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The Chemical Theory, Models and Computational Methods program supports the discovery and development of theoretical and computational methods or models to address a range of chemical challenges, with emphasis on emerging areas of chemical research.  Proposals that focus on established theoretical or computational approaches should involve innovative additions or modifications that substantially broaden their applicability.  Areas of interest include, but are not limited to, electronic structure, quantum reaction dynamics, statistical mechanics, molecular dynamics, and simulation and modeling techniques for molecular systems and systems in condensed phases.  Areas of application span the full range of chemical systems from small molecules to mesoscopic aggregates, including single molecules, biological systems and materials in condensed phases.   Despite the diverse application areas, the goal of the program is to support the development of new theoretical and computational methodologies that have the potential of being broadly applicable to a range of challenging chemical problems. We are particularly interested in fundamental areas of chemical research that are difficult or impossible to address using current synthetic, experimental, and/or computational methodologies.  We encourage the integration of innovative software development with methodological and algorithmic development, especially computational approaches that allow efficient utilization of the high end computers of the future.

The Chemical Theory, Models and Computational Methods program supports the discovery and development of theoretical and computational methods or models to address a range of chemical challenges, with emphasis on emerging areas of chemical research. Proposals that focus on established theoretical or computational approaches should involve innovative additions or modifications that substantially broaden their applicability. Areas of interest include, but are not limited to, electronic structure, quantum reaction dynamics, statistical mechanics, molecular dynamics, and simulation and modeling techniques for molecular systems and systems in condensed phases. Areas of application span the full range of chemical systems from small molecules to mesoscopic aggregates, including single molecules, biological systems and materials in condensed phases. Despite the diverse application areas, the goal of the program is to support the development of new theoretical and computational methodologies that have the potential of being broadly applicable to a range of challenging chemical problems. We are particularly interested in fundamental areas of chemical research that are difficult or impossible to address using current synthetic, experimental, and/or computational methodologies. We encourage the integration of innovative software development with methodological and algorithmic development, especially computational approaches that allow efficient utilization of the high end computers of the future.Proposals that utilize established theoretical and modeling approaches to solve problems in chemistry, biology or materials discovery and design may be more appropriate for other programs in either the Chemistry division or in other Divisions or Directorates.

The DOE SC program in Basic Energy Sciences (BES) hereby announces its interest in receiving new and renewal applications from small groups (2-3 principal investigators) and integrated multidisciplinary teams (typically from multiple institutions) in Computational Chemical Sciences (CCS). Single-investigator applications are not responsive to the objectives of this FOA. CCS will support basic research to develop validated, open-source codes for modeling and simulation of complex chemical processes and phenomena that allow full use of emerging exascale and future planned DOE leadership-class computing capabilities. The focus for CCS is on developing capabilities that allow modeling and simulation of new or previously inaccessible complex chemical systems and/or provide dramatic improvement in fidelity, scalability, and throughput. Teams should bring together expertise in domain areas (e.g., electronic structure, chemical dynamics, statistical mechanics, etc.) and other areas important to advance computational tools such as data science, algorithm development, and software architectures. Priority will be given to efforts that address reaction chemistry across multiple scales in complex environments important in geosciences, catalysis, biochemistry, or electrochemistry. CCS will continue to support the DOE Exascale Computing Initiative (ECI). The ECI aims to accelerate the research and development needed to overcome key exascale challenges and maximize benefits of high-performance computing. This funding opportunity continues the BES commitment to ECI by developing open-source codes that can take full advantage of emerging exascale and future planned DOE leadership-class computing facilities.

In 2013, a new NSF-funded petascale computing system, Blue Waters, was deployed at the University of Illinois. The goal of this project and system is to open up new possibilities in science and engineering by providing computational capability that makes it possible for investigators to tackle much larger and more complex research challenges across a wide spectrum of domains. The purpose of this solicitation is to invite research groups to submit requests for allocations of resources on the Blue Waters system. Proposers must show a compelling science or engineering challenge that will require petascale computing resources. Proposers must also be prepared to demonstrate that they have a science or engineering research problem that requires and can effectively exploit the petascale computing capabilities offered by Blue Waters. Proposals from or including junior researchers are encouraged, as one of the goals of this solicitation is to build a community capable of using petascale computing.

