IA Scholar Query: A Combinatorial Approach to Quantum Error Correcting Codes
https://scholar.archive.org/
Internet Archive Scholar query results feedeninfo@archive.orgMon, 21 Nov 2022 00:00:00 GMTfatcat-scholarhttps://scholar.archive.org/help1440Construction and local equivalence of dual-unitary operators: from dynamical maps to quantum combinatorial designs
https://scholar.archive.org/work/hugp3cga4ngwll7lvd5azfujpa
While quantum circuits built from two-particle dual-unitary (maximally entangled) operators serve as minimal models of typically nonintegrable many-body systems, the construction and characterization of dual-unitary operators themselves are only partially understood. A nonlinear map on the space of unitary operators was proposed in PRL. 125, 070501 (2020) that results in operators being arbitrarily close to dual unitaries. Here we study the map analytically for the two-qubit case describing the basins of attraction, fixed points, and rates of approach to dual unitaries. A subset of dual-unitary operators having maximum entangling power are 2-unitary operators or perfect tensors, and are equivalent to four-party absolutely maximally entangled states. It is known that they only exist if the local dimension is larger than d=2. We use the nonlinear map, and introduce stochastic variants of it, to construct explicit examples of new dual and 2-unitary operators. A necessary criterion for their local unitary equivalence to distinguish classes is also introduced and used to display various concrete results and a conjecture in d=3. It is known that orthogonal Latin squares provide a "classical combinatorial design" for constructing permutations that are 2-unitary. We extend the underlying design from classical to genuine quantum ones for general dual-unitary operators and give an example of what might be the smallest sized genuinely quantum design of a 2-unitary in d=4.Suhail Ahmad Rather, S. Aravinda, Arul Lakshminarayanwork_hugp3cga4ngwll7lvd5azfujpaMon, 21 Nov 2022 00:00:00 GMTReport of the Frontier For Rare Processes and Precision Measurements
https://scholar.archive.org/work/6lrmujql5zgi5khexexrip5bly
This is the Snowmass 2021 Rare and Precision Frontier Report. The Rare Processes and Precision Measurements Frontier, referred to as the "Rare and Precision Frontier", or RPF, encompasses searches for extremely rare processes or tiny deviations from the Standard Model (SM) that can be studied with intense sources and high-precision detectors. Our community studies have identified several unique research opportunities that may pin down the scales associated with New Physics (NP) interactions and constrain the couplings of possible new degrees of freedom. Searches for rare flavor transition processes and precision measurements are indispensable probes of flavor and fundamental symmetries, and provide insights into physics that manifests itself at higher energy or through weaker interactions than those directly accessible at high-energy colliders.Marina Artuso, Robert H. Bernstein, Alexey A. Petrovwork_6lrmujql5zgi5khexexrip5blyThu, 17 Nov 2022 00:00:00 GMTModelling the free energy of solvation: from data-driven to statistical mechanical approaches
https://scholar.archive.org/work/mogwa64tkbf5vovzxufciqff6m
The Gibbs free energy of solvation for a given solute in a solvent, usually considered at infinite dilution, provides a simple thermodynamic description of the solution and is related to numerous solvation properties. In the context of solution chemistry, it provides a route to understanding the effect of solvents on equilibrium constants and reaction rates. In the discovery of new drugs, the effectiveness of a drug depends in part on solubility and permeability, leading to the prediction of Gibbs free energy of solvation values to be used frequently in quantitative drug design. Given the importance of the Gibbs free energy of solvation, many predictive tools were developed, spanning quantum mechanical (QM) methods, empirical methods, and classical methods. Of note, empirical methods are data-driven approaches through statistical learning. In this work, we assembled a database of experimental Gibbs free energies of solvation and a corresponding set of 9 quantum mechanical (QM) solute descriptors and 12 bulk solvent descriptors. We also partitioned the Gibbs free energy of solvation into an electrostatic term and a nonelectrostatic term. The electrostatic term is the difference between the electronic energies of a solute in a vacuum and solvent obtained though using the X3LYP/6-31 G(d,p) electronic structure method and the Polarizable Continuum Model (PCM). We