jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Dalia Yablon | Ishita Chakraborty | Hillary Passino | Krishnan Iyer | Antonios Doufas | Maksim Shivokhin | Hasmukh Patel | Kenneth Johnson | Roland Martinez | Peter Boul
Source: MRS Bulletin (article, 2021-11-24)
Field: Materials Science — Materials Chemistry

Journal Highlights The use of artifi cial intelligence and machine learning is becoming prevalent in a number of materials development and analytical methods. In atomic force microscopy imaging, a deep learning model based on a convolutional neural net has been developed for microstructure correlation with various bulk mechanical properties. This should enable higher throughput methods of analysis and interpretation, leading to faster decision-making timelines. Engineering ethics ; Engineering Machine Learning in Materials Science ; Force Microscopy Techniques and Applications ; Advanced Electron Microscopy Techniques and Applications
Authors: Jon Eunan Quinlivan Domínguez | Konstantin M. Neyman | Albert Bruix
Source: ChemRxiv (preprint, 2021-10-19)
Field: Materials Science — Materials Chemistry

Stability of oxidized states of ceria-supported PtOx particles under a wide range of gas-phase conditions Nanostructured materials based on non-inert oxides CeO2 and PtyOx play a fundamental role in catalyst design. However, their characterization is often challenging due to their structural complexity and the tendency of the materials to change under reaction conditions. In this work, we combine calculations based on the density functional theory, a machine-learning assisted global optimization method (GOFEE) and ab initio thermodynamics to characterize stable oxidation states of ceria-supported PtyOx clusters in different environments. The collection of global minima for different stoichiometries resulting from the global optimisation effort is used to assess the effect of temperature, oxygen pressure, and support interactions on the phase diagrams, oxidation states, and structural properties of the PtyOx particles. We thus identify favoured structural motifs and O/Pt ratios, revealing that oxidized states of ceria-supported particles are more stable than reduced ones under a wide range of conditions. These results indicate that studies rationalizing activity of ceria-supported Pt clusters must consider such oxidized states, and that previous understanding of such materials obtained only with fully reduced Pt clusters may be incomplete. Stoichiometry ; Materials science ; Density functional theory ; Catalysis ; Maxima and minima ; Work (physics) ; Ab initio ; Characterization (materials science) ; Phase (matter) ; Chemical physics ; Range (aeronautics) ; Thermodynamics ; Computational chemistry ; Nanotechnology ; Physical chemistry ; Chemistry ; Physics ; Mathematics Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Electrocatalysts for Energy Conversion
Authors: Tarek Iraki | Lukas Morand | Johannes Dornheim | Norbert Link | Dirk Helm
Source: arXiv (Cornell University) (preprint, 2021-01-01)
Field: Materials Science — Materials Chemistry

A multi-task learning-based optimization approach for finding diverse sets of material microstructures with desired properties and its application to texture optimization The optimization along the chain processing-structure-properties-performance is one of the core objectives in data-driven materials science. In this sense, processes are supposed to manufacture workpieces with targeted material microstructures. These microstructures are defined by the material properties of interest and identifying them is a question of materials design. In the present paper, we addresse this issue and introduce a generic multi-task learning-based optimization approach. The approach enables the identification of sets of highly diverse microstructures for given desired properties and corresponding tolerances. Basically, the approach consists of an optimization algorithm that interacts with a machine learning model that combines multi-task learning with siamese neural networks. The resulting model (1) relates microstructures and properties, (2) estimates the likelihood of a microstructure of being producible, and (3) performs a distance preserving microstructure feature extraction in order to generate a lower dimensional latent feature space to enable efficient optimization. The proposed approach is applied on a crystallographic texture optimization problem for rolled steel sheets given desired properties. Microstructure ; Computer science ; Task (project management) ; Feature (linguistics) ; Artificial intelligence ; Optimization problem ; Texture (cosmology) ; Identification (biology) ; Machine learning ; Pattern recognition (psychology) ; Algorithm ; Materials science ; Image (mathematics) ; Engineering Machine Learning in Materials Science ; Industrial Vision Systems and Defect Detection ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Giannis Kostaras
Source: Gothenburg University Publications Electronic Archive (Gothenburg University) (article, 2021-01-26)
Field: Materials Science — Materials Chemistry

