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National Virtual Biotechnology Laboratory (NVBL) Symposium - Jim Brase, Deputy Associate Director for Computation, Lawrence Livermore National Laboratory, “Future S&T Needs: Artificial Intelligence/Machine Learning and Computing”
Over the last decade, there has been a growing interest in applying nanotechnology to cancer detection, treatment, and treatment monitoring. Advances in nanotechnology have enabled the fabrication of nanoparticles from various materials with different shapes and sizes. Nanoparticles can be accumulated preferentially within tumors by either “passive targeting” through a phenomenon typically known as “enhanced permeability and retention” or “active targeting” in which nanoparticles are conjugated with antibodies or peptides directed against tumor and/or stromal markers. The tumor specificity of nanoparticles in conjunction with their unique physicochemical properties offers many novel strategies for cancer treatment and detection. For example, notable approaches in the radiation oncology setting include the use of gold nanoparticles for radiation response modulation of tumor or normal tissue and thermal ablation or hyperthermia treatment of tumors. Some of these approaches are currently being tested either on humans or on animals and, very likely, will become the clinical reality in the near future. Various computational and experimental techniques have also been applied to address unique research issues associated with nanoparticles and may become the standard tools for future investigations and clinical translations. Therefore, both clinicians and researchers may need to be properly educated about the basic principles as well as the promise of nanoparticle-based applications with regard to the future of cancer diagnostics and therapeutics. This symposium will familiarize the audience with the potential applications of nanoparticles in oncologic imaging and therapy using specific illustrative examples. The audience will be properly oriented by these illustrative examples to the multiple avenues for collaborative research amongst interdisciplinary teams of physicists, clinicians, engineers, chemists, and biologists in industry and academia. Learning Objectives: Understand the physical bases of gold nanoparticle applications for radiosensitization and x-ray fluorescence imaging Understand the parameters that define gold nanoparticle-mediated radiosensitization in biological systems Understand the potential of magnetic nanoparticle characterization of the microenvironment Understand the various strategies for radiolabeling of nanoparticles and their applications S.C. and S.K. acknowledge support from MD Anderson Cancer Center, NIH (R01CA155446 and P30CA16672) and DoD (W81XWH-12-1-0198); J.W. acknowledges support from NIH (U54CA151662-01); W.C. acknowledges support from the University of Wisconsin-Madison, NIH (R01CA169365, P30CA014520, and T32CA009206), DoD (W81XWH-11-1-0644 and W81XWH-11-1-0648), and ACS (125246-RSG-13-099-01-CCE)
Over the last decade, there has been a growing interest in applying nanotechnology to cancer detection, treatment, and treatment monitoring. Advances in nanotechnology have enabled the fabrication of nanoparticles from various materials with different shapes and sizes. Nanoparticles can be accumulated preferentially within tumors by either “passive targeting” through a phenomenon typically known as “enhanced permeability and retention” or “active targeting” in which nanoparticles are conjugated with antibodies or peptides directed against tumor and/or stromal markers. The tumor specificity of nanoparticles in conjunction with their unique physicochemical properties offers many novel strategies for cancer treatment and detection. For example, notable approaches in the radiation oncology setting include the use of gold nanoparticles for radiation response modulation of tumor or normal tissue and thermal ablation or hyperthermia treatment of tumors. Some of these approaches are currently being tested either on humans or on animals and, very likely, will become the clinical reality in the near future. Various computational and experimental techniques have also been applied to address unique research issues associated with nanoparticles and may become the standard tools for future investigations and clinical translations. Therefore, both clinicians and researchers may need to be properly educated about the basic principles as well as the promise of nanoparticle-based applications with regard to the future of cancer diagnostics and therapeutics. This symposium will familiarize the audience with the potential applications of nanoparticles in oncologic imaging and therapy using specific illustrative examples. The audience will be properly oriented by these illustrative examples to the multiple avenues for collaborative research amongst interdisciplinary teams of physicists, clinicians, engineers, chemists, and biologists in industry and academia. Learning Objectives: Understand the physical bases of gold nanoparticle applications for radiosensitization and x-ray fluorescence imaging Understand the parameters that define gold nanoparticle-mediated radiosensitization in biological systems Understand the potential of magnetic nanoparticle characterization of the microenvironment Understand the various strategies for radiolabeling of nanoparticles and their applications S.C. and S.K. acknowledge support from MD Anderson Cancer Center, NIH (R01CA155446 and P30CA16672) and DoD (W81XWH-12-1-0198); J.W. acknowledges support from NIH (U54CA151662-01); W.C. acknowledges support from the University of Wisconsin-Madison, NIH (R01CA169365, P30CA014520, and T32CA009206), DoD (W81XWH-11-1-0644 and W81XWH-11-1-0648), and ACS (125246-RSG-13-099-01-CCE)
