Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

Fast parallel algorithms for short-range molecular dynamics

An object is to provide a semiconductor device of which a manufacturing process is not complicated and by which cost can be suppressed, by forming a thin film transistor using an oxide semiconductor film typified by zinc oxide, and a manufacturing method thereof. For the semiconductor device, a gate electrode is formed over a substrate; a gate insulating film is formed covering the gate electrode; an oxide semiconductor film is formed over the gate insulating film; and a first conductive film and a second conductive film are formed over the oxide semiconductor film. The oxide semiconductor film has at least a crystallized region in a channel region.

Semiconductor device, and manufacturing method thereof

The “protein folding problem” consists of three closely related puzzles: (a) What is the folding code? (b) What is the folding mechanism? (c) Can we predict the native structure of a protein from its amino acid sequence? Once regarded as a grand challenge, protein folding has seen great progress in recent years. Now, foldable proteins and nonbiological polymers are being designed routinely and moving toward successful applications. The structures of small proteins are now often well predicted by computer methods. And, there is now a testable explanation for how a protein can fold so quickly: A protein solves its large global optimization problem as a series of smaller local optimization problems, growing and assembling the native structure from peptide fragments, local structures first.

/pdf/the-protein-folding-problem-je8tdyrxrg.pdf

The Protein Folding Problem

Markov state models of molecular kinetics (MSMs), in which the long-time statistical dynamics of a molecule is approximated by a Markov chain on a discrete partition of configuration space, have seen widespread use in recent years. This approach has many appealing characteristics compared to straightforward molecular dynamics simulation and analysis, including the potential to mitigate the sampling problem by extracting long-time kinetic information from short trajectories and the ability to straightforwardly calculate expectation values and statistical uncertainties of various stationary and dynamical molecular observables. In this paper, we summarize the current state of the art in generation and validation of MSMs and give some important new results. We describe an upper bound for the approximation error made by modeling molecular dynamics with a MSM and we show that this error can be made arbitrarily small with surprisingly little effort. In contrast to previous practice, it becomes clear that the best MSM is not obtained by the most metastable discretization, but the MSM can be much improved if non-metastable states are introduced near the transition states. Moreover, we show that it is not necessary to resolve all slow processes by the state space partitioning, but individual dynamical processes of interest can be resolved separately. We also present an efficient estimator for reversible transition matrices and a robust test to validate that a MSM reproduces the kinetics of the molecular dynamics data.

/pdf/markov-models-of-molecular-kinetics-generation-and-rcn40bw5dw.pdf

Markov models of molecular kinetics: Generation and validation

A goal in the kinetic characterization of a macromolecular system is the description of its slow relaxation processes via (i) identification of the structural changes involved in these processes and (ii) estimation of the rates or timescales at which these slow processes occur. Most of the approaches to this task, including Markov models, master-equation models, and kinetic network models, start by discretizing the high-dimensional state space and then characterize relaxation processes in terms of the eigenvectors and eigenvalues of a discrete transition matrix. The practical success of such an approach depends very much on the ability to finely discretize the slow order parameters. How can this task be achieved in a high-dimensional configuration space without relying on subjective guesses of the slow order parameters? In this paper, we use the variational principle of conformation dynamics to derive an optimal way of identifying the "slow subspace" of a large set of prior order parameters - either generic internal coordinates or a user-defined set of parameters. Using a variational formulation of conformational dynamics, it is shown that an existing method-the time-lagged independent component analysis-provides the optional solution to this problem. In addition, optimal indicators-order parameters indicating the progress of the slow transitions and thus may serve as reaction coordinates-are readily identified. We demonstrate that the slow subspace is well suited to construct accurate kinetic models of two sets of molecular dynamics simulations, the 6-residue fluorescent peptide MR121-GSGSW and the 30-residue intrinsically disordered peptide kinase inducible domain (KID). The identified optimal indicators reveal the structural changes associated with the slow processes of the molecular system under analysis.

