jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: John A. Schlueter
Source: ECS Meeting Abstracts (article, 2021-10-19)
Field: Materials Science — Materials Chemistry

(Invited) Designing Materials to Revolutionize and Engineer the Future of Clean Energy Technologies The Materials Genome Initiative (MGI) is a multi-agency partnership that seeks to accelerate the progression of materials research across the Materials Development Continuum. Through use of a computationally led and data-driven approach, MGI promotes the rapid design, discovery, development and deployment of advanced materials that will ensure sustained American leadership in sectors including clean energy. The goals of the MGI include 1) leading a culture shift in materials research through use of an iterative feedback-loop approach, 2) integrating experiment, computation, and theory throughout the materials research community, 3) making digital data accessible and useful to the larger community, and 4) creating a world-class materials science and engineering workforce. The Designing Materials to Revolutionize and Engineer our Future (DMREF) program at the National Science Foundation (NSF) partners with other federal agencies to promote these objectives. DMREF currently supports about one hundred projects that cover the full spectrum of materials research, including several that focus on energy conversion technologies. This talk will provide an overview of the DMREF program and provide specific examples from DMREF teams that are benefitting from collaboration with MGI-related efforts at other federal agencies. Interactions with the U.S. Department of Energy's (DOE) Energy Materials Network (EMN) will be highlighted. Results of the 2021 DMREF competition, future priorities, and funding opportunities for the MGI-related research will be discussed. General partnership ; Software deployment ; Agency (philosophy) ; Workforce ; Engineering ; Engineering management ; Engineering ethics ; Political science ; Sociology Machine Learning in Materials Science
Authors: Sepideh Hashemi | Baskar Ganapathysubramanian | Stephen Casey | Ji Su | Surya R. Kalidindi
Source: arXiv (Cornell University) (preprint, 2021-11-02)
Field: Materials Science — Materials Chemistry

Feature engineering for microstructure-property mapping in organic photovoltaics Linking the highly complex morphology of organic photovoltaic (OPV) thin films to their charge transport properties is critical for achieving high performance material system that serves as a cost-efficient approach for energy harvesting. In this paper, a novel unsupervised feature engineering framework is developed and used to establish reduced-order structure-property linkages for OPV films. This framework takes advantage of digital image processing algorithms to identify the salient material features of OPVs undergoing the charge transport phenomenon. These material states are then used to obtain a low-dimensional representation of OPV microstructures via 2-point spatial correlations and principal component analysis. It is found that in addition to the material PC scores, two distance-based metrics are required to complete the microstructure quantification of complex OPVs. A localized version of the Gaussian process (laGP) is then used to link the material PC scores as well as the two distance-based metrics to the short-circuit current of OPVs. It is demonstrated that the unsupervised feature engineering framework presented in this paper in conjunction with the laGP can lead to high-fidelity and accurate data-driven structure-property linkages for OPV films. Feature (linguistics) ; Organic solar cell ; Property (philosophy) ; Computer science ; Representation (politics) ; Microstructure ; Materials science ; Characterization (materials science) ; Principal component analysis ; Artificial intelligence ; Nanotechnology ; Polymer ; Composite material Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Industrial Vision Systems and Defect Detection
Authors: Maximilian Rixner | Phaedon‐Stelios Koutsourelakis
Source: arXiv (Cornell University) (preprint, 2021-08-05)
Field: Materials Science — Materials Chemistry

