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This experiment will utilize electroencephalography (EEG) and electromyography (EMG) to compare and contrast infrequent events with different action requirements: those requiring stopping a prepotent response (as in the stop signal in the stop-signal task) and those requiring responding despite the occurrence of visually analogous infrequent event (a continue signal). This experiment seeks to complement a related, pre-registered experiment involving the same basic task with TMS in which participants made foot responses. At n=20 (of targeted sample size of 27), motor-evoked potential (MEP) acquired from the hand was commonly suppressed after both continue and stop signals demonstrating global motor suppression. Moreover, successful stops showed pronounced motor suppression which was most apparent at later TMS stimulation times (whereas failed stop and continue motor suppression become less pronounced at 200 ms stimulation time). Importantly, continue and stop signals were equally infrequent (p = .167) and occurred with the same stop signal delay (continue signal latency was yoked to an adaptive stop signal delay) thus differing only with respect to their motor demands. This finding also suggests motor inhibition can be implemented by two phases: (1) an early saliency-driven initial pause phase (MEP reduction in all continue and stop trials) and (2) a later cancellation phase (robustly maintained MEP reduction in successful stops compared to failed stops and continue trials). In this experiment, we will simultaneously record extracranial EEG as well as EMG from both hands while participants perform the same task with hand responses. By using this experimental design, we aim to compare and contrast continue and stop conditions using other techniques that offer precise temporal resolution throughout the entirety of each trial (i.e., not just three time points). First, using multivariate pattern analysis with EEG data, we predict that the classifier will initially not be able to decode successful stops from successful continues, but will be able to at later time courses. Second, using EMG (this time recorded from task-related effectors), we aim to replicate others who looked at subthreshold activity and showed that successful stopping was characterized by an initial increase in activity followed by a rapid decrease (e.g. Jana et al., 2020; Raud & Huster, 2017; Raud et al., 2020). Crucially, however, we will extend this work by further considering whether this EMG signature of action cancellation is also present after continue signals followed by an additional rebound in EMG activity consistent with a response. Also, as failed stops also involve the infrequent stop signal but with responses--and typically early ones--we predict that a short duration decrease in ongoing response-related EMG activity may also be evident.

The kinetic Monte Carlo (KMC) method is a popular modeling approach for reaching large materials length and time scales. The KMC dynamics is erroneous when atomic processes that are relevant to the dynamics are missing from the KMC model. Recently, we had developed for the first time an error measure for KMC in Bhute and Chatterjee [J. Chem. Phys. 138, 084103 (2013)]. The error measure, which is given in terms of the probability that a missing process will be selected in the correct dynamics, requires estimation of the missing rate. In this work, we present an improved procedure for estimating the missing rate. The estimate found using the new procedure is within an order of magnitude of the correct missing rate, unlike our previous approach where the estimate was larger by orders of magnitude. This enables one to find the error in the KMC model more accurately. In addition, we find the time for which the KMC model can be used before a maximum error in the dynamics has been reached. (C) 2013 AIP Publishing LLC.

A new class of rare event acceleration techniques based on steered molecular dynamics (SMD) simulations is introduced. A stretching force applied on a biomolecule causes it to access large end-to-end distances. Under these conditions the biomolecule undergoes rapid conformational changes that are rare at zero-force conditions. A theory describing kinetics of a biomolecule at various stretching forces is presented. Using the theory, a master-Markov state model (master-MSM) is constructed from rates frequently accessed over a small range of force conditions. The master-MSM is shown to be applicable over a wide range of force conditions. We demonstrate application of the theory to three different biomolecular systems, namely, deca-alanine, TBA (thrombin binding aptamer), and a RNA hairpin. The master-MSM is used to estimate the kinetics at zero-force conditions, i.e., on the unbiased free-energy landscape, resulting inasmuch as 2-6 orders-of-magnitude speed-up over standard molecular dynamics.

Kinetic rates at different temperatures and the associated Arrhenius parameters, whenever Arrhenius law is obeyed, are efficiently estimated by applying maximum likelihood analysis to waiting times collected using the temperature programmed molecular dynamics method. When transitions involving many activated pathways are available in the dataset, their rates may be calculated using the same collection of waiting times. Arrhenius behaviour is ascertained by comparing rates at the sampled temperatures with ones from the Arrhenius expression. Three prototype systems with corrugated energy landscapes, namely, solvated alanine dipeptide, diffusion at the metal-solvent interphase, and lithium diffusion in silicon, are studied to highlight various aspects of the method. The method becomes particularly appealing when the Arrhenius parameters can be used to find rates at low temperatures where transitions are rare. Systematic coarse-graining of states can further extend the time scales accessible to the method. Good estimates for the rate parameters are obtained with 500-1000 waiting times. Published by AIP Publishing.

The development of materials and devices at the nanoscale presents great challenges, from synthesis to assembly to characterization. Often, progress can only be made by complementing experimental work with electronic-structure modeling, harnessing the efficiency, predictive power, and atomic resolution of density functional theory to describe molecular architectures exactly at those scales (hundreds or thousands of atoms) where the most promising and undiscovered properties are to be engineered. Some of the next-generation technologies that will benefit first from first-principles simulations encompass areas as diverse as energy and information storage and retrieval, detection and sensing of biological and foreign contaminants, nanostructured catalysts, nanomechanical devices, hybrid organic-inorganic and biologically inspired materials, and novel computer technologies based on integrated optical and electronic platforms. This article reviews some of the recent successes and insights gained by electronic-structure modeling, ranging from carbon nanotubes to semiconducting nanoparticles, quantum dots, and self-assembled monolayers.