then obtain a separate database of derived nonelectrostatic energies alongside the Gibbs free energy of solvation database which are used to develop models using statistical and regression methodologies such as partial least squares (PLS), quadratic partial least squares (QPLS) and automatic learning of algebraic models for optimisation (ALAMO). We then carry out a systematic comparison of various activity coefficients, data-driven models, an equation of state, and a hybrid QM/activity coefficient model. Notable models include the Dortmund version of UNIFAC model (modUNIFAC (Do)), the statistical associating fluid theory (SAFT- γ Mie), and the conductor-like [...]Nur Redzuan Nur Jazlan, Claire Adjiman, Amparo Galindowork_mogwa64tkbf5vovzxufciqff6mThu, 17 Nov 2022 00:00:00 GMTNear-Term Quantum Computing Techniques: Variational Quantum Algorithms, Error Mitigation, Circuit Compilation, Benchmarking and Classical Simulation
https://scholar.archive.org/work/5cil662o5bclbky4ypzlw2akiq
Quantum computing is a game-changing technology for global academia, research centers and industries including computational science, mathematics, finance, pharmaceutical, materials science, chemistry and cryptography. Although it has seen a major boost in the last decade, we are still a long way from reaching the maturity of a full-fledged quantum computer. That said, we will be in the Noisy-Intermediate Scale Quantum (NISQ) era for a long time, working on dozens or even thousands of qubits quantum computing systems. An outstanding challenge, then, is to come up with an application that can reliably carry out a nontrivial task of interest on the near-term quantum devices with non-negligible quantum noise. To address this challenge, several near-term quantum computing techniques, including variational quantum algorithms, error mitigation, quantum circuit compilation and benchmarking protocols, have been proposed to characterize and mitigate errors, and to implement algorithms with a certain resistance to noise, so as to enhance the capabilities of near-term quantum devices and explore the boundaries of their ability to realize useful applications. Besides, the development of near-term quantum devices is inseparable from the efficient classical simulation, which plays a vital role in quantum algorithm design and verification, error-tolerant verification and other applications. This review will provide a thorough introduction of these near-term quantum computing techniques, report on their progress, and finally discuss the future prospect of these techniques, which we hope will motivate researchers to undertake additional studies in this field.He-Liang Huang, Xiao-Yue Xu, Chu Guo, Guojing Tian, Shi-Jie Wei, Xiaoming Sun, Wan-Su Bao, Gui-Lu Longwork_5cil662o5bclbky4ypzlw2akiqThu, 17 Nov 2022 00:00:00 GMTSearching for Singlet Fission Candidates with Many-Body Perturbation Theory and Machine Learning
https://scholar.archive.org/work/f537ccm2rrekzi5auw2yqm2bxa
Singlet fission (SF) is a photophysical process where one singlet-state exciton converts into two triplet-state excitons. SF is considered as a possible approach to surpass the Shockley-Queisser limit and has started wide discussions in the past decade. However, commercialization of SF-based photovoltaics remains in incubation due to the lack of practical SF materials. To tackle this bottleneck, performing large-scale simulation and screening molecular materials database to search for SF candidates with promising properties is suggested. One of the decisive excitonic properties directing the fission process, the SF thermodynamic driving force can be calculated with the state-of-the-art, many-body perturbation theory (MBPT) under the GW approximation paired with Bethe-Salpeter equation (BSE). However, GW+BSE calculation is too cumbersome to be selected as the screening scheme for a database with tens of thousands of molecular crystals. Statistical inference is hence introduced to maximize the probability of discovering SF candidates with minimized computational cost. To realize this process, a hierarchical screening workflow incorporating materials science and machine learning (MatML Workflow) was designed and implemented.Xingyu Liuwork_f537ccm2rrekzi5auw2yqm2bxaWed, 16 Nov 2022 00:00:00 GMTComputing with B-series
https://scholar.archive.org/work/oezarsjypfhtjjyirnbormzgp4