Over the last decade, there has been a growing interest in applying nanotechnology to cancer detection, treatment, and treatment monitoring. Advances in nanotechnology have enabled the fabrication of nanoparticles from various materials with different shapes and sizes. Nanoparticles can be accumulated preferentially within tumors by either “passive targeting” through a phenomenon typically known as “enhanced permeability and retention” or “active targeting” in which nanoparticles are conjugated with antibodies or peptides directed against tumor and/or stromal markers. The tumor specificity of nanoparticles in conjunction with their unique physicochemical properties offers many novel strategies for cancer treatment and detection. For example, notable approaches in the radiation oncology setting include the use of gold nanoparticles for radiation response modulation of tumor or normal tissue and thermal ablation or hyperthermia treatment of tumors. Some of these approaches are currently being tested either on humans or on animals and, very likely, will become the clinical reality in the near future. Various computational and experimental techniques have also been applied to address unique research issues associated with nanoparticles and may become the standard tools for future investigations and clinical translations. Therefore, both clinicians and researchers may need to be properly educated about the basic principles as well as the promise of nanoparticle-based applications with regard to the future of cancer diagnostics and therapeutics. This symposium will familiarize the audience with the potential applications of nanoparticles in oncologic imaging and therapy using specific illustrative examples. The audience will be properly oriented by these illustrative examples to the multiple avenues for collaborative research amongst interdisciplinary teams of physicists, clinicians, engineers, chemists, and biologists in industry and academia. Learning Objectives: Understand the physical bases of gold nanoparticle applications for radiosensitization and x-ray fluorescence imaging Understand the parameters that define gold nanoparticle-mediated radiosensitization in biological systems Understand the potential of magnetic nanoparticle characterization of the microenvironment Understand the various strategies for radiolabeling of nanoparticles and their applications S.C. and S.K. acknowledge support from MD Anderson Cancer Center, NIH (R01CA155446 and P30CA16672) and DoD (W81XWH-12-1-0198); J.W. acknowledges support from NIH (U54CA151662-01); W.C. acknowledges support from the University of Wisconsin-Madison, NIH (R01CA169365, P30CA014520, and T32CA009206), DoD (W81XWH-11-1-0644 and W81XWH-11-1-0648), and ACS (125246-RSG-13-099-01-CCE)
Over the last decade, there has been a growing interest in applying nanotechnology to cancer detection, treatment, and treatment monitoring. Advances in nanotechnology have enabled the fabrication of nanoparticles from various materials with different shapes and sizes. Nanoparticles can be accumulated preferentially within tumors by either “passive targeting” through a phenomenon typically known as “enhanced permeability and retention” or “active targeting” in which nanoparticles are conjugated with antibodies or peptides directed against tumor and/or stromal markers. The tumor specificity of nanoparticles in conjunction with their unique physicochemical properties offers many novel strategies for cancer treatment and detection. For example, notable approaches in the radiation oncology setting include the use of gold nanoparticles for radiation response modulation of tumor or normal tissue and thermal ablation or hyperthermia treatment of tumors. Some of these approaches are currently being tested either on humans or on animals and, very likely, will become the clinical reality in the near future. Various computational and experimental techniques have also been applied to address unique research issues associated with nanoparticles and may become the standard tools for future investigations and clinical translations. Therefore, both clinicians and researchers may need to be properly educated about the basic principles as well as the promise of nanoparticle-based applications with regard to the future of cancer diagnostics and therapeutics. This symposium will familiarize the audience with the potential applications of nanoparticles in oncologic imaging and therapy using specific illustrative examples. The audience will be properly oriented