In 2013, a new NSF-funded petascale computing system, Blue Waters, was deployed at the University of Illinois.  The goal of this project and system is to open up new possibilities in science and engineering by providing computational capability that makes it possible for investigators to tackle much larger and more complex research challenges across a wide spectrum of domains.  The purpose of this solicitation is to invite research groups to submit requests for allocations of resources on the Blue Waters system. Proposers must show a compelling science or engineering challenge that will require petascale computing resources. Proposers must also be prepared to demonstrate that they have a science or engineering research problem that requires and can effectively exploit the petascale computing  capabilities offered by Blue Waters.  Proposals from or including junior researchers are encouraged, as one of the goals of this solicitation is to build a community capable of using petascale computing.

The goals of the Network for Computational Nanotechnology (NCN) are to: 1) accelerate the transformation of nanoscience to nanotechnology through the integration of simulation with experimentation; 2) engage an ever-larger and more diverse cyber community sharing novel, high-quality nanoscale computation and simulation research and educational resources; 3) develop open-access, open-source software to stimulate data sharing; and 4) inspire and educate the next-generation workforce. The NCN consists of a stand-alone Cyber Platform, which provides computation, simulation, and education services to over 330,000 researchers, educators, students, and industry members of the nanoscience and engineering community annually worldwide; and Nodes, which develop compelling new computational and simulation tools to disseminate through Cyber Platform (nanoHUB.org) and cultivate communities of users in emerging areas of nanoscale science and engineering.

The National Science Foundation (NSF), through its Divisions of Electrical, Communications and Cyber Systems (ECCS), Computing and Communication Foundations (CCF), Molecular and Cellular Biosciences (MCB), and Materials Research (DMR) announces a follow-up solicitation on the Semiconductor Synthetic Biology for Information Storage and Retrieval Program (SemiSynBio-II).  Future ultra-low energy storage-based computing systems can be built on principles derived from organic systems that are at the intersection of physics, chemistry, biology, computer science and engineering.  Next-generation information storage technologies can be envisioned that are driven by biological principles and use biomaterials in the fabrication of devices and systems that can store data for more than 100 years with storage capacity 1,000 times more than current storage technologies.  Such a research effort can have a significant impact on the future of information storage and retrieval technologies. This focused solicitation seeks high-risk/high-return interdisciplinary research on novel concepts and enabling technologies that will address the fundamental scientific issues and technological challenges associated with the underpinnings of synthetic biology integrated with semiconductor technology. This research will foster interactions among various disciplines including biology, physics, chemistry, materials science, computer science and engineering that will enable in heretofore unanticipated breakthroughs.

Future ultra-low-energy computing, storage and signal-processing systems can be built on principles derived from organic systems that are at the intersection of chemistry, biology, and engineering. New information technologies can be envisioned that are based on biological principles and that use biomaterials in the fabrication of devices and components; it is anticipated that these information technologies could enable stored data to be retained for more than 100 years and storage capacity to be 1,000 times greater than current capabilities. These could also facilitate compact computers that will operate with substantially lower power than today's computers. Research in support of these goals can have a significant impact on advanced information processing and storage technologies. This focused solicitation seeks high-risk/high-return interdisciplinary research on novel concepts and enabling technologies that will address the scientific issues and technological challenges associated with the underpinnings of synthetic biology integrated with semiconductor technology. This research will foster interactions among various disciplines including biology, engineering, physics, chemistry, materials science, computer science, and information science that will enable heretofore-unanticipated breakthroughs as well as meet educational goals.