We present BSeries.jl, a Julia package for the computation and manipulation of B-series, which are a versatile theoretical tool for understanding and designing discretizations of differential equations. We give a short introduction to the theory of B-series and associated concepts and provide examples of their use, including method composition and backward error analysis. The associated software is highly performant and makes it possible to work with B-series of high order.David I. Ketcheson, Hendrik Ranochawork_oezarsjypfhtjjyirnbormzgp4Tue, 15 Nov 2022 00:00:00 GMTDesign and training of deep reinforcement learning agents
https://scholar.archive.org/work/v4bnexxtdbgkvdgyu4jub7gulm
Deep reinforcement learning is a field of research at the intersection of reinforcement learning and deep learning. On one side, the problem that researchers address is the one of reinforcement learning: to act efficiently. A large number of algorithms were developed decades ago in this field to update value functions and policies, explore, and plan. On the other side, deep learning methods provide powerful function approximators to address the problem of representing functions such as policies, value functions, and models. The combination of ideas from these two fields offers exciting new perspectives. However, building successful deep reinforcement learning experiments is particularly difficult due to the large number of elements that must be combined and adjusted appropriately. This thesis proposes a broad overview of the organization of these elements around three main axes: agent design, environment design, and infrastructure design. Arguably, the success of deep reinforcement learning research is due to the tremendous amount of effort that went into each of them, both from a scientific and engineering perspective, and their diffusion via open source repositories. For each of these three axes, a dedicated part of the thesis describes a number of related works that were carried out during the doctoral research. The first part, devoted to the design of agents, presents two works. The first one addresses the problem of applying discrete action methods to large multidimensional action spaces. A general method called action branching is proposed, and its effectiveness is demonstrated with a novel agent, named BDQ, applied to discretized continuous action spaces. The second work deals with the problem of maximizing the utility of a single transition when learning to achieve a large number of goals. In particular, it focuses on learning to reach spatial locations in games and proposes a new method called Q-map to do so efficiently. An exploration mechanism based on this method is then used to demonstrate the effect [...]Fabio Pardo, Petar Kormushev, Andrew Davison, Dyson Technology Limited (Firm)work_v4bnexxtdbgkvdgyu4jub7gulmTue, 15 Nov 2022 00:00:00 GMTDagstuhl Reports, Volume 12, Issue 4, April 2022, Complete Issue
https://scholar.archive.org/work/oiijemxg5zhmzehjc3gy2mvkhm
Dagstuhl Reports, Volume 12, Issue 4, April 2022, Complete Issuework_oiijemxg5zhmzehjc3gy2mvkhmMon, 14 Nov 2022 00:00:00 GMTMachine Learning Diffusion Monte Carlo Forces
https://scholar.archive.org/work/sztvv523kbdirp3rvgda5her7a
Diffusion Monte Carlo (DMC) is one of the most accurate techniques available for calculating the electronic properties of molecules and materials, yet it often remains a challenge to economically compute forces using this technique. As a result, ab initio molecular dynamics simulations and geometry optimizations that employ Diffusion Monte Carlo forces are often out of reach. One potential approach for accelerating the computation of "DMC forces" is to machine learn these forces from DMC energy calculations. In this work, we employ Behler-Parrinello Neural Networks to learn DMC forces from DMC energy calculations for geometry optimization and molecular dynamics simulations of small molecules. We illustrate the unique challenges that stem from learning forces without explicit force data and from noisy energy data by making rigorous comparisons of potential energy surface, dynamics, and optimization predictions among ab initio Density Functional Theory (DFT) simulations and machine learning models trained on DFT energies with forces, DFT energies without forces, and DMC energies without forces. We show for three small molecules - C2, H2O, and CH3Cl - that machine learned DMC dynamics can reproduce average bond lengths and angles within a few percent of known experimental results at a 100th of the typical cost. Our work describes a much-needed means of performing dynamics simulations on high-accuracy, DMC PESs and for generating DMC-quality molecular geometries given current algorithmic constraints.Cancan Huang, Brenda M. Rubensteinwork_sztvv523kbdirp3rvgda5her7aMon, 14 Nov 2022 00:00:00 GMTGroup-Equivariant Neural Networks with Fusion Diagrams