/pdf/identification-of-slow-molecular-order-parameters-for-markov-4doec0dqla.pdf

Identification of slow molecular order parameters for Markov model construction

Atomic layer etching (ALE) is a technique for removing thin layers of material using sequential reaction steps that are self-limiting. ALE has been studied in the laboratory for more than 25 years. Today, it is being driven by the semiconductor industry as an alternative to continuous etching and is viewed as an essential counterpart to atomic layer deposition. As we enter the era of atomic-scale dimensions, there is need to unify the ALE field through increased effectiveness of collaboration between academia and industry, and to help enable the transition from lab to fab. With this in mind, this article provides defining criteria for ALE, along with clarification of some of the terminology and assumptions of this field. To increase understanding of the process, the mechanistic understanding is described for the silicon ALE case study, including the advantages of plasma-assisted processing. A historical overview spanning more than 25 years is provided for silicon, as well as ALE studies on oxides, III–V compounds, and other materials. Together, these processes encompass a variety of implementations, all following the same ALE principles. While the focus is on directional etching, isotropic ALE is also included. As part of this review, the authors also address the role of power pulsing as a predecessor to ALE and examine the outlook of ALE in the manufacturing of advanced semiconductor devices.

https://avs.scitation.org/doi/pdf/10.1116/1.4913379

Overview of atomic layer etching in the semiconductor industry

Hydrogenated amorphous and nanocrystalline silicon films manufactured by plasma deposition techniques are used widely in electronic and optoelectronic devices1,2. The crystalline fraction and grain size of these films determines electronic and optical properties; the nanocrystal nucleation mechanism, which dictates the final film structure, is governed by the interactions between the hydrogen atoms of the plasma and the solid silicon matrix. Fundamental understanding of these interactions is important for optimizing the film structure and properties. Here we report the mechanism of hydrogen-induced crystallization of hydrogenated amorphous silicon films during post-deposition treatment with an H2 (or D2) plasma. Using molecular-dynamics simulations3,4 and infrared spectroscopy5, we show that crystallization is mediated by the insertion of H atoms into strained Si–Si bonds as the atoms diffuse through the film. This chemically driven mechanism may be operative in other covalently bonded materials, where the presence of hydrogen leads to disorder-to-order transitions.

Mechanism of hydrogen-induced crystallization of amorphous silicon

We use Bayesian inference to derive the rate coefficients of a coarse master equation from molecular dynamics simulations. Results from multiple short simulation trajectories are used to estimate propagators. A likelihood function constructed as a product of the propagators provides a posterior distribution of the free coefficients in the rate matrix determining the Markovian master equation. Extensions to non-Markovian dynamics are discussed, using the trajectory “paths” as observations. The Markovian approach is illustrated for the filling and emptying transitions of short carbon nanotubes dissolved in water. We show that accurate thermodynamic and kinetic properties, such as free energy surfaces and kinetic rate coefficients, can be computed from coarse master equations obtained through Bayesian inference.

Coarse Master Equation from Bayesian Analysis of Replica Molecular Dynamics Simulations

Methods of performing controllable lateral etches into the silicon layer using a plasma-enhanced etch-deposit-etch sequence are disclosed The first etch step etches into the silicon layer The deposition step passivates horizontal surfaces, including the bottom of the etched feature The second etch step increases the lateral undercut, resulting in a low V:L ratio silicon layer etch

Multi-step method for etching strain gate recesses

Using a coarse-grained molecular dynamics (CMD) approach we study the apparent nonlinear dynamics of water molecules filling or emptying carbon nanotubes as a function of system parameters. Different levels of the pore hydrophobicity give rise to tubes that are empty, water-filled, or fluctuate between these two long-lived metastable states. The corresponding coarse-grained free-energy surfaces and their hysteretic parameter dependence are explored by linking MD to continuum fixed point and bifurcation algorithms. The results are validated through equilibrium MD simulations.

Saravanapriyan Sriraman

Papers

Overview of atomic layer etching in the semiconductor industry

Mechanism of hydrogen-induced crystallization of amorphous silicon

Coarse Master Equation from Bayesian Analysis of Replica Molecular Dynamics Simulations

Multi-step method for etching strain gate recesses

Coarse nonlinear dynamics and metastability of filling-emptying transitions: water in carbon nanotubes.