by these illustrative examples to the multiple avenues for collaborative research amongst interdisciplinary teams of physicists, clinicians, engineers, chemists, and biologists in industry and academia. Learning Objectives: Understand the physical bases of gold nanoparticle applications for radiosensitization and x-ray fluorescence imaging Understand the parameters that define gold nanoparticle-mediated radiosensitization in biological systems Understand the potential of magnetic nanoparticle characterization of the microenvironment Understand the various strategies for radiolabeling of nanoparticles and their applications S.C. and S.K. acknowledge support from MD Anderson Cancer Center, NIH (R01CA155446 and P30CA16672) and DoD (W81XWH-12-1-0198); J.W. acknowledges support from NIH (U54CA151662-01); W.C. acknowledges support from the University of Wisconsin-Madison, NIH (R01CA169365, P30CA014520, and T32CA009206), DoD (W81XWH-11-1-0644 and W81XWH-11-1-0648), and ACS (125246-RSG-13-099-01-CCE)
Over the last decade, there has been a growing interest in applying nanotechnology to cancer detection, treatment, and treatment monitoring. Advances in nanotechnology have enabled the fabrication of nanoparticles from various materials with different shapes and sizes. Nanoparticles can be accumulated preferentially within tumors by either “passive targeting” through a phenomenon typically known as “enhanced permeability and retention” or “active targeting” in which nanoparticles are conjugated with antibodies or peptides directed against tumor and/or stromal markers. The tumor specificity of nanoparticles in conjunction with their unique physicochemical properties offers many novel strategies for cancer treatment and detection. For example, notable approaches in the radiation oncology setting include the use of gold nanoparticles for radiation response modulation of tumor or normal tissue and thermal ablation or hyperthermia treatment of tumors. Some of these approaches are currently being tested either on humans or on animals and, very likely, will become the clinical reality in the near future. Various computational and experimental techniques have also been applied to address unique research issues associated with nanoparticles and may become the standard tools for future investigations and clinical translations. Therefore, both clinicians and researchers may need to be properly educated about the basic principles as well as the promise of nanoparticle-based applications with regard to the future of cancer diagnostics and therapeutics. This symposium will familiarize the audience with the potential applications of nanoparticles in oncologic imaging and therapy using specific illustrative examples. The audience will be properly oriented by these illustrative examples to the multiple avenues for collaborative research amongst interdisciplinary teams of physicists, clinicians, engineers, chemists, and biologists in industry and academia. Learning Objectives: Understand the physical bases of gold nanoparticle applications for radiosensitization and x-ray fluorescence imaging Understand the parameters that define gold nanoparticle-mediated radiosensitization in biological systems Understand the potential of magnetic nanoparticle characterization of the microenvironment Understand the various strategies for radiolabeling of nanoparticles and their applications S.C. and S.K. acknowledge support from MD Anderson Cancer Center, NIH (R01CA155446 and P30CA16672) and DoD (W81XWH-12-1-0198); J.W. acknowledges support from NIH (U54CA151662-01); W.C. acknowledges support from the University of Wisconsin-Madison, NIH (R01CA169365, P30CA014520, and T32CA009206), DoD (W81XWH-11-1-0644 and W81XWH-11-1-0648), and ACS (125246-RSG-13-099-01-CCE)
Probability density function (PDF) methods have been very useful in describing many physical aspects of turbulent mixing. In applications of these methods, modeled PDF transport equations are commonly simulated via classical Monte Carlo techniques, which provide estimates of moments of the PDF at arbitrary accuracy. In this paper, recently developed techniques in quantum computing and quantum enhanced measurements (quantum metrology) are used to construct a quantum algorithm that accelerates the computation of such estimates. This quantum algorithm provides a quadratic speedup over classical Monte Carlo methods in terms of the number of repetitions needed to achieve the desired precision. This paper illustrates the power of this algorithm by considering a binary scalar mixing process modeled by means of the coalescence/dispersion (C/D) closure. The equation is first simulated using classical Monte Carlo methods, where error estimates for the computation of central moments are provided. Then the quantum algorithm for this problem is simulated by sampling from the same probability distribution as that of the output of a quantum computer, and it is shown that significantly fewer resources are required to achieve the same precision. Finally, the results demonstrate potential applications of future quantum computers for simulation of turbulent mixing, and large classes of related problems.