Self-supervised optimization of random material microstructures in the small-data regime While the forward and backward modeling of the process-structure-property chain has received a lot of attention from the materials community, fewer efforts have taken into consideration uncertainties. Those arise from a multitude of sources and their quantification and integration in the inversion process are essential in meeting the materials design objectives. The first contribution of this paper is a flexible, fully probabilistic formulation of such optimization problems that accounts for the uncertainty in the process-structure and structure-property linkages and enables the identification of optimal, high-dimensional, process parameters. We employ a probabilistic, data-driven surrogate for the structure-property link which expedites computations and enables handling of non-differential objectives. We couple this with a novel active learning strategy, i.e. a self-supervised collection of data, which significantly improves accuracy while requiring small amounts of training data. We demonstrate its efficacy in optimizing the mechanical and thermal properties of two-phase, random media but envision its applicability encompasses a wide variety of microstructure-sensitive design problems. Probabilistic logic ; Property (philosophy) ; Computer science ; Process (computing) ; Computation ; Identification (biology) ; Variety (cybernetics) ; Probabilistic design ; Bayesian optimization ; Machine learning ; Mathematical optimization ; Industrial engineering ; Engineering design process ; Artificial intelligence ; Algorithm ; Engineering ; Mathematics ; Mechanical engineering Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; X-ray Diffraction in Crystallography
Authors: Michael G. Taylor | Aditya Nandy | Connie C. Lu | Heather J. Kulik
Source: arXiv (Cornell University) (preprint, 2021-07-29)
Field: Materials Science — Materials Chemistry

Deciphering Cryptic Behavior in Bimetallic Transition Metal Complexes\n with Machine Learning The rational tailoring of transition metal complexes is necessary to address\noutstanding challenges in energy utilization and storage. Heterobimetallic\ntransition metal complexes that exhibit metal-metal bonding in stacked "double\ndecker" ligand structures are an emerging, attractive platform for catalysis,\nbut their properties are challenging to predict prior to laborious synthetic\nefforts. We demonstrate an alternative, data-driven approach to uncovering\nstructure-property relationships for rational bimetallic complex design. We\ntailor graph-based representations of the metal-local environment for these\nheterobimetallic complexes for use in training of multiple linear regression\nand kernel ridge regression (KRR) models. Focusing on oxidation potentials, we\nobtain a set of 28 experimentally characterized complexes to develop a multiple\nlinear regression model. On this training set, we achieve good accuracy (mean\nabsolute error, MAE, of 0.25 V) and preserve transferability to unseen\nexperimental data with a new ligand structure. We trained a KRR model on a\nsubset of 330 structurally characterized heterobimetallics to predict the\ndegree of metal-metal bonding. This KRR model predicts relative metal-metal\nbond lengths in the test set to within 5%, and analysis of key features reveals\nthe fundamental atomic contributions (e.g., the valence electron configuration)\nthat most strongly influence the behavior of complexes. Our work provides\nguidance for rational bimetallic design, suggesting that properties including\nthe formal shortness ratio should be transferable from one period to another.\n Bimetallic strip ; Rational design ; Quantitative structure–activity relationship ; Valence (chemistry) ; Metal ; Transition metal ; Computer science ; Materials science ; Computational chemistry ; Chemistry ; Machine learning ; Nanotechnology ; Catalysis Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Organometallic Complex Synthesis and Catalysis
Authors: Zachary M. Sparrow | Brian G. Ernst | Trine Kay Quady | Robert A. DiStasio
Source: arXiv (Cornell University) (preprint, 2021-09-26)
Field: Chemistry — Inorganic Chemistry

CASE21: Uniting Non-Empirical and Semi-Empirical Density Functional Approximation Strategies using Constraint-Based Regularization In this work, we present a general framework that unites the two primary strategies for constructing density functional approximations (DFAs): non-empirical (NE) constraint satisfaction and semi-empirical (SE) data-driven optimization. The proposed method employs B-splines -- bell-shaped spline functions with compact support -- to construct each inhomogeneity correction factor (ICF). This choice offers several distinct advantages over a polynomial basis by enabling explicit enforcement of linear and non-linear constraints as well as ICF smoothness using Tikhonov regularization and penalized B-splines (P-splines). As proof of concept, we use this approach to construct CASE21 -- a Constrained And Smoothed semi-Empirical hybrid generalized gradient approximation that completely satisfies all but one constraint (and partially satisfies the remaining one) met by the PBE0 NE-DFA and exhibits enhanced performance across a diverse set of chemical properties. As such, we argue that the paradigm presented herein maintains the physical rigor and transferability of NE-DFAs while leveraging high-quality quantum-mechanical data to improve performance. Regularization (linguistics) ; Smoothness ; Computer science ; Mathematical optimization ; Tikhonov regularization ; Applied mathematics ; Spline (mechanical) ; Constraint (computer-aided design) ; Construct (python library) ; Polynomial ; B-spline ; Mathematics ; Inverse problem ; Artificial intelligence ; Mathematical analysis Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Source: Journal of Physics Conference Series (article, 2021-06-01)
Field: Materials Science — Materials Chemistry

