55 research outputs found
Force-free identification of minimum-energy pathways and transition states for stochastic electronic structure theories
Stochastic electronic structure theories, e.g., Quantum Monte Carlo methods,
enable highly accurate total energy calculations which in principle can be used
to construct highly accurate potential energy surfaces. However, their
stochastic nature poses a challenge to the computation and use of forces and
Hessians, which are typically required in algorithms for minimum-energy pathway
(MEP) and transition state (TS) identification, such as the nudged-elastic band
(NEB) algorithm and its climbing image formulation. Here, we present strategies
that utilize the surrogate Hessian line-search method - previously developed
for QMC structural optimization - to efficiently identify MEP and TS structures
without requiring force calculations at the level of the stochastic electronic
structure theory. By modifying the surrogate Hessian algorithm to operate in
path-orthogonal subspaces and on saddle points, we show that it is possible to
identify MEPs and TSs using a force-free QMC approach. We demonstrate these
strategies via two examples, the inversion of the ammonia molecule and an SN2
reaction. We validate our results using Density Functional Theory- and coupled
cluster-based NEB calculations. We then introduce a hybrid DFT-QMC approach to
compute thermodynamic and kinetic quantities - free energy differences, rate
constants, and equilibrium constants - that incorporates
stochastically-optimized structures and their energies, and show that this
scheme improves upon DFT accuracy. Our methods generalize straightforwardly to
other systems and other high-accuracy theories that similarly face challenges
computing energy gradients, paving the way for highly accurate PES mapping,
transition state determination, and thermodynamic and kinetic calculations, at
significantly reduced computational expense
Origin of Metal-Insulator Transitions in Correlated Perovskite Metals
The mechanisms that drive metal-to-insulator transitions (MIT) in correlated
solids are not fully understood. For example, the perovskite (PV) SrCoO3 is a
FM metal while the oxygen-deficient (n-doped) brownmillerite (BM) SrCoO2.5 is
an anti-ferromagnetic (AFM) insulator. Given the magnetic and structural
transitions that accompany the MIT, the driver for such a MIT transition is
unclear. We also observe that the perovskite metals LaNiO3, SrFeO3, and SrCoO3
also undergo MIT when n-doped via high-to-low valence compositional changes.
Also, pressurizing the insulating BM SrCoO2.5 phase, drives a gap closing.
Using DFT and correlated diffusion Monte Carlo approaches we demonstrate that
the ABO3 perovskites most prone to MIT are self hole-doped materials,
reminiscent of a negative charge-transfer system. Upon n-doping away from the
negative-charge transfer metallic phase, an underlying charge-lattice (or
e-phonon) coupling drives the system to a bond-disproportionated gapped state,
thereby achieving ligand hole passivation at certain sites only, leading to
charge-disproportionated states. The size of the gap opened is correlated with
the size of the hole-filling at these ligand sites. This suggests that the
interactions driving the gap opening to realize a MIT even in correlated metals
is the charge-transfer energy, but it couples with the underlying phonons to
enable the transition to the insulating phase. Other orderings (magnetic,
charge, etc.) driven by weaker interactions are secondary and may assist gap
openings at small dopings, but its the charge-transfer energy that
predominantly determines the bandgap, with a negative energy preferring the
metallic phase. This n-doping can be achieved by modulations in stoichiometry
or composition or pressure. Hence, controlling the amount of the ligand-hole is
key in controlling MIT. We compare our predictions to experiments where
possible
Locality Error Free Effective Core Potentials for 3d Transition Metal Elements Developed for the Diffusion Monte Carlo Method
Pseudopotential locality errors have hampered the applications of the
diffusion Monte Carlo (DMC) method in materials containing transition metals,
in particular oxides. We have developed locality error free effective core
potentials, pseudo-Hamiltonians, for transition metals ranging from Cr to Zn.