Numerical weather prediction (NWP) models have been widely used for wind resource assessment. Model runs with higher spatial resolution are generally more accurate, yet extremely computational expensive. An alternative approach is to use data generated by a low resolution NWP model, in conjunction with statistical methods. In order to analyze the accuracy and computational efficiency of different types of NWP-based wind resource assessment methods, this paper performs a comparison of three deterministic and probabilistic NWP-based wind resource assessment methodologies: (i) a coarse resolution (0.5 degrees x 0.67 degrees) global reanalysis data set, the Modern-Era Retrospective Analysis for Research and Applications (MERRA); (ii) an analog ensemble methodology based on the MERRA, which provides both deterministic and probabilistic predictions; and (iii) a fine resolution (2-km) NWP data set, the Wind Integration National Dataset (WIND) Toolkit, based on the Weather Research and Forecasting model. Results show that: (i) as expected, the analog ensemble and WIND Toolkit perform significantly better than MERRA confirming their ability to downscale coarse estimates; (ii) the analog ensemble provides the best estimate of the multi-year wind distribution at seven of the nine sites, while the WIND Toolkit is the best at one site; (iii) the WIND Toolkit is more accurate in estimating the distribution of hourly wind speed differences, which characterizes the wind variability, at five of the available sites, with the analog ensemble being best at the remaining four locations; and (iv) the analog ensemble computational cost is negligible, whereas the WIND Toolkit requires large computational resources. Future efforts could focus on the combination of the analog ensemble with intermediate resolution (e.g., 10-15 km) NWP estimates, to considerably reduce the computational burden, while providing accurate deterministic estimates and reliable probabilistic assessments.
Abstract The current predictive modeling techniques applied to Density Functional Theory (DFT) computations have helped accelerate the process of materials discovery by providing significantly faster methods to scan materials candidates, thereby reducing the search space for future DFT computations and experiments. However, in addition to prediction error against DFT-computed properties, such predictive models also inherit the DFT-computation discrepancies against experimentally measured properties. To address this challenge, we demonstrate that using deep transfer learning, existing large DFT-computational data sets (such as the Open Quantum Materials Database (OQMD)) can be leveraged together with other smaller DFT-computed data sets as well as available experimental observations to build robust prediction models. We build a highly accurate model for predicting formation energy of materials from their compositions; using an experimental data set of $$1,643$$ 1 , 643 observations, the proposed approach yields a mean absolute error (MAE) of $$0.07$$ 0.07 eV/atom, which is significantly better than existing machine learning (ML) prediction modeling based on DFT computations and is comparable to the MAE of DFT-computation itself.
The Argonne Leadership Computing Facility (ALCF) helps researchers solve some of the world’s largest and most complex problems, while also advancing the nation’s efforts to develop future exascale computing systems. This report presents some of the ALCF’s notable achievements in key strategic areas over the past year.