https://scholar.archive.org/work/szrqn52ujjd3ldr5fksjpvqyya
Many learning tasks in physics and chemistry involve global spatial symmetries as well as permutational symmetry between particles. The standard approach to such problems is equivariant neural networks, which employ tensor products between various tensors that transform under the spatial group. However, as the number of different tensors and the complexity of relationships between them increases, the bookkeeping associated with ensuring parsimony as well as equivariance quickly becomes nontrivial. In this paper, we propose to use fusion diagrams, a technique widely used in simulating SU(2)-symmetric quantum many-body problems, to design new equivariant components for use in equivariant neural networks. This yields a diagrammatic approach to constructing new neural network architectures. We show that when applied to particles in a given local neighborhood, the resulting components, which we call fusion blocks, are universal approximators of any continuous equivariant function defined on the neighborhood. As a practical demonstration, we incorporate a fusion block into a pre-existing equivariant architecture (Cormorant) and show that it improves performance on benchmark molecular learning tasks.Zimu Li, Han Zheng, Erik Thiede, Junyu Liu, Risi Kondorwork_szrqn52ujjd3ldr5fksjpvqyyaMon, 14 Nov 2022 00:00:00 GMTCFLOBDDs: Context-Free-Language Ordered Binary Decision Diagrams
https://scholar.archive.org/work/lmgksqlwyndfvfv4mgwpxhokcu
This paper presents a new compressed representation of Boolean functions, called CFLOBDDs (for Context-Free-Language Ordered Binary Decision Diagrams). They are essentially a plug-compatible alternative to BDDs (Binary Decision Diagrams), and hence useful for representing certain classes of functions, matrices, graphs, relations, etc. in a highly compressed fashion. CFLOBDDs share many of the good properties of BDDs, but--in the best case--the CFLOBDD for a Boolean function can be exponentially smaller than any BDD for that function. Compared with the size of the decision tree for a function, a CFLOBDD--again, in the best case--can give a double-exponential reduction in size. They have the potential to permit applications to (i) execute much faster, and (ii) handle much larger problem instances than has been possible heretofore. CFLOBDDs are a new kind of decision diagram that go beyond BDDs (and their many relatives). The key insight is a new way to reuse sub-decision-diagrams: components of CFLOBDDs are structured hierarchically, so that sub-decision-diagrams can be treated as standalone "procedures" and reused. We applied CFLOBDDs to the problem of simulating quantum circuits, and found that for several standard problems the improvement in scalability--compared to simulation using BDDs--is quite dramatic. In particular, the number of qubits that could be handled using CFLOBDDs was larger, compared to BDDs, by a factor of 128x for GHZ; 1,024x for BV; 8,192x for DJ; and 128x for Grover's algorithm. (With a 15-minute timeout, the number of qubits that CFLOBDDs can handle are 65,536 for GHZ, 524,288 for BV; 4,194,304 for DJ; and 4,096 for Grover's Algorithm.)Meghana Sistla, Swarat Chaudhuri, Thomas Repswork_lmgksqlwyndfvfv4mgwpxhokcuSun, 13 Nov 2022 00:00:00 GMTTensor diagrams and cluster combinatorics at punctures
https://scholar.archive.org/work/mos5oumlwfgddbfxozsf25fm6q
Fock and Goncharov introduced a family of cluster algebras associated with the moduli of SL(k)-local systems on a marked surface with extra decorations at marked points. We study this family from an algebraic and combinatorial perspective, emphasizing the structures which arise when the surface has punctures. When k is 2, these structures are the tagged arcs and tagged triangulations of Fomin, Shapiro, and Thurston. For higher k, the tagging of arcs is replaced by a Weyl group action at punctures discovered by Goncharov and Shen. We pursue a higher analogue of a tagged triangulation in the language of tensor diagrams, extending work of Fomin and the second author, and we formulate skein-algebraic tools for calculating in these cluster algebras. We analyze the finite mutation type examples in detail.Chris Fraser, Pavlo Pylyavskyywork_mos5oumlwfgddbfxozsf25fm6qThu, 10 Nov 2022 00:00:00 GMTTowards near-term quantum simulation of materials
https://scholar.archive.org/work/pzwomhk5nvcylcmk7cimxgt2om