We have modified a procedure published by some of us in [M.C. Bennett et al,
JCTC 18 (2022)]. We carefully optimized our pseudo-Hamiltonians and achieved
transferability errors comparable to the best semilocal pseudopotentials used
with DMC but without incurring in locality errors. Our pseudo-Hamiltonian set
(named OPH23) bears the potential to significantly improve the accuracy of
many-body-first-principles calculations in fundamental science research of
complex materials involving transition metals
QMCPACK: Advances in the development, efficiency, and application of auxiliary field and real-space variational and diffusion Quantum Monte Carlo
We review recent advances in the capabilities of the open source ab initio
Quantum Monte Carlo (QMC) package QMCPACK and the workflow tool Nexus used for
greater efficiency and reproducibility. The auxiliary field QMC (AFQMC)
implementation has been greatly expanded to include k-point symmetries,
tensor-hypercontraction, and accelerated graphical processing unit (GPU)
support. These scaling and memory reductions greatly increase the number of
orbitals that can practically be included in AFQMC calculations, increasing
accuracy. Advances in real space methods include techniques for accurate
computation of band gaps and for systematically improving the nodal surface of
ground state wavefunctions. Results of these calculations can be used to
validate application of more approximate electronic structure methods including
GW and density functional based techniques. To provide an improved foundation
for these calculations we utilize a new set of correlation-consistent effective
core potentials (pseudopotentials) that are more accurate than previous sets;
these can also be applied in quantum-chemical and other many-body applications,
not only QMC. These advances increase the efficiency, accuracy, and range of
properties that can be studied in both molecules and materials with QMC and
QMCPACK
Software engineering to sustain a high-performance computing scientific application: QMCPACK
We provide an overview of the software engineering efforts and their impact
in QMCPACK, a production-level ab-initio Quantum Monte Carlo open-source code
targeting high-performance computing (HPC) systems. Aspects included are: (i)
strategic expansion of continuous integration (CI) targeting CPUs, using GitHub
Actions runners, and NVIDIA and AMD GPUs in pre-exascale systems, using
self-hosted hardware; (ii) incremental reduction of memory leaks using
sanitizers, (iii) incorporation of Docker containers for CI and
reproducibility, and (iv) refactoring efforts to improve maintainability,
testing coverage, and memory lifetime management. We quantify the value of
these improvements by providing metrics to illustrate the shift towards a
predictive, rather than reactive, sustainable maintenance approach. Our goal,
in documenting the impact of these efforts on QMCPACK, is to contribute to the
body of knowledge on the importance of research software engineering (RSE) for
the sustainability of community HPC codes and scientific discovery at scale.Comment: Accepted at the first US-RSE Conference, USRSE2023,
https://us-rse.org/usrse23/, 8 pages, 3 figures, 4 table
Transductive Learning for Spatial Data Classification
Learning classifiers of spatial data presents several issues, such as the heterogeneity of spatial objects, the implicit definition of spatial relationships among objects, the spatial autocorrelation and the abundance of unlabelled data which potentially convey a large amount of information. The first three issues are due to the inherent structure of spatial units of analysis, which can be easily accommodated if a (multi-)relational data mining approach is considered. The fourth issue demands for the adoption of a transductive setting, which aims to make predictions for a given set of unlabelled data. Transduction is also motivated by the contiguity of the concept of positive autocorrelation, which typically affect spatial phenomena, with the smoothness assumption which characterize the transductive setting. In this work, we investigate a relational approach to spatial classification in a transductive setting. Computational solutions to the main difficulties met in this approach are presented. In particular, a relational upgrade of the nave Bayes classifier is proposed as discriminative model, an iterative algorithm is designed for the transductive classification of unlabelled data, and a distance measure between relational descriptions of spatial objects is defined in order to determine the k-nearest neighbors of each example in the dataset. Computational solutions have been tested on two real-world spatial datasets. The transformation of spatial data into a multi-relational representation and experimental results are reported and commented
"Now he walks and walks, as if he didn't have a home where he could eat": food, healing, and hunger in Quechua narratives of madness
In the Quechua-speaking peasant communities of southern Peru, mental disorder is understood less as individualized pathology and more as a disturbance in family and social relationships. For many Andeans, food and feeding are ontologically fundamental to such relationships. This paper uses data from interviews and participant observation in a rural province of Cuzco to explore the significance of food and hunger in local discussions of madness. Carersâ narratives, explanatory models, and theories of healing all draw heavily from idioms of food sharing and consumption in making sense of affliction, and these concepts structure understandings of madness that differ significantly from those assumed by formal mental health services. Greater awareness of the salience of these themes could strengthen the input of psychiatric and psychological care with this population and enhance knowledge of the alternative treatments that they use. Moreover, this case provides lessons for the global mental health movement on the importance of openness to the ways in which indigenous cultures may construct health, madness, and sociality. Such local meanings should be considered by mental health workers delivering services in order to provide care that can adjust to the alternative ontologies of sufferers and carers
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