A5075051974- Machine Learning in Materials Science
- Electronic and Structural Properties of Oxides
- Perovskite Materials and Applications
- Advanced Condensed Matter Physics
- Computational Drug Discovery Methods
- X-ray Diffraction in Crystallography
- Ferroelectric and Piezoelectric Materials
- biodegradable polymer synthesis and properties
- Magnetic and transport properties of perovskites and related materials
- Catalysis and Oxidation Reactions
- Nuclear materials and radiation effects
- Radiation Detection and Scintillator Technologies
- Semiconductor materials and devices
- Multiferroics and related materials
- Synthesis and properties of polymers
- 2D Materials and Applications
- Catalytic Processes in Materials Science
- Dielectric materials and actuators
- Scientific Computing and Data Management
- Inorganic Chemistry and Materials
- Nuclear Materials and Properties
- Advanced Photocatalysis Techniques
- Machine Learning and Algorithms
- Ammonia Synthesis and Nitrogen Reduction
- Quantum Dots Synthesis And Properties
GE Global Research (United States)
2023-2024
Los Alamos National Laboratory
2014-2023
General Electric (United States)
2023
Government of the United States of America
2023
Los Alamos Medical Center
2019-2021
Fritz Haber Institute of the Max Planck Society
2017
University of Connecticut
2009-2014
Albuquerque Academy
2013
Abstract Propelled partly by the Materials Genome Initiative, and algorithmic developments resounding successes of data-driven efforts in other domains, informatics strategies are beginning to take shape within materials science. These approaches lead surrogate machine learning models that enable rapid predictions based purely on past data rather than direct experimentation or computations/simulations which fundamental equations explicitly solved. Data-centric methods becoming useful...
The materials discovery process can be significantly expedited and simplified if we learn effectively from available knowledge data. In the present contribution, show that efficient accurate prediction of a diverse set properties material systems is possible by employing machine (or statistical) learning methods trained on quantum mechanical computations in combination with notions chemical similarity. Using family one-dimensional chain systems, general formalism allows us to discover...
Abstract The ability to make rapid and accurate predictions on bandgaps of double perovskites is much practical interest for a range applications. While quantum mechanical computations high-fidelity are enormously computation-time intensive thus impractical in high throughput studies, informatics-based statistical learning approaches can be promising alternative. Here we demonstrate systematic feature-engineering approach robust framework efficient electronic perovskites. After evaluating...
Abstract The ability to efficiently design new and advanced dielectric polymers is hampered by the lack of sufficient, reliable data on wide polymer chemical spaces difficulty generating such given time computational/experimental constraints. Here, we address issue accelerating dielectrics extracting learning models from generated accurate state-of-the-art first principles computations for occupying an important part subspace. are ‘fingerprinted’ as simple, easily attainable numerical...
Understanding the behavior (and failure) of dielectric insulators experiencing extreme electric fields is critical to operation present and emerging electrical electronic devices. Despite its importance, development a predictive theory breakdown has remained challenge, owing complex multiscale nature this process. Here, we focus on intrinsic field insulators—the theoretical limit determined purely by chemistry material, i.e., elements material composed of, atomic-level structure, bonding....
The Materials Genome Initiative (MGI) has heralded a sea change in the philosophy of materials design. In an increasing number applications, successful deployment novel benefited from use computational methodologies, data descriptors, and machine learning. Polymers have long suffered lack on electronic, mechanical, dielectric properties across large chemical spaces, causing stagnation set suitable candidates for various applications. Extensive efforts over last few years seen fruitful...
New and improved dielectric materials with high breakdown strength are required for both energy density electric storage applications continued miniaturization of electronic devices. Despite much practical significance, accurate ab initio predictions complex beyond the current state-of-the art. Here we take an alternative data-enabled route to address this design problem. Our informatics-based approach employs a transferable machine learning model, trained validated on limited amount data...