Preface International Virtual Conference on Bridging Innovative Trends in Mathematics, Engineering &amp; Technology-BITMET’20 Bridging Innovative Trends in Mathematics, Engineering &amp; Technology-BITMET’20 M.parimala Dr. G.M. Arun Prasath Dr. Waseem Ghazi Alshanti Dr. E. Balamurugan Dr. Renuka Mohanraj K.Palani 1 Department of Mathematics, Bannari Amman Institute of Technology, Sathyamangalam, Erode, tamilnadu, India. 2 University of Technology andApplied Sciences, Oman 3 Jubail University College, King SaudiArabia 4 University of Africa, NIGERIA 5 Maharishi InternationalUniversity-Fairfield, USA 6 PG &amp; Research Department of Mathematics,A.P.C.Mahalaxmi College for Women, Thoothukudi, TN,India International Virtual Conference on Bridging Innovative Trends in Materials Science (BITMS-2020) provides a global platform for academics, researchers and students to share their knowledge, work and experiences both through the presentations/posters from the conference and dissemination of high quality research publications in the areas of Mathematics, Engineering &amp; Technology. This acts as an international forum for researchers and scholars to have face-to-face interactions with an eminent speakers and experts to discuss the most recent innovations, trends and concerns as well as practical challenges encountered and solutions adopted in the fields of Mathematics, Engineering &amp; Technology. Dr. G.M. Arun Prasath University of Technology and Applied Sciences, Oman ‘Applications of Analytic Hierarchy Process in Science and Technology’ Dr. Waseem Ghazi Alshanti Jubail University College, King Saudi Arabia ‘On the Horizontal Deviation of a Spinning Projectile Penetrating into Granular Systems’ Dr. E. Balamurugan, University of Africa, NIGERIA ‘Role of Mathematics in Artificial Intelligence’ Dr. Renuka Mohanraj, Maharishi International University-Fairfield, USA ‘Internet of Things for Android’ International Virtual Conference on Bridging Innovative Trends in Mathematics, Engineering &amp; Technology-BITMET’20, Organizing Committee, Convener, Coordinators, Internal Members, International Technical Committee Members and this titles are available in this pdf. Library science ; Bridging (networking) ; Engineering ; Mathematics ; Management ; Computer science Machine Learning in Materials Science ; 2D Materials and Applications ; Surface Chemistry and Catalysis
Authors: Bartosz Barzdajn | A.M. Garrett | T.M. Whiting | C.P. Race
Source: Modelling and Simulation in Materials Science and Engineering (preprint, 2021-09-29)
Field: Materials Science — Materials Chemistry

Development of data-driven spd tight-binding models of Fe—parameterisation based on QSGW and DFT calculations including information about higher-order elastic constants Abstract Quantum-mechanical (QM) simulations, thanks to their predictive power, can provide significant insights into the nature and dynamics of defects such as vacancies, dislocations and grain boundaries. These considerations are essential in the context of the development of reliable, inexpensive and environmentally friendly alloys. However, despite significant progress in computer performance, QM simulations of defects are still extremely time-consuming with ab-initio /non-parametric methods. The two-centre Slater–Koster (SK) tight-binding (TB) models can achieve significant computational efficiency and provide an interpretable picture of the electronic structure. In some cases, this makes TB a compelling alternative to models based on abstraction of the electronic structure, such as the embedded atom model. The biggest challenge in the implementation of the SK method is the estimation of the optimal and transferable parameters that are used to construct the Hamiltonian matrix. In this paper, we will present results of the development of a data-driven framework, following the classical approach of adjusting parameters in order to recreate properties that can be measured or estimated using ab-initio or non-parametric methods. Distinct features include incorporation of data from QSGW (quasi-particle self-consistent GW approximation) calculations, as well as consideration of higher-order elastic constants. Furthermore, we provide a description of the optimisation procedure, omitted in many publications, including the design stage. We also apply modern optimisation techniques that allow us to minimise constraints on the parameter space. In summary, this paper introduces some methodological improvements to the semi-empirical approach while addressing associated challenges and advantages. Computer science ; Context (archaeology) ; Parametric statistics ; Ab initio ; Hamiltonian (control theory) ; Statistical physics ; Quantum ; Electronic structure ; Mathematical optimization ; Physics ; Mathematics ; Computational chemistry ; Chemistry ; Quantum mechanics Machine Learning in Materials Science ; High Temperature Alloys and Creep ; Microstructure and Mechanical Properties of Steels
Authors: Vanja Subotić | Michael Eibl | Thomas Thaller | Philipp Harter | Christoph Hochenauer
Source: ECS Meeting Abstracts (article, 2021-07-23)
Field: Materials Science — Materials Chemistry