Simulation of materials is one of the most promising applications of quantum computers. On near-term hardware the crucial constraint on these simulations is circuit depth. Many quantum simulation algorithms rely on a layer of unitary evolutions generated by each term in a Hamiltonian. This appears in time-dynamics as a single Trotter step, and in variational quantum eigensolvers under the Hamiltonian variational ansatz as a single ansatz layer. We present a new quantum algorithm design for materials modelling where the depth of a layer is independent of the system size. This design takes advantage of the locality of materials in the Wannier basis and employs a tailored fermionic encoding that preserves locality. We analyse the circuit costs of this approach and present a compiler that transforms density functional theory data into quantum circuit instructions – connecting the physics of the material to the simulation circuit. The compiler automatically optimises circuits at multiple levels, from the base gate level to optimisations derived from the physics of the specific target material. We present numerical results for materials spanning a wide structural and technological range. Our results demonstrate a reduction of many orders of magnitude in circuit depth over standard prior methods that do not consider the structure of the Hamiltonian. For example our results improve resource requirements for Strontium Vanadate (SrVO_3) from 864 to 180 qubits for a 3×3×3 lattice, and the circuit depth of a single Trotter or variational layer from 7.5× 10^8 to depth 884. Although this is still beyond current hardware, our results show that materials simulation may be feasible on quantum computers without necessarily requiring scalable, fault-tolerant quantum computers, provided quantum algorithm design incorporates understanding of the materials and applications.Laura Clinton, Toby Cubitt, Brian Flynn, Filippo Maria Gambetta, Joel Klassen, Ashley Montanaro, Stephen Piddock, Raul A. Santos, Evan Sheridanwork_pzwomhk5nvcylcmk7cimxgt2omThu, 10 Nov 2022 00:00:00 GMTQuantum Power Flows: From Theory to Practice
https://scholar.archive.org/work/jup3yqxc4rfebn3q5cm5yg5tjm
Climate change is becoming one of the greatest challenges to the sustainable development of modern society. Renewable energies with low density greatly complicate the online optimization and control processes, where modern advanced computational technologies, specifically quantum computing, have significant potential to help. In this paper, we discuss applications of quantum computing algorithms toward state-of-the-art smart grid problems. We suggest potential, exponential quantum speedup by the use of the Harrow-Hassidim-Lloyd (HHL) algorithms for sparse matrix inversions in power-flow problems. However, practical implementations of the algorithm are limited by the noise of quantum circuits, the hardness of realizations of quantum random access memories (QRAM), and the depth of the required quantum circuits. We benchmark the hardware and software requirements from the state-of-the-art power-flow algorithms, including QRAM requirements from hybrid phonon-transmon systems, and explicit gate counting used in HHL for explicit realizations. We also develop near-term algorithms of power flow by variational quantum circuits and implement real experiments for 6 qubits with a truncated version of power flows.Junyu Liu, Han Zheng, Masanori Hanada, Kanav Setia, Dan Wuwork_jup3yqxc4rfebn3q5cm5yg5tjmThu, 10 Nov 2022 00:00:00 GMTThe hull of two classical propagation rules and their applications
https://scholar.archive.org/work/a2qnhphxprc4xmqnahamgpwhii
Propagation rules are of great help in constructing good linear codes. Both Euclidean and Hermitian hulls of linear codes perform an important part in coding theory. In this paper, we consider these two aspects together and determine the dimensions of Euclidean and Hermitian hulls of two classical propagation rules, namely, the direct sum construction and the (𝐮,𝐮+𝐯)-construction. Some new criteria for resulting codes derived from these two propagation rules being self-dual, self-orthogonal or linear complement dual (LCD) codes are given. As applications, we construct some linear codes with prescribed hull dimensions and many new binary, ternary Euclidean formally self-dual (FSD) LCD codes, quaternary Hermitian FSD LCD codes and good quaternary Hermitian LCD codes which are optimal or have best or almost best known parameters according to Datebase at http://www.codetables.de. Moreover, our methods contributes positively to improve the lower bounds on the minimum distance of known LCD codes.Yang Li, Shixin Zhuwork_a2qnhphxprc4xmqnahamgpwhiiWed, 09 Nov 2022 00:00:00 GMTQuantum Search Algorithm for Binary Constant Weight Codes
https://scholar.archive.org/work/egpczubqpza4dhdwz67vtqvmby