Opto-vibrational coupling in molecular solar thermal storage materials: Electronic structure calculations and neural-networkbased analysis Giannis Kostaras Degree project Molecular solar thermal storage materials are proposed as a clean, renewable energy\nsolution for a world with ever increasing energy needs. Norbornadiene is an\norganic compound suitable for molecular solar thermal storage systems. Computational\nmethods such as density functional theory offer solutions for improvement of\nnorbornadiene-based molecular solar thermal storage systems via theoretical spectroscopy.\nMachine learning methods, such as artificial neural networks may offer\nuseful insights to improve theoretical spectroscopy methods. Degree (music) ; Coupling (piping) ; Thermal ; Materials science ; Thermal analysis ; Physics ; Thermodynamics ; Composite material ; Acoustics Phase-change materials and chalcogenides ; Photochemistry and Electron Transfer Studies ; Perovskite Materials and Applications
Authors: Hanmin Liu | Jie Shen | Hua Xiang
Source: AIP Advances (article, 2021-08-01)
Field: Materials Science — Materials Chemistry

Hydrogen generation from methanol with network efficient power control for wireless communication system This paper presents a numerical and experimental study on the performance of a methanol steam reformer integrated with a hydrogen/air combustion reactor for hydrogen production. The steam methane reforming reaction mechanism essential for the rational design of catalysts for efficient hydrogen production was elucidated. The development of non-precious metal hydrogen evolution catalysts to replace platinum is crucial for efficient hydrogen production. The simulation showed that deep neural network algorithms for fluid-structure topology optimization could improve the control effect. Meanwhile, the system can run stably under conditions close to the theoretical optimum with the reformer, non-precious metal catalysts, and neural network methanol flow control. Hydrogen production ; Steam reforming ; Hydrogen ; Methanol ; Catalysis ; Methane reformer ; Methane ; Chemical engineering ; Combustion ; Materials science ; Platinum ; Process engineering ; Computer science ; Chemistry ; Engineering ; Organic chemistry Catalytic Processes in Materials Science ; Catalysts for Methane Reforming ; Electrocatalysts for Energy Conversion
Authors: Sam Lemonick﻿
Source: C&EN Global Enterprise (article, 2021-12-20)
Field: Materials Science — Materials Chemistry

Machine learning solves a long-standing DFT problem Chemists use density functional theory (DFT) methods, approximations of the exact distribution of a molecule’s electrons, to make accurate and computationally efficient models of molecules and materials. But there are well-known circumstances in which DFT tools fail, such as predicting how atoms share an electron’s charge. Errors arise because DFT equations are scientists’ approximations of physical reality. Researchers associated with Alphabet’s DeepMind machine-learning project say their neural network eliminates that electron error and makes predictions that are more accurate than those of traditional DFT methods ( Science 2021, DOI: 10.1126/science.abj6511 ). The group trained its neural network on data about the properties of small, main-group models and on imaginary systems that contain fractions of electrons that previous work had theorized might help resolve the familiar DFT failures ( Science 2008, DOI: 10.1126/science.115872 ). They then used the resulting neural network, called DeepMind21 (DM21) to predict electron distributions for test molecules. Artificial neural network ; Density functional theory ; Computer science ; Electron ; Alphabet ; Statistical physics ; Artificial intelligence ; Machine learning ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Various Chemistry Research Topics ; Computational Drug Discovery Methods
Authors: José J. Plata | Víctor Posligua | Antonio M. Márquez | Javier Fdez. Sanz | Ricardo Grau‐Crespo
Source: ChemRxiv (preprint, 2021-09-17)
Field: Materials Science — Materials Chemistry