Different Approaches to Predict and Optimize an SOC Performance: Experimental and Numerical Study Solid oxide cells (SOC) provide a great potential to enable desired transition to an efficient, environmentally friendly and low-carbon energy system. Their great advantage is the capability of a reversible operation, which enables to utilize fuels to generate electricity and heat, or in a reversible mode to utilize electricity, steam and carbon dioxide to generate valuable fuels. Nevertheless, in order to accelerate their commercialization, the main criteria such as reliability, stability and degradation prevention at its preliminary stage have to be ensured. In order to fulfil these requirements, different optimization steps are necessary, starting with the material development, over cell, stack and system manufacturing, towards optimized operating environment. In this study we applied different approaches to (i) predict and optimize the cell performance, (ii) predict different failure modes, and thus to (iii) prolong the SOC lifetime. A design of experiment was made in order to cover a range of different parameters and to use them for the further model validation. The experimental investigations have been performed employing both conventional and non-conventional methodologies, e.g. total harmonic distortion (THD) analysis. For the purpose of experimental investigations, the cell was fuelled with humidified hydrogen or humidified methane, whereby to generate hydrogen purely steam electrolysis was employed. Different parameters such as operating temperature, fuel volume flow, etc. were varied and their impact on the cell performance was studied. On the other hand, two different modelling approaches were considered in order to predict and optimize the cell performance. The first one is based on a physical model, which enables very precise performance prediction, and suggests the required optimization steps. The second one is based on the artificial intelligence, which enables to determine best appropriate operating conditions using the information from previous experiments carried out. As an illustrative example, Fig. 1 compares different approaches, such as the temperature monitoring over the entire cell surface and the electrochemical impedance spectra both simulated and subsequently validated using the experimental data. Within this study we show that the cell performance can be optimized employing different simulation aspects with the aim of time-saving calculations. Moreover, when employing non-conventional online monitoring tools, an early degradation prediction is possible thus significantly prolonging the cell overall lifetime. Figure 1 Process engineering ; Stack (abstract data type) ; Solid oxide fuel cell ; Computer science ; Electricity ; Engineering ; Anode ; Chemistry Advancements in Solid Oxide Fuel Cells ; Catalytic Processes in Materials Science ; Chemical Looping and Thermochemical Processes
Authors: Shraddha Dhanraj Nehate
Source: Journal of International Crisis and Risk Communication Research (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Characterization of Hydrogenated Boron Carbide and Boron Carbon Nitride Thin Films and Applications Dimension scaling has been the driving force for improved performance of semiconductor integrated circuits (ICs) for the past few decades. While the semiconductor industry continues to achieve returns on Moore's law, the resistance-capacitance (RC) delay remains a critical bottleneck towards further performance improvement. To support advanced computing performance, the development and integration of new low dielectric (low-k) materials to reduce the capacitance of interconnects are crucial. Apart from the low dielectric constant value, one of the other vital parameters to replace the current dielectric material is mechanical stability. Materials in the boron, carbon, and nitrogen ternary triangle have emerged promising low dielectric materials. The combination of boron, carbon, and nitrogen leads to unique materials exhibiting distinct properties from graphite to boron carbide (B4C), boron nitride (BN), boron carbon nitride (BCN) compounds going from metallic to semiconducting to insulating. BCN compounds combine properties of diamond to display excellent mechanical properties and reproduce BN semiconducting properties with adjustable bandgaps. The dielectric constant (k) value of B4C and BCN is between 4-6. This dissertation attempted to reduce the k value of B4C and BCN by introducing non-polar bonds in the materials through hydrogenation using the RF magnetron sputtering technique. Thin films were deposited by single or dual-target sputtering from B4C and BN targets by varying hydrogen to nitrogen reactive gas and substrate temperature. All the films demonstrated distinct composition at different growth parameters and displayed evidence of tunable properties with film composition. It was shown that tuning film composition achieves low-k values while ensuring no deterioration in the mechanical properties of thin films. Moreover, the influence of hydrogenation and variation in substrate temperature was investigated on B4C and BCN properties for applications in electrical, mechanical, and optical devices. Additionally, graphene analogous BCN nanocoating synthesized in this study exhibited outstanding inhibition against bacterial growth and biofilm formation, making them promising for biomedical devices. Boron nitride ; Boron carbide ; Characterization (materials science) ; Materials science ; Boron ; Carbide ; Carbon fibers ; Nitride ; Thin film ; Wide-bandgap semiconductor ; Nanotechnology ; Metallurgy ; Optoelectronics ; Composite material ; Chemistry ; Composite number Boron and Carbon Nanomaterials Research ; MXene and MAX Phase Materials ; Graphene research and applications
Authors: Dr. Ahsan Farooq
Source: American Journal Of Pure And Applied Physics (article, 2021-04-30)
Field: Materials Science — Materials Chemistry

