weight-sensitive applications. Using forty six training data pairs from the Box-Behnken design plan, an Adaptive Network-based Fuzzy Inference System (ANFIS) and Artificial Neural Networks (ANN) were built to predict the optimum tensile strength of polyurethane wood ash composite. Numerical optimization was done using the graft of ANFIS-MOGA method. The process control factors considered were particle size, curing time, power level, volume fraction and curing angle. The predictive accuracy of the evaluated machine learning methods were assessed using Coefficient of Determination (R2), Root Mean Square Error (RMSE), Mean Average Error (MAE) and Standard Error of Prediction (SEP). RSM (RMSE=0.0339, MAE=0.0002, SEP=0.0295, R2=0.994) and ANFIS (RMSE=0.0307, MAE=0.0098, SEP=0.0267,R2=0.995) models gave higher degree of accuracy than ANN model (RMSE=0.0827, MAE=0.0124, SEP=0.0719,R2=0.988). The optimization exercise gave optimal tensile strength of 2.27 MPa at optimum process setting of 177µm particle size, 33 min of curing time, power level at 411 Watt, 42 % volume fraction and 110 curing angle. Complementary trial at the named optimum process setting conveyed workable results. Furthermore, selecting microwave post-cured composite for weight-sensitive applications was justified considering that the desirability factor for polyurethane (42 % wood ash) is fairly higher than other material in the same class, this points to the fact that deployment of the microwave post-cured composite in weigh-sensitive applications could benefit weight reduction. Mean squared error ; Ultimate tensile strength ; Composite number ; Adaptive neuro fuzzy inference system ; Artificial neural network ; Materials science ; Curing (chemistry) ; Coefficient of determination ; Composite material ; Particle swarm optimization ; Machine learning ; Mathematics ; Algorithm ; Computer science ; Fuzzy logic ; Artificial intelligence ; Statistics ; Fuzzy control system Natural Fiber Reinforced Composites ; Textile materials and evaluations ; Tribology and Wear Analysis
Authors: Sergey K. Savelyev | Kirill V. Mitrofanov | Artem Karpov
Source:  (article, 2021-10-27)
Field: Chemistry — Inorganic Chemistry

MACHINE LEARNING THE BAND GAP OF METAL-ORGANIC FRAMEWORKS This work presents building and study of physico-chemically interpretable machine learning models fit for metal-organic frameworks’ band gap prediction. Computer science ; Work (physics) ; Artificial intelligence ; Machine learning ; Deep learning ; Engineering ; Mechanical engineering Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Yu Kitamura | Emi Terado | Zechen Zhang | Hirofumi Yoshikawa | Tomoko Inose | Hiroshi Uji‐i | Masaharu Tanimizu | Akihiro Inokuchi | Yoshinobu Kamakura | Daisuke Tanaka
Source:  (preprint, 2021-07-02)
Field: Chemistry — Inorganic Chemistry

Failure-experiment-supported optimization of poorly reproducible synthetic conditions for novel lanthanide metal-organic frameworks with two-dimensional secondary building units A series of novel metal organic frameworks with lanthanide double-layer-based inorganic subnetworks (KGF-3) was synthesized assisted by machine learning. Pure KGF-3 was difficult to isolate in the initial screening experiments. The synthetic conditions were successfully optimized by extracting the dominant factors for KGF-3 synthesis using two machine-learning techniques. Cluster analysis was used to classify the obtained PXRD patterns of the products and to decide automatically whether the experiments were successful or had failed. Decision tree analysis was used to visualize the experimental results, with the factors that mainly affected the synthetic reproducibility being extracted. The water adsorption isotherm revealed that KGF-3 possesses unique hydrophilic pores, and impedance measurements demonstrated good proton conductivities (σ = 5.2 × 10&lt;sup&gt;−4&lt;/sup&gt; S cm&lt;sup&gt;−1&lt;/sup&gt; for KGF-3(Y)) at a high temperature (363 K) and high relative humidity (95%).&lt;br /&gt; Lanthanide ; Metal-organic framework ; Adsorption ; Materials science ; Relative humidity ; Reproducibility ; Metal ; Analytical Chemistry (journal) ; Chemistry ; Chromatography ; Organic chemistry ; Thermodynamics ; Metallurgy Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Organic and Molecular Conductors Research