Computational neuroscience provides a theoretical foundation and a rich set of technical approaches for understanding complex neurobiological systems, building on the theory, methods, and findings of computer science, neuroscience, and numerous other disciplines. Through the CRCNS program, the National Science Foundation (NSF), the National Institutes of Health (NIH), the German Federal Ministry of Education and Research (Bundesministerium für Bildung und Forschung, BMBF), the French National Research Agency (Agence Nationale de la Recherche, ANR), and the United States-Israel Binational Science Foundation (BSF) support collaborative activities that will advance the understanding of nervous system structure and function, mechanisms underlying nervous system disorders, and computational strategies used by the nervous system. 

Advanced computational infrastructure and the ability to perform large-scale simulations and accumulate massive amounts of data have revolutionized scientific and engineering disciplines.  The goal of the CDS&E program is to identify and capitalize on opportunities for major scientific and engineering breakthroughs through new computational and data analysis approaches.  The intellectual drivers may be in an individual discipline or they may cut across more than one discipline in various Directorates.  The key identifying factor is that the outcome relies on the development, adaptation, and utilization of one or more of the capabilities offered by advancement of both research and infrastructure in computation and data, either through cross-cutting or disciplinary programs. 

Predictive Science is potentially applicable to a variety of fields, including nuclear weapons, efficient manufacturing, biological systems, nanoscale material science, organic chemical processes, climate modeling, etc. Success in these simulations requires both software and algorithmic frameworks for integrating multiple disciplines into a single application and adding significant disciplinary strength and depth to make that integration effective. Applications should conduct computer science research in areas that will contribute to the advancement of Exascale computing technologies and demonstrate the results in the context of the chosen application. The FOA contains topics that are of particular interest to the NNSA National Laboratories as they move towards Exascale computing. Other topics that will enable the advancements in Exascale computing are encouraged as well. More details are contained in the FOA.

CMMT supports theoretical and computational materials research in the topical areas represented in DMR's Topical Materials Research Programs (these are also variously known as Individual Investigator Award (IIA) Programs, or Core Programs, or Disciplinary Programs), which include: Condensed Matter Physics (CMP), Biomaterials (BMAT), Ceramics (CER), Electronic and Photonic Materials (EPM), Metals and Metallic Nanostructures (MMN), Polymers (POL), and Solid State and Materials Chemistry (SSMC). The CMMT program supports fundamental research that advances conceptual understanding of hard and soft materials, and materials-related phenomena; the development of associated analytical, computational, and data-centric techniques; and predictive materials-specific theory, simulation, and modeling for materials research.Research may encompass the advance of new paradigms in materials research, including emerging data-centric approaches utilizing data-analytics or machine learning. Computational efforts span from the level of workstations to advanced and high-performance scientific computing. Emphasis is on approaches that begin at the smallest appropriate length scale, such as electronic, atomic, molecular, nano-, micro-, and mesoscale, required to yield fundamental insight into material properties, processes, and behavior, to predict new materials and states of matter, and to reveal new materials phenomena. Approaches that span multiple scales of length and time may be required to advance fundamental understanding of materials properties and phenomena, particularly for polymeric materials and soft matter.

The National Science Foundation (NSF), through its Divisions of Electrical, Communications and Cyber Systems (ECCS), Computing and Communication Foundations (CCF), Molecular and Cellular Biosciences (MCB), and Materials Research (DMR) announces a follow-up solicitation on the Semiconductor Synthetic Biology for Information Storage and Retrieval Program (SemiSynBio-II). Future ultra-low energy storage-based computing systems can be built on principles derived from organic systems that are at the intersection of physics, chemistry, biology, computer science and engineering. Next-generation information storage technologies can be envisioned that are driven by biological principles and use biomaterials in the fabrication of devices and systems that can store data for more than 100 years with storage capacity 1,000 times more than current storage technologies. Such a research effort can have a significant impact on the future of information storage and retrieval technologies. This focused solicitation seeks high-risk/high-return interdisciplinary research on novel concepts and enabling technologies that will address the fundamental scientific issues and technological challenges associated with the underpinnings of synthetic biology integrated with semiconductor technology. This research will foster interactions among various disciplines including biology, physics, chemistry, materials science, computer science and engineering that will enable in heretofore unanticipated breakthroughs.