Exploring the Properties of Two-Dimensional Materials for Energy Storage Two-dimensional (2D) materials such as graphene, transition metal dichalcogenides (TMDs), and MXenes have gained considerable attention for their exceptional structural and electrochemical properties, making them ideal candidates for energy storage devices. Their high surface-to-volume ratios, excellent conductivity, and tunable surface chemistry allow efficient charge transport and storage. This article explores the critical properties, synthesis techniques, and practical applications of 2D materials in energy storage technologies such as supercapacitors and lithium-ion batteries. It also discusses the current challenges and future directions needed for commercial scalability and environmental sustainability. MXenes ; Supercapacitor ; Electrochemical energy storage ; Energy storage ; Scalability ; Transition metal ; Ideal (ethics) ; Charge (physics) MXene and MAX Phase Materials ; Supercapacitor Materials and Fabrication ; 2D Materials and Applications
Authors: F. Pelin Kinik | Andres Ortega Guerrero | Fatmah Mish Ebrahim | Christopher P. Ireland | Özge Kadioglu | Amber Mace | Mehrdad Asgari | Berend Smit
Source: ChemRxiv (preprint, 2021-07-02)
Field: Chemistry — Inorganic Chemistry

Towards optimal photocatalytic hydrogen generation from water using pyrene-based metal-organic frameworks Metal organic frameworks (MOFs) are promising materials for the photocatalytic H2 evolution reaction (HER) from water. To find the optimal MOF for a photocatalytic HER one has to consider many different factors. For example, studies have emphasized the importance of light absorption capability, optical band gap and band alignment. However, most of these studies have been carried on very different materials. In this work, we present a combined experimental and computation study of the photocatalytic HER performance, of a set of isostructural pyrene-based MOFs (M-TBAPy, where M = Sc, Al, Ti, and In). We systematically studied the effects of changing the metal in the node on the different factors that contribute to the HER rate (e.g., optical properties, the band structure, water adsorption). In addition, for Sc-TBAPy we also studied the effect of changes in crystal morphology on the photocatalytic HER rate. We used this understanding to improve photocatalytic HER efficiency of Sc-TBAPy, to exceed the one reported for Ti-TBAPy, in the presence of co-catalyst. Photocatalysis ; Isostructural ; Metal-organic framework ; Pyrene ; Materials science ; Photocatalytic water splitting ; Catalysis ; Adsorption ; Band gap ; Photochemistry ; Water splitting ; Chemical engineering ; Nanotechnology ; Crystal structure ; Chemistry ; Optoelectronics ; Physical chemistry ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Advanced Photocatalysis Techniques ; MXene and MAX Phase Materials