Introducing VECMAtk - Verification, Validation and Uncertainty Quantification for Multiscale and HPC Simulations

Groen, Derek; Richardson, Robin A.; Wright, David W.; Jancauskas, Vytautas; Sinclair, Robert C.; Karlshoefer, Paul; Vassaux, Maxime; Piontek, Tomasz; Kopta, Piotr; Bosak, Bartosz; Lakhlili, Jalal; Hoenen, O.; Suleimenova, Diana; Edeling, Wouter; Crommelin, Daan; Nikishova, Anna; Coveney, Peter V.

doi:10.1007/978-3-030-22747-0_36

Cited by 17 publications

(19 citation statements)

References 35 publications

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“…This may reflect, to some extent, the lack of uptake in available tools, and perhaps also that current tools do not yet cover significant portions of use cases. In particular, we find that VVUQ on complex, multiscale workflows with diverse HPC requirements has not been fully addressed by existing tools [114]. This is due to the need to handle complex execution patterns across multiple HPC resources, rigorous handling of job failure, and efficient communication of high dimensional distributions, to name a few factors.…”

Section: A Lack Of Reproducibilitymentioning

confidence: 99%

Toward High Fidelity Materials Property Prediction from Multiscale Modeling and Simulation

Vassaux

Sinclair

Richardson

et al. 2019

Advcd Theory and Sims

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The current approach to materials discovery and design remains dominated by experimental testing, frequently based on little more than trial and error. With the advent of ever more powerful computers, rapid, reliable and reproducible computer simulations are beginning to represent a feasible alternative. As high performance computing reaches the exascale, exploiting the resources efficiently presents interesting challenges and opportunities. Multiscale modelling and simulation of materials is an extremely promising candidate for exploiting these resources based on the assumption of a separation of scales in the architectures of nanomaterials. We present examples of hierarchical and concurrent multiscale approaches which benefit from the weak scaling of monolithic applications, thereby efficiently exploiting large scale computational resources. We discuss several multiscale techniques, incorporating the electronic to the continuum scale, which can be applied to the efficient design of a range of nanocomposites. We then discuss our work on the development of a software toolkit designed to provide verification, validation and uncertainty quantification to support actionable prediction from such calculations.

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Section: A Lack Of Reproducibilitymentioning

confidence: 99%

Toward High Fidelity Materials Property Prediction from Multiscale Modeling and Simulation

Vassaux

Sinclair

Richardson

et al. 2019

Advcd Theory and Sims

Self Cite

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“…Instead, we can obtain statistically robust results by performing ensemble-based simulations, that is, a collection of n replicas each differing from the other solely in terms of the initial velocities assigned to all the atoms, drawn from a Maxwell-Boltzmann distribution at the temperature of interest [62]. Furthermore, ensemble-based simulations provide a reliable means of quantifying uncertainty in general [63].…”

Section: Ensemble-based Classical Molecular Dynamicsmentioning

confidence: 99%

“…To establish an appropriate number of replicas n constituting the QM/MM ensemble, the bootstrap statistical method was employed [ 63 ]. By applying the QM/MM methodology as described in § 4.3 , the reaction energy (Δ E rxn ) for the G:C → G*:C* tautomerism was calculated per replica.…”

Section: Multiscale Modelling Of Dnamentioning

confidence: 99%

The influence of base pair tautomerism on single point mutations in aqueous DNA

2020

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The relationship between base pair hydrogen bond proton transfer and the rate of spontaneous single point mutations at ambient temperatures and pressures in aqueous DNA is investigated. By using an ensemble-based multiscale computational modelling method, statistically robust rates of proton transfer for the A:T and G:C base pairs within a solvated DNA dodecamer are calculated. Several different proton transfer pathways are observed within the same base pair. It is shown that, in G:C, the double proton transfer tautomer is preferred, while the single proton transfer process is favoured in A:T. The reported range of rate coefficients for double proton transfer is consistent with recent experimental data. Notwithstanding the approximately 1000 times more common presence of single proton transfer products from A:T, observationally there is bias towards G:C to A:T mutations in a wide range of living organisms. We infer that the double proton transfer reactions between G:C base pairs have a negligible contribution towards this bias for the following reasons: (i) the maximum half-life of the G*:C* tautomer is in the range of picoseconds, which is significantly smaller than the milliseconds it takes for DNA to unwind during replication, (ii) statistically, the majority of G*:C* tautomers revert back to their canonical forms through a barrierless process, and (iii) the thermodynamic instability of the tautomers with respect to the canonical base pairs. Through similar reasoning, we also deduce that proton transfer in the A:T base pair does not contribute to single point mutations in DNA.

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“…UQ is an established domain in applied mathematics and engineering but has been notably absent inter alia from the analysis of computer simulations performed using electronic structure and molecular simulation methods. At this time, we are witnessing unified developments in quantifying uncertainty in computer simulation across a wide range of domains including weather, climate, material, fusion, molecular and biomedical sciences [7][8][9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Rapid, accurate, precise and reproducible ligand–protein binding free energy prediction

et al. 2020

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A central quantity of interest in molecular biology and medicine is the free energy of binding of a molecule to a target biomacromolecule. Until recently, the accurate prediction of binding affinity had been widely regarded as out of reach of theoretical methods owing to the lack of reproducibility of the available methods, not to mention their complexity, computational cost and time-consuming procedures. The lack of reproducibility stems primarily from the chaotic nature of classical molecular dynamics (MD) and the associated extreme sensitivity of trajectories to their initial conditions. Here, we review computational approaches for both relative and absolute binding free energy calculations, and illustrate their application to a diverse set of ligands bound to a range of proteins with immediate relevance in a number of medical domains. We focus on ensemble-based methods which are essential in order to compute statistically robust results, including two we have recently developed, namely thermodynamic integration with enhanced sampling and enhanced sampling of MD with an approximation of continuum solvent. Together, these form a set of rapid, accurate, precise and reproducible free energy methods. They can be used in real-world problems such as hit-to-lead and lead optimization stages in drug discovery, and in personalized medicine. These applications show that individual binding affinities equipped with uncertainty quantification may be computed in a few hours on a massive scale given access to suitable high-end computing resources and workflow automation. A high level of accuracy can be achieved using these approaches.

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Introducing VECMAtk - Verification, Validation and Uncertainty Quantification for Multiscale and HPC Simulations

Cited by 17 publications

References 35 publications

Toward High Fidelity Materials Property Prediction from Multiscale Modeling and Simulation

Toward High Fidelity Materials Property Prediction from Multiscale Modeling and Simulation

The influence of base pair tautomerism on single point mutations in aqueous DNA

Rapid, accurate, precise and reproducible ligand–protein binding free energy prediction

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