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Register a new userModeling of Nuclear Waste Forms: State-of-the-Art and Perspectives
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Computational modeling is an important aspect of the research on nuclear waste materials. In particular, atomistic simulations, when used complementary to experimental efforts, contribute to the scientific basis of safety case for nuclear waste repositories. Here we discuss the state-of-the-art and perspectives of atomistic modeling for nuclear waste management on a few cases of successful synergy of atomistic simulations and experiments. In particular, we discuss here: (1) the potential of atomistic simulations to investigate the uranium oxidation state in mixed valence uranium oxides and (2) the ability of cementitious barrier materials to retain radionuclides such as 226Ra and 90Sr, and of studtite/metastudtite secondary peroxide phases to incorporate actinides such as Np and Am. The new contribution we make here is the computation of the incorporation of Sr by C-S-H (calcium silicate hydrate) phases.
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The past few years have witnessed the concrete and fast spreading of quantum technologies for practical computation and simulation. In particular, quantum computing platforms based on either trapped ions or superconducting qubits have become available for simulations and benchmarking, with up to few tens of qubits that can be reliably initialized, controlled, and measured. The present review aims at giving a comprehensive outlook on the state of art capabilities offered from these near-term noisy devices as universal quantum simulators, i.e. programmable quantum computers potentially able to calculate the time evolution of many physical models. First, we give a pedagogic overview on the basic theoretical background pertaining digital quantum simulations, with a focus on hardware-dependent mapping of spin-type Hamiltonians into the corresponding quantum circuit model as a key initial step towards simulating more complex models. Then, we review the main experimental achievements obtained in the last decade regarding the digital quantum simulation of such spin models, mostly employing the two leading quantum architectures. We compare their performances and outline future challenges, also in view of prospective hybrid technologies, towards the ultimate goal of reaching the long sought quantum advantage for the simulation of complex many body models in the physical sciences.
Complementary metal-oxide semiconductor (CMOS) technology has radically reshaped the world by taking humanity to the digital age. Cramming more transistors into the same physical space has enabled an exponential increase in computational performance, a strategy that has been recently hampered by the increasing complexity and cost of miniaturization. To continue achieving significant gains in computing performance, new computing paradigms, such as quantum computing, must be developed. However, finding the optimal physical system to process quantum information, and scale it up to the large number of qubits necessary to build a general-purpose quantum computer, remains a significant challenge. Recent breakthroughs in nanodevice engineering have shown that qubits can now be manufactured in a similar fashion to silicon field-effect transistors, opening an opportunity to leverage the know-how of the CMOS industry to address the scaling challenge. In this article, we focus on the analysis of the scaling prospects of quantum computing systems based on CMOS technology.
We recently developed a procedure to recognize gamma-ray blazar candidates within the positional uncertainty regions of the unidentified/unassociated gamma-ray sources (UGSs). Such procedure was based on the discovery that Fermi blazars show peculiar infrared colors. However, to confirm the real nature of the selected candidates, optical spectroscopic data are necessary. Thus, we performed an extensive archival search for spectra available in the literature in parallel with an optical spectroscopic campaign aimed to reveal and confirm the nature of the selected gamma-ray blazar candidates. Here, we first search for optical spectra of a selected sample of gamma-ray blazar candidates that can be potential counterparts of UGSs using the Sloan Digital Sky Survey (SDSS DR12). This search enables us to update the archival search carried out to date. We also describe the state-of-art and the future perspectives of our campaign to discover previously unknown gamma-ray blazars.
We present NECI, a state-of-the-art implementation of the Full Configuration Interaction Quantum Monte Carlo algorithm, a method based on a stochastic application of the Hamiltonian matrix on a sparse sampling of the wave function. The program utilizes a very powerful parallelization and scales efficiently to more than 24000 CPU cores. In this paper, we describe the core functionalities of NECI and recent developments. This includes the capabilities to calculate ground and excited state energies, properties via the one- and two-body reduced density matrices, as well as spectral and Greens functions for ab initio and model systems. A number of enhancements of the bare FCIQMC algorithm are available within NECI, allowing to use a partially deterministic formulation of the algorithm, working in a spin-adapted basis or supporting transcorrelated Hamiltonians. NECI supports the FCIDUMP file format for integrals, supplying a convenient interface to numerous quantum chemistry programs and it is licensed under GPL-3.0.
Understanding how genotypes map onto phenotypes, fitness, and eventually organisms is arguably the next major missing piece in a fully predictive theory of evolution. We refer to this generally as the problem of the genotype-phenotype map. Though we are still far from achieving a complete picture of these relationships, our current understanding of simpler questions, such as the structure induced in the space of genotypes by sequences mapped to molecular structures, has revealed important facts that deeply affect the dynamical description of evolutionary processes. Empirical evidence supporting the fundamental relevance of features such as phenotypic bias is mounting as well, while the synthesis of conceptual and experimental progress leads to questioning current assumptions on the nature of evolutionary dynamics-cancer progression models or synthetic biology approaches being notable examples. This work delves into a critical and constructive attitude in our current knowledge of how genotypes map onto molecular phenotypes and organismal functions, and discusses theoretical and empirical avenues to broaden and improve this comprehension. As a final goal, this community should aim at deriving an updated picture of evolutionary processes soundly relying on the structural properties of genotype spaces, as revealed by modern techniques of molecular and functional analysis.
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