Scientific advances such as genomics, quantitative structural biology, imaging techniques, and modeling of complex systems have created opportunities for exciting research careers at the interface between the physical/computational sciences and the biological sciences. Tackling key problems in biology will require scientists trained in areas such as chemistry, physics, applied mathematics, computer science, and engineering. Recognizing the vital role such cross-trained scientists will play in furthering biomedical science, the Burroughs Wellcome Fund has developed the Career Awards at the Scientific Interface. These grants are intended to foster the early career development of researchers who have transitioned or are transitioning from undergraduate and/or graduate work in the physical/mathematical/computational sciences or engineering into postdoctoral work in the biological sciences, and who are dedicated to pursuing a career in academic research. Candidates are expected to draw from their training in a scientific field other than biology to propose innovative approaches to answer important questions in the biological sciences.

Computational neuroscience provides a theoretical foundation and a rich set of technical approaches for understanding complex neurobiological systems, building on the theory, methods, and findings of computer science, neuroscience, and numerous other disciplines. Through the CRCNS program, the National Science Foundation (NSF), the National Institutes of Health (NIH), the German Federal Ministry of Education and Research (Bundesministerium für Bildung und Forschung, BMBF), the French National Research Agency (Agence Nationale de la Recherche, ANR), the United States-Israel Binational Science Foundation (BSF), and Japan's National Institute of Information and Communications Technology (NICT) support collaborative activities that will advance the understanding of nervous system structure and function, mechanisms underlying nervous system disorders, and computational strategies used by the nervous system.

The Environmental Chemical Sciences (ECS) Program supports basic research in chemistry that promotes the understanding of natural and anthropogenic chemical processes in our environment.  Projects supported by this program enable fundamentally new avenues of basic research and transformative technologies. The program is particularly interested in studying molecular phenomena on surfaces and interfaces in order to understand the inherently complex and heterogeneous environment.  Projects utilize advanced experimental, modeling and computational approaches, as well as developing new approaches.  Topics include studies of environmental surfaces and interfaces under laboratory conditions, the fundamental properties of water and water solutions important in environmental processes, dissolution, composition, origin and behavior of molecular scale systems under a variety of naturally occurring environmental conditions, chemical reactivity of synthetic nanoparticles and their molecular level interactions with the environment, and application of theoretical models and computational approaches to discover and predict environmental phenomena at the molecular scale.

The Environmental Chemical Sciences (ECS) Program supports basic research in chemistry that promotes the understanding of natural and anthropogenic chemical processes in our environment.  Projects supported by this program enable fundamentally new avenues of basic research and transformative technologies. The program is particularly interested in studying molecular phenomena on surfaces and interfaces in order to understand the inherently complex and heterogeneous environment.  Projects utilize advanced experimental, modeling and computational approaches, as well as developing new approaches.  Topics include studies of environmental surfaces and interfaces under laboratory conditions, the fundamental properties of water and water solutions important in environmental processes, dissolution, composition, origin and behavior of molecular scale systems under a variety of naturally occurring environmental conditions, chemical reactivity of synthetic nanoparticles and their molecular level interactions with the environment, and application of theoretical models and computational approaches to discover and predict environmental phenomena at the molecular scale.