Advanced materials with improved properties have the potential to fuel future technological advancements. However, identification and discovery of these optimal for a specific application is non-trivial task, because vastness chemical search space enormous compositional configurational degrees freedom. Materials informatics provides an efficient approach towards rational design new materials, via learning from known data make decisions on previously unexplored compounds in accelerated...
Abstract Emerging computation- and data-driven approaches are particularly useful for rationally designing materials with targeted properties. Generally, these rely on identifying structure-property relationships by learning from a dataset of sufficiently large number relevant materials. The learned information can then be used to predict the properties not already in dataset, thus accelerating design. Herein, we develop 1,073 polymers related make it available at http://khazana.uconn.edu/ ....
Perovskite oxides continue to attract huge interest due their fascinating and wide-ranging properties for diverse applications. The tunability of these may be further enhanced by increasing compositional complexity via double perovskite-ordered configurations containing multiple cations. In this work, we focus on an exhaustive chemical space single oxide perovskites optimally explore identify novel compositions that are likely form stable compounds. Critically, examine the relationship...
Polyhydroxyalkanoate-based polymers—being ecofriendly, biosynthesizable, and economically viable possessing a broad range of tunable properties—are currently being actively pursued as promising alternatives for petroleum-based plastics. The vast chemical complexity accessible within this class polymers gives rise to challenges in the rational discovery novel polymer chemistries specific applications. burgeoning field informatics addresses challenge via providing tools strategies accelerated...
Abstract Atomic simulations provide an effective means to understand the underlying physics of structural phase transformations. However, this remains a challenge for certain allotropic metals due failure classical interatomic potentials represent multitude bonding. Based on machine-learning (ML) techniques, we develop hybrid method in which describing martensitic transformations can be learned with high degree fidelity from ab initio molecular dynamics (AIMD). Using zirconium as model...
We study the coherent and semi-coherent Al/α-Al2O3 interfaces using molecular dynamics simulations with a mixed, metallic-ionic atomistic model. For interfaces, both Al-terminated O-terminated nonstoichiometric have been studied their relative stability has established. To understand misfit accommodation at interface, 1-dimensional (1D) dislocation model 2-dimensional (2D) network studied. latter case, our analysis reveals an interface structure of three sets parallel dislocations, each...
Abstract Spinels represent an important class of technologically relevant materials, used in diverse applications ranging from dielectrics, sensors and energy materials. While solid solutions combining two “single spinels” have been explored a number past studies, no ordered “double” spinels reported. Based on our first principles computations, here we predict the existence such double spinel compound MgAlGaO 4 , formed by equimolar mixing MgAl 2 O normal MgGa inverse spinels. After studying...
First Page
Scintillators are important materials for radiographic imaging and tomography (RadIT), when ionizing radiations used to reveal internal structures of materials. Since its invention by R\"ontgen, RadIT now come in many modalities such as absorption-based X-ray radiography, phase contrast imaging, coherent diffractive high-energy X- $\gamma-$ray radiography at above 1 MeV, computed (CT), proton (IT), neutron IT, positron emission (PET), electron muon tomography, etc. Spatial, temporal...
Abstract The compositional and structural variety inherent to oxide perovskites spawn wide-ranging applications. In perovskites, the band gap E g , a key material parameter for these applications, can be optimally controlled by varying composition. Here, we implement hierarchical screening process in which two cross-validated predictive machine learning models classification regression, trained using exhaustive datasets that span 68 elements of periodic table, are applied sequentially. model...
Due to increased environmental pressures, significant research has focused on finding suitable biodegradable plastics replace ubiquitous petrochemical-derived polymers. Polyhydroxyalkanoates (PHAs) are a class of polymers that can be synthesized by microorganisms and biodegradable, making them candidates. The present study looks at the degradation properties two PHA polymers: polyhydroxybutyrate (PHB) polyhydroxybutyrate-co-polyhydroxyvalerate (PHBV; 8 wt.% valerate), in different soil...