Fast, accurate and non-empirical determination of the lattice thermal conductivities of I-III-VI2 chalcopyrite semiconductors The use of computer simulation to predict the lattice thermal conductivity of materials has the potential to accelerate the discovery of new thermoelectric materials. However, the accurate prediction of this property from first principles, without input from experiment, is very computationally demanding, which limits the use of high-throughput strategies in thermoelectric materials design. We present here an accurate, fast, and non-empirical determination of the lattice thermal conductivities of a large family of semiconductors, with composition ABX2 (I-III-VI2), with A=Cu, Ag; B=Al, Ga, In, Tl; and X=S, Se, Te. We solve the Boltzmann transport equation with force constants derived from density functional theory calculations and machine-learning-based regression algorithms, reducing between one and two orders of magnitude the computational cost with respect to conventional approaches of the same accuracy. The results are in good agreement with available experimental data and allow us to rationalize the role of chemical composition, temperature and nanostructuring on the thermal conductivities across this important family of semiconductors. Thermoelectric effect ; Semiconductor ; Thermal conductivity ; Lattice (music) ; Thermal ; Density functional theory ; Materials science ; Lattice Boltzmann methods ; Computation ; Thermodynamics ; Lattice constant ; Chalcopyrite ; Condensed matter physics ; Statistical physics ; Computational physics ; Computer science ; Chemistry ; Computational chemistry ; Physics ; Algorithm ; Optoelectronics ; Quantum mechanics ; Metallurgy Advanced Thermoelectric Materials and Devices ; Chalcogenide Semiconductor Thin Films ; Machine Learning in Materials Science
Authors: James Damewood | Daniel Schwalbe‐Koda | Rafael Gómez‐Bombarelli
Source: arXiv (Cornell University) (preprint, 2021-07-11)
Field: Materials Science — Materials Chemistry

Sampling Lattices in Semi-Grand Canonical Ensemble with Autoregressive Machine Learning Calculating thermodynamic potentials and observables efficiently and accurately is key for the application of statistical mechanics simulations to materials science. However, naive Monte Carlo approaches, on which such calculations are often dependent, struggle to scale to complex materials in many state-of-the-art disciplines such as the design of high entropy alloys or multicomponent catalysts. To address this issue, we adapt sampling tools built upon machine-learning based generative modeling to the materials space by transforming them into the semi-grand canonical ensemble. Furthermore, we show that the resulting models are transferable across wide-ranges of thermodynamic conditions and can be implemented with any internal energy model U, allowing integration into many existing materials workflows. We demonstrate the applicability of this approach to the simulation of benchmark systems (AgPd, CuAu) that exhibit diverse thermodynamic behavior in their phase diagrams. Finally, we discuss remaining challenges in model development and promising research directions for future improvements. Computer science ; Statistical ensemble ; Benchmark (surveying) ; Statistical physics ; Canonical ensemble ; Statistical mechanics ; Grand canonical ensemble ; Observable ; Ensemble learning ; Monte Carlo method ; Entropy (arrow of time) ; Machine learning ; Workflow ; Sampling (signal processing) ; Key (lock) ; Artificial intelligence ; Physics ; Mathematics ; Thermodynamics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Advanced Electron Microscopy Techniques and Applications
Authors: Bao N. Nguyen
Source: ePrints Soton (University of Southampton) (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