n 2016, the National Science Foundation (NSF) unveiled a set of "Big Ideas," 10 bold, long-term research and process ideas that identify areas for future investment at the frontiers of science and engineering (seehttps://www.nsf.gov/news/special_reports/big_ideas/index.jsp). The Big Ideas represent unique opportunities to position our Nation at the cutting edge of global science and engineering leadership by bringing together diverse disciplinary perspectives to support convergence research. As such, when responding to this solicitation, even though proposals must be submitted to the Directorate for Mathematical & Physical Sciences/Office of Multidisciplinary Activities (MPS/OMA),once received, the proposals will be managed by a cross-disciplinary team of NSF Program Directors. The Quantum Idea Incubator for Transformational Advances in Quantum Systems (QII - TAQS) program is designed to support interdisciplinary teams that will explore highly innovative, original, and potentially transformative ideas for developing and applying quantum science, quantum computing, and quantum engineering. Proposals with the potential to deliver new concepts, new platforms, and/or new approaches that will accelerate the science, computing, and engineering of quantum technologies are encouraged. Breakthroughs in quantum sensing, quantum communications, quantum simulations, or quantum computing systems are anticipated.

Candidates must hold a Ph.D. (or equivalent) in chemistry, computational or evolutionary molecular biology, computer science, economics, mathematics, neuroscience, ocean sciences (including marine biology), physics, or a related field; Candidates must hold a tenure track (or equivalent) position at a college, university or other degree-granting institution in the United States or Canada;  Candidates must normally be no more than six years from completion of their most recent Ph.D. (or equivalent) as of the year of their nomination.  (That is, most recent Ph.D. must have been awarded on or after September 2007.)** While Fellows are expected to be at an early stage of their research careers, there should be strong evidence of independent research accomplishments. Candidates in all fields are normally below the rank of associate professor and do not hold tenure, but these are not strict requirements. The Alfred P. Sloan Foundation welcomes nominations of all candidates who meet the traditional high standards of this program, and strongly encourages the participation of women and members of underrepresented minority groups.

The National Science Foundation Directorates for Mathematical and Physical Sciences (MPS), Computer and Information Science and Engineering (CISE), Engineering (ENG), and the Simons Foundation Division of Mathematics and Physical Sciences will jointly sponsor up to two new research collaborations consisting of mathematicians, statisticians, electrical engineers, and theoretical computer scientists. Research activities will be focused on explicit topics involving some of the most challenging questions in the general area of Mathematical and Scientific Foundations of Deep Learning. Each collaboration will conduct training through research involvement of recent doctoral degree recipients, graduate students, and/or undergraduate students from across this multi-disciplinary spectrum. Annual meetings of the Principal Investigators ("PIs") and other principal researchers involved in the collaborations will be held at the Simons Foundation in New York City. This program complements NSF's National Artificial Intelligence Research Institutes program by supporting collaborative research focused on the mathematical and scientific foundations of Deep Learning through a different modality and at a different scale.

The National Science Foundation (NSF) Directorates for Engineering (ENG) and Geosciences (GEO), the Divisions of Integrative Organismal Systems (IOS) and Environmental Biology (DEB), in the Directorate for Biological Sciences (BIO), the Division of Computer and Network Systems in the Directorate Computer and Information Science and Engineering (CISE/CNS), and the Division of Chemistry (CHE) in the Directorate for Mathematical and Physical Sciences, in collaboration with the US Department of Agriculture National Institute of Food and Agriculture (USDA NIFA) encourage convergent research that transforms existing capabilities in understanding dynamic soil processes, including soil formation, through advances in sensor systems and modeling. The Signals in the Soil (SitS) program fosters collaboration among the two partner agencies and the researchers they support by combining resources and funding for the most innovative and high-impact projects that address their respective missions. To make transformative advances in our understanding of soils, multiple disciplines must converge to produce environmentally-benign novel sensing systems with multiple modalities that can adapt to different environments and collect and transmit data for a wide range of biological, chemical, and physical parameters. Effective integration of sensor data will be key for achieving a better understanding of signaling interactions among plants, animals, microbes, the soil matrix, and aqueous and gaseous components. New sensor networks have the potential to inform models in novel ways, to radically change how data is obtained from various natural and managed (both urban and rural) ecosystems, and to better inform the communities that directly rely on soils for sustenance and livelihood.