10 results on '"Baker, Jack S."'
Search Results
2. GradDFT. A software library for machine learning enhanced density functional theory.
- Author
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M. Casares, Pablo A., Baker, Jack S., Medvidović, Matija, Reis, Roberto dos, and Arrazola, Juan Miguel
- Subjects
- *
DENSITY functional theory , *MACHINE learning , *POTENTIAL energy surfaces , *COMPUTATIONAL chemistry , *MATERIALS science - Abstract
Density functional theory (DFT) stands as a cornerstone method in computational quantum chemistry and materials science due to its remarkable versatility and scalability. Yet, it suffers from limitations in accuracy, particularly when dealing with strongly correlated systems. To address these shortcomings, recent work has begun to explore how machine learning can expand the capabilities of DFT: an endeavor with many open questions and technical challenges. In this work, we present GradDFT a fully differentiable JAX-based DFT library, enabling quick prototyping and experimentation with machine learning-enhanced exchange–correlation energy functionals. GradDFT employs a pioneering parametrization of exchange–correlation functionals constructed using a weighted sum of energy densities, where the weights are determined using neural networks. Moreover, GradDFT encompasses a comprehensive suite of auxiliary functions, notably featuring a just-in-time compilable and fully differentiable self-consistent iterative procedure. To support training and benchmarking efforts, we additionally compile a curated dataset of experimental dissociation energies of dimers, half of which contain transition metal atoms characterized by strong electronic correlations. The software library is tested against experimental results to study the generalization capabilities of a neural functional across potential energy surfaces and atomic species, as well as the effect of training data noise on the resulting model accuracy. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
3. Original Research by Young Twinkle Students (ORBYTS): When Can Students Start Performing Original Research?
- Author
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Sousa-Silva, Clara, McKemmish, Laura K., Chubb, Katy L., Gorman, Marie N., Baker, Jack S., Barton, Emma J., Rivlin, Tom, and Tennyson, Jonathan
- Abstract
Involving students in state-of-the-art research from an early age eliminates the idea that science is only for the scientists and empowers young people to explore STEM (Science, Technology, Engineering and Maths) subjects. It is also a great opportunity to dispel harmful stereotypes about who is suitable for STEM careers, while leaving students feeling engaged in modern science and the scientific method. As part of the Twinkle Space Mission's educational programme, EduTwinkle, students between the ages of 15 and 18 have been performing original research associated with the exploration of space since January 2016. The student groups have each been led by junior researchers--PhD and post-doctoral scientists--who themselves benefit substantially from the opportunity to supervise and manage a research project. This research aims to meet a standard for publication in peer-reviewed journals. At present the research of two ORBYTS teams have been published, one in the Astrophysical Journal Supplement Series and another in JQSRT; we expect more papers to follow. Here we outline the necessary steps for a productive scientific collaboration with school children, generalising from the successes and downfalls of the pilot ORBYTS projects.
- Published
- 2018
- Full Text
- View/download PDF
4. A Quantum-Inspired Binary Optimization Algorithm for Representative Selection
- Author
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Hughes, Anna G., Baker, Jack S., and Radha, Santosh Kumar
- Subjects
FOS: Economics and business ,Quantum Physics ,Quantitative Finance - Computational Finance ,FOS: Physical sciences ,Computational Finance (q-fin.CP) ,Computational Physics (physics.comp-ph) ,Quantum Physics (quant-ph) ,Physics - Computational Physics - Abstract
Advancements in quantum computing are fuelling emerging applications across disciplines, including finance, where quantum and quantum-inspired algorithms can now make market predictions, detect fraud, and optimize portfolios. Expanding this toolbox, we propose the selector algorithm: a method for selecting the most representative subset of data from a larger dataset. The selected subset includes data points that simultaneously meet the two requirements of being maximally close to neighboring data points and maximally far from more distant data points where the precise notion of distance is given by any kernel or generalized similarity function. The cost function encoding the above requirements naturally presents itself as a Quadratic Unconstrained Binary Optimization (QUBO) problem, which is well-suited for quantum optimization algorithms - including quantum annealing. While the selector algorithm has applications in multiple areas, it is particularly useful in finance, where it can be used to build a diversified portfolio from a more extensive selection of assets. After experimenting with synthetic datasets, we show two use cases for the selector algorithm with real data: (1) approximately reconstructing the NASDAQ 100 index using a subset of stocks, and (2) diversifying a portfolio of cryptocurrencies. In our analysis of use case (2), we compare the performance of two quantum annealers provided by D-Wave Systems., 11 pages, 9 figures
- Published
- 2023
5. Quantum Variational Rewinding for Time Series Anomaly Detection
- Author
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Baker, Jack S., Horowitz, Haim, Radha, Santosh Kumar, Fernandes, Stenio, Jones, Colin, Noorani, Noorain, Skavysh, Vladimir, Lamontangne, Philippe, and Sanders, Barry C.