A binary constant weight code is a type of error-correcting code with a wide range of applications. The problem of finding a binary constant weight code has long been studied as a combinatorial optimization problem in coding theory. In this paper, we propose a quantum search algorithm for binary constant weight codes. Specifically, the search problem is newly formulated as a quadratic unconstrained binary optimization (QUBO) and Grover adaptive search (GAS) is used for providing the quadratic speedup. Focusing on the inherent structure of the problem, we derive an upper bound on the minimum of the objective function value and a lower bound on the exact number of solutions. In our algebraic analysis, it was found that this proposed algorithm is capable of reducing the number of required qubits, thus enhancing the feasibility. Additionally, our simulations demonstrated that it reduces the query complexities by 63% in the classical domain and 31% in the quantum domain. The proposed approach may be useful for other quantum search algorithms and optimization problems.Kein Yukiyoshi, Naoki Ishikawawork_egpczubqpza4dhdwz67vtqvmbyWed, 09 Nov 2022 00:00:00 GMTPerformance of Localized-Orbital Coupled Cluster Approaches for the Conformational Energies of Longer n-alkane Chains
https://scholar.archive.org/work/vykudgbed5gh3mv6bjfiwgvct4
We report an update and enhancement of the ACONFL (conformer energies of large alkanes [Ehlert, S.; Grimme, S.; Hansen, A. J. Phys. Chem. A 2022, 126, 3521-3535]) dataset. For the ACONF12 (n-dodecane) subset, we report basis set limit canonical CCSD(T) reference data obtained from MP2-F12/cc-pVT,QZ-F12 extrapolation, [CCSD(F12*)-MP2-F12]/aug-cc-pVTZ-F12, and a (T) correction from conventional CCSD(T)/aug-cc-pVD,TZ calculations. Then we explored the performance of a variety of single and composite localized-orbital CCSD(T) approximations, ultimately finding an affordable LNO-CCSD(T)-based post-MP2 correction that agrees to 0.008 kcal/mol MAD (mean absolute deviation) with the revised canonical reference data. In tandem with canonical MP2-F12/CBS extrapolation, this was then used to re-evaluate the ACONF16 and ACONF20 subsets for n-hexadecane and n-icosane, respectively. A revised ACONFL set was thus obtained. It was then used to assess the performance of different localized-orbital coupled cluster approaches, such as PNO-LCCSD(T) as implemented in MOLPRO, DLPNO-CCSD (T1) as implemented in ORCA, and LNO-CCSD(T) as implemented in MRCC, at their various accuracy settings. A three-tier LNO-CCSD(T)-based composite scheme disagrees by only 0.02 kcal/mol from the revised ACONFL reference data. When extrapolated to the complete PNO space limit, DLPNO-CCSD(T1, Tight) and a composite method are the best picks among all the localized coupled cluster methods tested for the dodecane conformers. Dispersion-corrected dRPA-based double hybrids perform remarkably well for the ACONFL set. While the revised reference data do not affect any conclusions on the less accurate methods, they may upend orderings for more accurate methods with error statistics on the same order as the difference between reference datasets.Golokesh Santra, Jan M. L. Martinwork_vykudgbed5gh3mv6bjfiwgvct4Wed, 09 Nov 2022 00:00:00 GMTSpin squeezed GKP codes for quantum error correction in atomic ensembles
https://scholar.archive.org/work/tfaajmmfrfdvrkseuafumfwxlm
GKP codes encode a qubit in displaced phase space combs of a continuous-variable (CV) quantum system and are useful for correcting a variety of high-weight photonic errors. Here we propose atomic ensemble analogues of the single-mode CV GKP code by using the quantum central limit theorem to pull back the phase space structure of a CV system to the compact phase space of a quantum spin system. We study the optimal recovery performance of these codes under error channels described by stochastic relaxation and isotropic ballistic dephasing processes using the diversity combining approach for calculating channel fidelity. We find that the spin GKP codes outperform other spin system codes such as cat codes or binomial codes. Our spin GKP codes based on the two-axis countertwisting interaction and superpositions of SU(2) coherent states are direct spin analogues of the finite-energy CV GKP codes, whereas our codes based on one-axis twisting do not yet have well-studied CV analogues. An implementation of the spin GKP codes is proposed which uses the linear combination of unitaries method, applicable to both the CV and spin GKP settings. Finally, we discuss a fault-tolerant approximate gate set for quantum computing with spin GKP-encoded qubits, obtained by translating gates from the CV GKP setting using quantum central limit theorem.Sivaprasad Omanakuttan, T.J. Volkoffwork_tfaajmmfrfdvrkseuafumfwxlmWed, 09 Nov 2022 00:00:00 GMTSolid Interfaces in Lithium Ion Batteries
