A5076915647- Machine Learning in Materials Science
- Ammonia Synthesis and Nitrogen Reduction
- Scientific Computing and Data Management
- Electrocatalysts for Energy Conversion
- Hydrogen Storage and Materials
- Advanced Photocatalysis Techniques
- Catalytic Processes in Materials Science
- Catalysis and Oxidation Reactions
- X-ray Diffraction in Crystallography
- Research Data Management Practices
- Radiomics and Machine Learning in Medical Imaging
- Ionic liquids properties and applications
- Environmental DNA in Biodiversity Studies
- CO2 Reduction Techniques and Catalysts
- Advanced Database Systems and Queries
- Distributed and Parallel Computing Systems
- Mineral Processing and Grinding
- Cloud Computing and Resource Management
- Advanced Electron Microscopy Techniques and Applications
- Personal Information Management and User Behavior
- Cloud Data Security Solutions
- Innovative Microfluidic and Catalytic Techniques Innovation
- Particle accelerators and beam dynamics
- Carbon dioxide utilization in catalysis
- Graph Theory and Algorithms
University Hospitals of Cleveland
2024
Case Western Reserve University
2024
University School
2024
SLAC National Accelerator Laboratory
2024
Interface (United States)
2016-2020
Toyota Research Institute
2020
Stanford University
2016-2020
Stanford Medicine
2018-2020
Shriram Institute for Industrial Research
2020
Franciscan University of Steubenville
2020
ADVERTISEMENT RETURN TO ISSUEPREVViewpointNEXTElectrochemical Ammonia Synthesis—The Selectivity ChallengeAayush R. Singh†, Brian A. Rohr†, Jay Schwalbe†, Matteo Cargnello†, Karen Chan†‡, Thomas F. Jaramillo†‡, Ib Chorkendorff¶, and Jens K. Nørskov*†‡View Author Information† SUNCAT Center for Interface Science Catalysis, Department of Chemical Engineering, Stanford University, Stanford, California 94305, United States‡ SLAC National Accelerator Laboratory, Menlo Park, 94025, States¶ Physics,...
The active and selective electroreduction of atmospheric nitrogen (N2) to ammonia (NH3) using energy from solar or wind sources at the point use would enable a sustainable alternative Haber–Bosch process for fertilizer production. While is thermodynamically possible, experimental attempts thus far have required large overpotentials produced primarily hydrogen (H2). In this Perspective, we show how insights electronic structure calculations energetics process, combined with mean-field...
Abstract X-ray absorption spectroscopy (XAS) produces a wealth of information about the local structure materials, but interpretation spectra often relies on easily accessible trends and prior assumptions structure. Recently, researchers have demonstrated that machine learning models can automate this process to predict coordinating environments absorbing atoms from their XAS spectra. However, are difficult interpret, making it challenging determine when they valid whether consistent with...
Benchmarking metrics for materials discovery <italic>via</italic> sequential learning are presented, to assess the efficacy of existing algorithms and be scientific in our assessment accelerated science.
The Haber–Bosch process for the reduction of atmospheric nitrogen to ammonia is one most optimized heterogeneous catalytic reactions, but there are aspects industrial that remain less than ideal. It has been shown activity metal catalysts limited by a Brønsted–Evans–Polanyi (BEP) scaling relationship between reaction and transition-state energies N2 dissociation, leading negligible production rate at ambient conditions modest under harsh conditions. In this study, we use density functional...
The Volmer reaction is rendered with quite higher barriers using LutH<sup>+</sup>when compared to H<sub>3</sub>O<sup>+</sup>, mainly due surface puckering.
Abstract Electrochemical processes for ammonia synthesis could potentially replace the high temperature and pressure conditions of Haber‐Bosch process, with voltage offering a pathway to distributed fertilizer production that leverages rapidly decreasing cost renewable electricity. However, nitrogen is an unreactive molecule hydrogen evolution reaction presents major selectivity challenge. An electrode electrodeposited lithium in tetrahydrofuran solvent overcomes both problems by providing...
Scaling relations and volcano plots are widely used in heterogeneous catalysis. In this Perspective, we discuss the prospects challenges associated with application of similar concepts homogeneous catalysis using examples from literature that have appeared recently.
Abstract Rapid discovery and synthesis of future materials requires intelligent data acquisition strategies to navigate large design spaces. A popular strategy is Bayesian optimization, which aims find candidates that maximize material properties; however, often finding specific subsets the space meet more complex or specialized goals. We present a framework captures experimental goals through straightforward user-defined filtering algorithms. These algorithms are automatically translated...
Graph representations of hierarchical knowledge, including experiment provenances, will help usher in a new era data-driven materials science.
The competition between the hydrogen evolution reaction and electrochemical reduction of carbon dioxide to multi-carbon products is a well-known challenge. In this study, we present simple micro-kinetic model these competing reactions over platinum catalyst under strong reducing potential at varying proton concentrations in non-aqueous solvent. provides some insight into mechanism suggests that low concentration high fraction stepped sites likely improve selectivity products.
Event-based data workflows powered by cloud computing can help accelerate the development of materials acceleration platforms while fostering ideals extensibility and interoperability in chemistry research.
The electrochemical transformation of potent greenhouse gases and low-value carbon sources to produce useful carbon-based products is a highly desirable sustainability goal.
We present a database resulting from high throughput experimentation, primarily on metal oxide solid state materials. The central relational database, the Materials Provenance Store (MPS), manages metadata and experimental provenance acquisition of raw materials, through synthesis, to broad range materials characterization techniques. Given primary research goal discovery solar fuels many experiments involve electrochemistry, along with optical, structural, compositional characterizations....
Cellular hypertrophy of adipose tissue underlies many the proposed proinflammatory mechanisms for obesity-related diseases. Adipose results from an accumulation esterified lipids (triglycerides) into membrane-enclosed intracellular lipid droplets (LDs). The coupling between adipocyte metabolism and LD morphology could be exploited to investigate biochemical regulation pathways by monitoring dynamics LDs. This article describes image processing method identify LDs based on several distinctive...
We present a generalizable database architecture ESAMP that captures the complete provenance associated with material. demonstrate this and based machine learning on one of largest experimental materials databases.
While the vision of accelerating materials discovery using data driven methods is well-founded, practical realization has been throttled due to challenges in generation, ingestion, and state-aware machine learning. High-throughput experiments automated computational workflows are addressing challenge capitalizing on these emerging resources requires ingestion into an architecture that captures complex provenance simulations. In this manuscript, we describe event-sourced for (ESAMP) encodes...
X-ray absorption spectroscopy (XAS) produces a wealth of information about the local structure materials, but interpretation spectra often relies on easily accessible trends and prior assumptions structure. Recently, researchers have demonstrated that machine learning models can automate this process to predict coordinating environments absorbing atoms from their XAS spectra. However, are difficult interpret, making it challenging determine when they valid whether consistent with physical...
Abstract Invited for this month's cover picture is the group of Prof. Matteo Cargnello in Chemical Engineering Department at Stanford University and within SUNCAT Center Interface Science Catalysis (USA). The shows nitrogen molecules being reduced to ammonia on a lithium‐covered surface. Read full text Article 10.1002/celc.201902124 .
While the vision of accelerating materials discovery using data driven methods is well-founded, practical realization has been throttled due to challenges in generation, ingestion, and state-aware machine learning. High-throughput experiments automated computational workflows are addressing challenge capitalizing on these emerging resources requires ingestion into an architecture that captures complex provenance simulations. In this manuscript, we describe event-sourced for (ESAMP) encodes...