- Subjects
Quantum Physics ,FOS: Physical sciences ,Quantum Physics (quant-ph) - Abstract
Electron dynamics, financial markets and nuclear fission reactors, though seemingly unrelated, all produce observable characteristics evolving with time. Within this broad scope, departures from normal temporal behavior range from academically interesting to potentially catastrophic. New algorithms for time series anomaly detection (TAD) are therefore certainly in demand. With the advent of newly accessible quantum processing units (QPUs), exploring a quantum approach to TAD is now relevant and is the topic of this work. Our approach - Quantum Variational Rewinding, or, QVR - trains a family of parameterized unitary time-devolution operators to cluster normal time series instances encoded within quantum states. Unseen time series are assigned an anomaly score based upon their distance from the cluster center, which, beyond a given threshold, classifies anomalous behavior. After a first demonstration with a simple and didactic case, QVR is used to study the real problem of identifying anomalous behavior in cryptocurrency market data. Finally, multivariate time series from the cryptocurrency use case are studied using IBM's Falcon r5.11H family of superconducting transmon QPUs, where anomaly score errors resulting from hardware noise are shown to be reducible by as much as 20% using advanced error mitigation techniques., Main article: 11 pages and 4 figures. Supplemental material: 11 pages, 6 figures and 2 tables
- Published
- 2022
6. Large scale and linear scaling DFT with the CONQUEST code.
- Author
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Nakata, Ayako, Baker, Jack S., Mujahed, Shereif Y., Poulton, Jack T. L., Arapan, Sergiu, Lin, Jianbo, Raza, Zamaan, Yadav, Sushma, Truflandier, Lionel, Miyazaki, Tsuyoshi, and Bowler, David R.
- Subjects
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DENSITY functional theory , *MOLECULAR dynamics , *CODING theory - Abstract
We survey the underlying theory behind the large-scale and linear scaling density functional theory code, conquest, which shows excellent parallel scaling and can be applied to thousands of atoms with diagonalization and millions of atoms with linear scaling. We give details of the representation of the density matrix and the approach to finding the electronic ground state and discuss the implementation of molecular dynamics with linear scaling. We give an overview of the performance of the code, focusing in particular on the parallel scaling, and provide examples of recent developments and applications. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
7. Wasserstein Solution Quality and the Quantum Approximate Optimization Algorithm: A Portfolio Optimization Case Study
- Author
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Baker, Jack S. and Radha, Santosh Kumar
- Subjects
FOS: Economics and business ,Quantum Physics ,Quantitative Finance - Computational Finance ,Portfolio Management (q-fin.PM) ,FOS: Physical sciences ,Computational Finance (q-fin.CP) ,Quantum Physics (quant-ph) ,Quantitative Finance - Portfolio Management - Abstract
Optimizing of a portfolio of financial assets is a critical industrial problem which can be approximately solved using algorithms suitable for quantum processing units (QPUs). We benchmark the success of this approach using the Quantum Approximate Optimization Algorithm (QAOA); an algorithm targeting gate-model QPUs. Our focus is on the quality of solutions achieved as determined by the Normalized and Complementary Wasserstein Distance, $\eta$, which we present in a manner to expose the QAOA as a transporter of probability. Using $\eta$ as an application specific benchmark of performance, we measure it on selection of QPUs as a function of QAOA circuit depth $p$. At $n = 2$ (2 qubits) we find peak solution quality at $p=5$ for most systems and for $n = 3$ this peak is at $p=4$ on a trapped ion QPU. Increasing solution quality with $p$ is also observed using variants of the more general Quantum Alternating Operator Ans\"{a}tz at $p=2$ for $n = 2$ and $3$ which has not been previously reported. In identical measurements, $\eta$ is observed to be variable at a level exceeding the noise produced from the finite number of shots. This suggests that variability itself should be regarded as a QPU performance benchmark for given applications. While studying the ideal execution of QAOA, we find that $p=1$ solution quality degrades when the portfolio budget $B$ approaches $n/2$ and increases when $B \approx 1$ or $n-1$. This trend directly corresponds to the binomial coefficient $nCB$ and is connected with the recently reported phenomenon of reachability deficits. Derivative-requiring and derivative-free classical optimizers are benchmarked on the basis of the achieved $\eta$ beyond $p=1$ to find that derivative-free optimizers are generally more effective for the given computational resources, problem sizes and circuit depths., Comment: 21 pages and 11 Figures in main article, 8 pages, 5 Figures and 3 tables in Supplemental Material