Some influential American policymakers, scholars, and analysts are extremely concerned with the effects that quantum computing will have on national security. Similar to the way space technology was viewed in the context of the US-Soviet rivalry in the Cold War, they view scientific advancement in quantum computing as a race with significant national security consequences and a part of the emerging US-China rivalry. It is claimed that the winner of this race will be able to thwart all the cryptographic efforts of the loser and gain unfettered access to any state secret of the losing government. Additionally, it is suggested that the winner will be able to protect their secrets with a higher level of security than contemporary cryptography guarantees at the moment. This paper argues that these three claims are considerably overstated, and that policymakers and scholars should shift focus to a different national security problem that quantum computing will create. Maintaining the long-term security of secret information secured by contemporary cryptographic protections, which will fail against an attack by a future quantum computer, is the predominant challenge needing attention. This paper takes a technically-informed approach to this issue. It first lays out the national security problems that are envisioned in much of the current discourse surrounding quantum computing. Then, it will assess the technical advantages that quantum computers will have over classical computers in the field of cryptography. Next, it will provide an explanation of the specific ways that the technical realities of quantum computing are inconsistent with the predominant areas of concern. Following, it will highlight the more pressing national security problem posed by quantum computing that the public discussion has, for the most part, missed. The piece will conclude with recommendations for how to mitigate this issue.
Quantum chemistry is a key application area for noisy-intermediate scale quantum (NISQ) devices, and therefore serves as an important benchmark for current and future quantum computer performance. Previous benchmarks in this field have focused on variational methods for computing ground and excited states of various molecules, including a benchmarking suite focused on the performance of computing ground states for alkali-hydrides under an array of error mitigation methods. State-of-the-art methods to reach chemical accuracy in hybrid quantum-classical electronic structure calculations of alkali hydride molecules on NISQ devices from IBM are outlined here. Here it is demonstrated how to extend the reach of variational eigensolvers with symmetry preserving Ansätze. Next, it is outlined how to use quantum imaginary time evolution and Lanczos as a complementary method to variational techniques, highlighting the advantages of each approach. Finally, a new error mitigation method is demonstrated which uses systematic error cancellation via hidden inverse gate constructions, improving the performance of typical variational algorithms. These results show that electronic structure calculations have advanced rapidly, to routine chemical accuracy for simple molecules, from their inception on quantum computers a few short years ago, and they point to further rapid progress to larger molecules as the power of NISQ devices grows.
Simulating non-equilibrium phenomena in strongly-interacting quantum many-body systems, including thermalization, is a promising application of near-term and future quantum computation. By performing experiments on a digital quantum computer consisting of fully-connected optically-controlled trapped ions, we study the role of entanglement in the thermalization dynamics of a Z 2 lattice gauge theory in 2+1 spacetime dimensions. Using randomized-measurement protocols, we efficiently learn a classical approximation of non-equilibrium states that yields the gap-ratio distribution and the spectral form factor of the entanglement Hamiltonian. These observables exhibit universal early-time signals for quantum chaos, a prerequisite for thermalization. Our work, therefore, establishes quantum computers as robust tools for studying universal features of thermalization in complex many-body systems, including in gauge theories.
The increasing penetration of power electronics in power grids significantly raises the computing requirements in a real-time (and/or fast) simulation of the power grid. The real-time simulation is an enabler for evaluating controllers, protection systems, new equipment, and twinning. In this paper, emerging computing architectures such as tensor processing units (TPU), neural/neuromorphic processing units (NPU), and quantum processing units (QPU) are introduced and characterized for the real-time (and/or fast) simulation of power electronics-dominated power grids. The metrics and the process to characterize emerging computing architectures to perform real-time (and/or fast) simulations of future power grids with power electronics are discussed. Three of the emerging computing units are characterized based on these metrics and the process developed. This characterization will enable identification and comparison of emerging computing architectures that can perform real-time (and/or fast) simulation of future power grids.