AI3SD Video: Machine Learning with Causality: Solubility Prediction in Organic Solvents and Water Solubility prediction remains a critical challenge in drug development, synthetic route and chemical process design, extraction and crystallisation. Here we report a successful approach to solubility prediction in organic solvents and water using a combination of machine learning (ANN, SVM, RF, ExtraTrees, Bagging and GP) and computational chemistry. Rational interpretation of dissolution process into a numerical problem led to a small set of selected descriptors and subsequent predictions which are independent of the applied machine learning method. These models gave significantly more accurate predictions compared to benchmarked open-access and commercial tools, achieving accuracy close to the expected level of noise in training data (LogSâÂ±â0.7). Finally, they reproduced physicochemical relationship between solubility and molecular properties in different solvents, which led to rational approaches to improve the accuracy of each models. Solubility ; Machine learning ; Artificial intelligence ; Computer science ; Support vector machine ; Process (computing) ; Dissolution ; COSMO-RS ; Chemistry ; Organic chemistry Crystallization and Solubility Studies ; Computational Drug Discovery Methods ; Analytical Chemistry and Chromatography
Authors: LE Hunger | Alexander German | Felix Glang | KM Khakzar | Cach N. Dang | Angelika Mennecke | Andreas Maier | Frederik B. Laun | M Zaiß
Source:  (article, 2021-05-01)
Field: Materials Science — Materials Chemistry

DeepCEST: 7T Chemical exchange saturation transfer MRI contrast inferred from 3T data via deep learning with uncertainty quantification Contrast (vision) ; Saturation (graph theory) ; Transfer of learning ; Artificial intelligence ; Computer science ; Mathematics Lanthanide and Transition Metal Complexes ; Advanced MRI Techniques and Applications ; Atomic and Subatomic Physics Research
Authors: Toni Sagayaraj | Carsten Eickhoff
Source: arXiv (Cornell University) (preprint, 2021-11-20)
Field: Materials Science — Materials Chemistry

Image-Like Graph Representations for Improved Molecular Property Prediction Research into deep learning models for molecular property prediction has primarily focused on the development of better Graph Neural Network (GNN) architectures. Though new GNN variants continue to improve performance, their modifications share a common theme of alleviating problems intrinsic to their fundamental graph-to-graph nature. In this work, we examine these limitations and propose a new molecular representation that bypasses the need for GNNs entirely, dubbed CubeMol. Our fixed-dimensional stochastic representation, when paired with a transformer model, exceeds the performance of state-of-the-art GNN models and provides a path for scalability. Computer science ; Scalability ; Theoretical computer science ; Graph ; Transformer ; Representation (politics) ; Artificial intelligence ; Property (philosophy) ; Machine learning ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Christian Feiler | Tim Würger | Di Mei | Bahram Vaghefinazari | David A. Winkler | Sviatlana V. Lamaka | Robert H. Meißner | Mikhail L. Zheludkevich
Source:  (article, 2021-05-08)
Field: Chemistry — Physical and Theoretical Chemistry

Exploring chemical space using computational methods As the lightest structural engineering metal, magnesium (Mg) is a promising base material for the development of advanced technologies in transport, medical as well as in battery applications. A prerequisite to unlock the full potential of Mg&ndash;based materials is gaining control over their corrosion behaviour due to the relatively high chemical reactivity of Mg whereas each application field imposes unique requirements on this challenge. Corrosion prevention is essential in transport applications to avoid material failure. Bone implants require a degradation rate that is tailored to a specific injury whereas constant dissolution of the anode material is required to boost the efficiency of Mg-air primary batteries. Fortunately, small organic molecules have shown great potential to control the dissolution properties of pure Mg materials and its alloys.1 However, the vast space of small molecules with potentially useful dissolution modulating properties (inhibitors or accelerators) renders conventional experimental discovery methods too time- and resource-consuming. Consequently, computer-assisted selection prior to experimental investigations of the most promising candidates is of great benefit in the search for effective corrosion modulating additives. Here, we demonstrate how unsupervised clustering of potential Mg dissolution modulators based on structural similarities and sketch-maps2 can quantitatively predict their experimental performance when combined with a kernel ridge regression (KRR) model.3 The prediction accuracy of the KRR model is compared to an artificial neural network (ANN) model that was trained on a combination of atomistic and structural molecular descriptors.4 Furthermore, we confirm the robustness of our data-driven model by blind prediction of the dissolution modulating performance of 10 untested compounds. Finally, a workflow is presented that facilitates an automated selection of compounds with promising properties by screening of a large database comprised of commercially available substances. [1] S. V. Lamaka, B. Vaghefinazari, D. Mei, R.P. Petrauskas, D. H&ouml;che, M. L. Zheludkevich, Corros. Sci. 2017, 128, 224-240. [2] M. Ceriotti, G. A. Tribello, M. Parrinello, Proc. Natl Acad. Sci. USA 2011, 108, 13023. [3] T. W&uuml;rger, D. Mei, B. Vaghefinazari, D. A. Winkler, S. V. Lamaka, M. L. Zheludkevich, R. H. Mei&szlig;ner, C. Feiler, npj. Mater. Degrad. 2021, 5, 2. [4] C. Feiler, D. Mei, B. Vaghefinazari, T. W&uuml;rger, R. H.Mei&szlig;ner, B. J. C. Luthringer-Feyerabend, D. A. Winkler, M. L. Zheludkevich, S. V. Lamaka, Corros. Sci. 2020, 163, 108245. Computer science ; Space (punctuation) ; Chemical space ; Chemistry History and advancements in chemistry
Authors: Hao Xia | Weijie Wang | Yan Pang
Source:  (article, 2021-07-26)
Field: Materials Science — Materials Chemistry