https://scholar.archive.org/work/ov3cy4246fgjjidjwc6khyr26m
It is difficult to imagine modern life without batteries: they are present in our mobile phones, laptop computers, automotive vehicles, and even more vital devices, such as pacemakers. Most of these gadgets are powered by lithium (Li) ion batteries, in part due to their high voltage window and relative longevity. While this technology has had major successes, at the current rate of progress, it is unlikely to meet the mid-century global demands related to full de-carbonization and interruption of use of fossil fuels for transportation and energy generation. The replacement of currently used anodes by Li metal is one of the most promising puzzle pieces involved in the solution to this problem. However, a myriad of obstacles hinders its commercialization, many of which are related to phenomena happening at the interface between the anode and the electrolyte. In this thesis, some of the interfaces present in Li-ion and Li-metal batteries are explored. From a purely mathematical standpoint, interfaces are simply two-dimensional (2D) constructs. However, in real materials, their properties are determined by the atomic structure in their vicinity, thus making them more akin to 2.5D systems. Given the high complexity of such interfacial structures, this thesis is organized in a bottom-up approach. First, we study the possibility of using twisted bilayer graphene, a novel material that, similar to interfaces, can be described as being 2.5D, for electrochemical applications, including Li-ion technologies. Next, some of the interface-related issues that plague Li-ion and Li-metal batteries are scrutinized. Among them, this work focuses on the formation and growth of dendrites, on the ionic conductivity of components of the solid electrolyte interphase (SEI), and on the development of surface voids and pits during the discharge process. Using a well-established electrodeposition model for solid electrolytes, a carefully engineered polymer composite separator is conceptualized to harness advantageous properties of its c [...]Victor Venturiwork_ov3cy4246fgjjidjwc6khyr26mTue, 08 Nov 2022 00:00:00 GMTFaster Exact Exchange for Solids via occ-RI-K: Application to Combinatorially Optimized Range-Separated Hybrid Functionals for Simple Solids with Pseudopotentials Near the Basis Set Limit
https://scholar.archive.org/work/vo4t7c6quneupc4vnkf3frcbtm
In this work, we developed and showcased the occ-RI-K algorithm to compute the exact exchange contribution in density functional calculations of solids near the basis set limit. Within the gaussian planewave (GPW) density fitting, our algorithm achieves a 1-2 orders of magnitude speedup compared to conventional GPW algorithms. Since our algorithm is well-suited for simulations with large basis sets, we applied it to 12 hybrid density functionals with pseudopotentials and a large uncontracted basis set to assess their performance on band gaps of 25 simple solids near the basis set limit. The largest calculation performed in this work involves 16 electrons and 350 basis functions in the unit cell utilizing a 6x6x6 k-mesh. With 20-27% exact exchange, global hybrid functionals (B3LYP, PBE0, revPBE0, B97-3, SCAN0) perform similarly with a root-mean-square-deviation (RMSD) of 0.61-0.77 eV while other global hybrid functionals such as M06-2X (2.02 eV) and MN15 (1.05 eV) show higher RMSD due to their increased fraction of exact exchange. A short-range hybrid functional, HSE achieves a similar RMSD (0.76 eV) but shows a noticeable underestimation of band gaps due to the complete lack of long-range exchange. We found that two combinatorially optimized range-separated hybrid functionals, ωB97X-rV (3.94 eV) and ωB97M-rV (3.40 eV), and the two other range separated hybrid functionals, CAM-B3LYP (2.41 eV) and CAM-QTP01 (4.16 eV), significantly overestimate the band gap because of their high fraction of long-range exact exchange. Given the failure of ωB97X-rV and ωB97M-rV, we have yet to find a density functional that offers consistent performance for both molecules and solids. Our algorithm development and density functional assessment will serve as a stepping stone towards developing more accurate hybrid functionals and applying them to practical applications.Joonho Lee and Adam Rettig and Xintian Feng and Evgeny Epifanovsky and Martin Head-Gordonwork_vo4t7c6quneupc4vnkf3frcbtmTue, 08 Nov 2022 00:00:00 GMT