The Korea Atomic Energy Research Institute is currently in the process of qualifying a low-enriched-uranium fuel element design for the new Ki-Jang Research Reactor (KJRR). As part of this effort, a prototype KJRR fuel element was irradiated in this paper for several operating cycles in the northeast flux trap of the Advanced Test Reactor (ATR) at the Idaho National Laboratory. The KJRR fuel element contained a very large quantity of fissile material (618 g 235 U) in comparison with historical ATR experiment standards (<1 g 235 U), and its presence in the ATR flux trap was expected to create a neutronic configuration that would be well outside of the approved validation envelope for the reactor physics analysis methods used to support ATR operations. Accordingly, it was necessary to conduct an extensive set of new low-power physics measurements in the ATR Critical Facility (ATRC), a companion facility to the ATR, located in an immediately adjacent building and sharing the same fuel storage canal. The new measurements included fission power distributions, reactivity, and measurements related to the calibration of the in-core online instrumentation. The effort was focused on the objective of expanding the validation envelope for the computational reactor physics tools used to support ATR operations and safety analysis to include the planned KJRR irradiation in the ATR and similar experiments that are anticipated in the future. The computational and experimental results have demonstrated that the neutronic behavior of the KJRR fuel element in the ATRC is well understood in terms of its general effects on ATRC core reactivity and fission power distributions and its effects on the calibration of the ATR Lobe Power Calculation and Indication System, as well as in terms of its own internal fission rate distribution and total fission power per unit ATRC core power. Finally, taken as a whole, these results have significantly extended the ATR physics validation envelope, thereby enabling an entire new class of irradiation experiments.
The Korea Atomic Energy Research Institute is currently in the process of qualifying a Low-Enriched Uranium fuel element design for the new Ki-Jang Research Reactor (KJRR). As part of this effort, a prototype KJRR fuel element was irradiated for several operating cycles in the Northeast Flux Trap of the Advanced Test Reactor (ATR) at the Idaho National Laboratory. The KJRR fuel element contained a very large quantity of fissile material (618g 235U) in comparison with historical ATR experiment standards (<1g 235U), and its presence in the ATR flux trap was expected to create a neutronic configuration that would be well outside of the approved validation envelope for the reactor physics analysis methods used to support ATR operations. Accordingly it was necessary, prior to high-power irradiation of the KJRR fuel element in the ATR, to conduct an extensive set of new low-power physics measurements with the KJRR fuel element installed in the ATR Critical Facility (ATRC), a companion facility to the ATR that is located in an immediately adjacent building, sharing the same fuel handling and storage canal. The new measurements had the objective of expanding the validation envelope for the computational reactor physics tools used to support ATR operations and safety analysis to include the planned KJRR irradiation in the ATR and similar experiments that are anticipated in the future. The computational and experimental results demonstrated that the neutronic behavior of the KJRR fuel element in the ATRC is well-understood, both in terms of its general effects on core excess reactivity and fission power distributions, its effects on the calibration of the core lobe power measurement system, as well as in terms of its own internal fission rate distribution and total fission power per unit ATRC core power. Taken as a whole, these results have significantly extended the ATR physics validation envelope, thereby enabling an entire new class of irradiation experiments.
Whisper is statistical analysis code using sensitivity/uncertainty-based methods to determine baseline upper subcritical limit (USL) for nuclear criticality safety. Features of Whisper 1.1 include: GLLS method implemented to compute adjusted covariance based on current benchmark suite (1,100+ ICSBEP models), BLO “low-fidelity” covariance data used (44 energy groups), adjusted covariance is pre-computed and saved, and adjusted cross sections are NOT computed. Potential future efforts include: an extension to include angular distributions in benchmark selection and in GLLS adjustment, a move toward more modern covariance data (ENDF/B-VIII.0) and different group structure, and compute and store adjusted cross sections (trivial).
We review the establishment of computational biology in Greece and Cyprus from its inception to date and issue recommendations for future development. We compare output to other countries of similar geography, economy, and size—based on publication counts recorded in the literature—and predict future growth based on those counts as well as national priority areas. Our analysis may be pertinent to wider national or regional communities with challenges and opportunities emerging from the rapid expansion of the field and related industries. Our recommendations suggest a 2-fold growth margin for the 2 countries, as a realistic expectation for further expansion of the field and the development of a credible roadmap of national priorities, both in terms of research and infrastructure funding.