Continuous crystallization process control with deep Galerkin method The aim of the continuous crystallization process control is to produce final product with desirable crystal size distribution (CSD). The evolution of CSD during the process can be obtained by solving the so-called population balance equation (PBE) which governs the dynamic behavior of crystallization processes. As a model, PBE is essentially a nonlinear partial integro-differential equation. The numerical solution of such PBE is a remarkably complicated. With the advances of deep learning, a new type of neural networks called physics informed neural network (PINN), are trained for supervised learning tasks while respecting physics described by general nonlinear partial differential equations has been introduced. In this paper, we use a specific PINN called deep Galerkin method (DGM) to train the neural networks to approximate the solution of the PBE for continuous crystallization process. With the PBE solved by the DGM, we are able to optimize the temperature profile of the process to produce products matching the desired CSD. Simulation experiments are used to prove the feasibility of the proposed method. Galerkin method ; Nonlinear system ; Artificial neural network ; Population balance equation ; Partial differential equation ; Process (computing) ; Computer science ; Applied mathematics ; Mathematical optimization ; Artificial intelligence ; Population ; Mathematics ; Algorithm ; Mathematical analysis ; Physics Crystallization and Solubility Studies ; Advanced Control Systems Optimization ; Fault Detection and Control Systems
Authors: Sam Lemonick﻿
Source: C&EN Global Enterprise (article, 2021-04-12)
Field: Materials Science — Materials Chemistry

Machine-learning performance put to the test Imagine you’re a materials scientist and your job is to discover a new material, a combination of atoms no one has ever made. Maybe you’re looking for a metal-organic framework (MOF). They have a lot of potential applications: carbon capture, drug delivery, and hydrogen fuel storage, just to name a few. So how do you find a new MOF? You could try machine learning. People are saying good things about machine learning, especially graph neural networks (GNNs), which are designed to behave like neurons in a brain. And lucky for you, there are already a handful of GNNs designed to predict new materials. Better yet, you can download them right now, for free, from a site like GitHub. But which GNN should you use? How should you teach it to make accurate predictions? What are the optimal settings? Choose wisely; you’re about to invest time and money and maybe hire Computer science ; Download ; Artificial intelligence ; Test (biology) ; Machine learning ; Data science ; World Wide Web ; Ecology Machine Learning in Materials Science
Authors: 祐 村上 | 洋一 菊地 | 隆明 武井
Source: Institutional Repositories DataBase (IRDB) (article, 2021-03-26)
Field: Chemistry — Physical and Theoretical Chemistry

The Decrease of Buoyant Force and the Conservation of Mass while Dissolving Water-soluble Substances : A case study of teaching materials connecting different units for deep-learning Dissolution ; Work (physics) ; Component (thermodynamics) ; Field (mathematics) Various Chemistry Research Topics ; Science Education and Pedagogy ; Experimental and Theoretical Physics Studies