- Published
- 2022
8. Polar Morphologies from First Principles: PbTiO3 Films on SrTiO3 Substrates and the p(2×Λ) Surface Reconstruction.
- Author
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Baker, Jack S. and Bowler, David R.
- Abstract
Low‐dimensional structures comprised of ferroelectric (FE) PbTiO3 (PTO) and quantum paraelectric SrTiO3 (STO) are hosts to complex polarization textures such as polar waves, flux‐closure domains, and polar skyrmion phases. Density functional theory (DFT) simulations can provide insight into this order, but are limited by the computational effort required. Within DFT, the novel multi‐site support function method is used to reduce the solution time for the electronic groundstate whilst preserving high accuracy. This allows for large‐scale simulations of PTO films on STO substrates with system sizes >2000 atoms. In the ultrathin limit, the polar wave texture with cylindrical chiral bubbles emerges as an intermediate phase between full‐flux‐closure domains and in‐plane polarization. This is driven by an internal bias field born of the compositionally broken inversion symmetry in the [001] direction. Manipulation of this built‐in field informs a new principle of design for control over chiral order on the nanoscale through the careful choice of substrate, surface termination, or use of overlayers. Antiferrodistortive (AFD) order locally interacts with these polar textures giving rise to strong FE/AFD coupling at the PbO terminated surface driving a p(2×Λ) surface reconstruction. This offers another pathway for the local control of ferroelectricity. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
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9. Origin of Ferroelectric Domain Wall Alignment with Surface Trenches in Ultrathin Films.
- Author
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Baker, Jack S. and Bowler, David R.
- Subjects
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THIN films , *ELECTRIC dipole moments , *DOMAIN walls (Ferromagnetism) , *TRENCHES , *DENSITY functional theory , *SUPERLATTICES - Abstract
Engraving trenches on the surfaces of ultrathin ferroelectric (FE) films and superlattices promises control over the orientation and direction of FE domain walls (DWs). Through exploiting the phenomenon of DW-surface trench (ST) parallel alignment, systems where DWs are known for becoming electrical conductors could now become useful nanocircuits using only standard lithographical techniques. Despite this clear application, the microscopic mechanism responsible for the alignment phenomenon has remained elusive. Using ultrathin PbTiO3 films as a model system, we explore this mechanism with large scale density functional theory simulations on as many as 5,136 atoms. Although we expect multiple contributing factors, we show that parallel DW-ST alignment can be well explained by this configuration giving rise to an arrangement of electric dipole moments which best restore polar continuity to the film. These moments preserve the polar texture of the pristine film, thus minimizing ST-induced depolarizing fields. Given the generality of this mechanism, we suggest that STs could be used to engineer other exotic polar textures in a variety of FE nanostructures as supported by the appearance of ST-induced polar cycloidal modulations in this Letter. Our simulations also support experimental observations of ST-induced negative strains which have been suggested to play a role in the alignment mechanism. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
10. Bringing pupils into the ORBYTS of research.
- Author
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McKemmish, Laura K., Chubb, Katy L., Rivlin, Tom, Baker, Jack S., Gorman, Maire N., Heward, Anita, Dunn, William, and Tessenyi, Marcell
- Subjects
SCIENTISTS ,SCIENTIFIC knowledge ,RESEARCH papers (Students) ,STUDENT research ,REPORT writing - Published
- 2017
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