Several studies in the past have generated Markov State Models (MSMs), i.e., kinetic models, of biomolecular systems by post-analyzing long standard molecular dynamics (MD) calculations at the temperature of interest and focusing on the maximally ergodic subset of states. Questions related to goodness of these models, namely, importance of the missing states and kinetic pathways, and the time for which the kinetic model is valid, are generally left unanswered. We show that similar questions arise when we generate a room-temperature MSM (denoted MSM-A) for solvated alanine dipeptide using state-constrained MD calculations at higher temperatures and Arrhenius relation - the main advantage of such a procedure being a speed-up of several thousand times over standard MD-based MSM building procedures. Bounds for rate constants calculated using probability theory from state-constrained MD at room temperature help validate MSM-A. However, bounds for pathways possibly missing in MSM-A show that alternate kinetic models exist that produce the same dynamical behaviour at short time scales as MSM-A but diverge later. Even in the worst case scenario, MSM-A is found to be valid longer than the time required to generate it. Concepts introduced here can be straightforwardly extended to other MSM building techniques. (C) 2015 AIP Publishing LLC.

We present a hierarchical coarse-grained simulation technique called the temperature programmed molecular dynamics (TPMD) method for accelerating molecular dynamics (MD) simulations of rare events. The method is targeted towards materials where a system visits many times a collection of energy basins in the potential energy surface, called a superbasin, via low-barrier moves before escaping to a new superbasin via a high-barrier move. The superbasin escape events are rare at the MD time scales. The low-barrier moves become accessible to MD by employing a temperature program, i.e., the MD temperature changes during the simulation. Once a superbasin is detected, transitions within the superbasin are ignored, in effect causing coarse-graining of basins. The temperature program enables the system to escape from the superbasin with reduced computational cost thereby overcoming the "low-barrier" problem. The main advantage of our approach is that the superbasin-to-superbasin transitions are accurately obtained at the original temperature with a reasonable computational cost. We study surface diffusion in Ag/Ag(001) system and demonstrate the ability of the TPMD method to span a wide-range of timescales. (C) 2014 AIP Publishing LLC.

Markov state models (MSMs) are employed extensively in literature with the kinetic Monte Carlo (KMC) method for studying state-to-state dynamics in a wide range of material systems. A MSM contains a list of atomic processes and their rate constants for different states of the system. In many situations, only few of the possible atomic processes are included in the MSM. The use of an incomplete MSM with the KMC method can lead to an error in the dynamics. In this work, we develop an error measure to assess the accuracy of a MSM generated using dynamical basin escape pathway searches. We show that the error associated with an incomplete MSM depends on the rate constants missing from the MSM. A procedure to estimate the missing rate constants is developed. We demonstrate our approach using some examples., A.C. acknowledges support from BRNS Young Scientist Award from Department of Atomic Energy (DAE-BRNS) No. 2011/36/43-BRNS/1975.

Rate constants of surface diffusion processes in metals often depend on the local atomic environment. Accurate models that describe this local environment dependence for specific material systems are not easily available in the literature despite the need for such information in kinetic models. In this work, we find the prefactor and activation barrier associated with adatom hop processes for a large number of local environments and use this information to systematically train cluster expansion (CE) models for Ag, Al, Cu, Ni, Pd and Pt. We demonstrate that although the local environments used for training is a small subset of all possible local environments, the CE model can predict the rate constants for many local environments that have not been used while training the model with reasonable accuracy. It is found that up to fourth nearest neighbor positions can be relevant to the activation barrier. Furthermore, many-body effects play an important role. We also find that the CE model depends on the direction of the hop. (C) 2013 Elsevier B.V. All rights reserved.

An extension of superposition state molecular dynamics (SSMD) [Venkatnathan and Voth J. Chem. Theory Comput. 2005, 1, 36] is presented with the goal to accelerate timescales and enable the study of "long-time" phenomena for condensed phase systems. It does not require any a priori knowledge about final and transition state configurations, or specific topologies. The system is induced to explore new configurations by virtue of a fictitious (free-particle-like) accelerating potential. The acceleration method can be applied to all degrees of freedom in the system and can be applied to condensed phases and fluids.

We present atomistic simulations of crystal growth where realistic experimental deposition rates are reproduced, without needing any a priori information on the relevant diffusion processes. Using the temperature accelerated dynamics method, we simulate the deposition of 4 monolayers (ML) of Ag/Ag(100) at the rate of 0.075 ML/s, thus obtaining a boost of several orders of magnitude with respect to ordinary molecular dynamics. In the temperature range analyzed (0--70 K), steering and activated mechanisms compete in determining the surface roughness.

We discuss how recent accelerated molecular dynamics techniques can be applied to crystal growth studies. Focusing our attention on the temperature accelerated dynamics (TAD) method, we show that at low temperatures the accessible time scale can be several orders of magnitude greater than with ordinary molecular dynamics. This allows us to match experimental deposition rates, as shown by preliminary results for the Cu/Cu(100) system.