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Efficiently creating a concise but comprehensive data set for training machine-learned interatomic potentials (MLIPs) is an under-explored problem. Active learning, which uses biased or unbiased molecular dynamics (MD) to generate candidate pools, aims to address this objective. Existing biased and unbiased MD-simulation methods, however, are prone to miss either rare events or extrapolative regions—areas of the configurational space where unreliable predictions are made. This work demonstrates that MD, when biased by the MLIP's energy uncertainty, simultaneously captures extrapolative regions and rare events, which is crucial for developing uniformly accurate MLIPs. Furthermore, exploiting automatic differentiation, we enhance bias-forces-driven MD with the concept of bias stress. We employ calibrated gradient-based uncertainties to yield MLIPs with similar or, sometimes, better accuracy than ensemble-based methods at a lower computational cost. Finally, we apply uncertainty-biased MD to alanine dipeptide and MIL-53(Al), generating MLIPs that represent both configurational spaces more accurately than models trained with conventional MD. [ABSTRACT FROM AUTHOR]

Chemically complex multicomponent alloys possess exceptional properties derived from an inexhaustible compositional space. The complexity however makes interatomic potential development challenging. We explore two complementary machine-learned potentials—the moment tensor potential (MTP) and the Gaussian moment neural network (GM-NN)—in simultaneously describing configurational and vibrational degrees of freedom in the Ta-V-Cr-W alloy family. Both models are equally accurate with excellent performance evaluated against density-functional-theory. They achieve root-mean-square-errors (RMSEs) in energies of less than a few meV/atom across 0 K ordered and high-temperature disordered configurations included in the training. Even for compositions not in training, relative energy RMSEs at high temperatures are within a few meV/atom. High-temperature molecular dynamics forces have similarly small RMSEs of about 0.15 eV/Å for the disordered quaternary included in, and ternaries not part of training. MTPs achieve faster convergence with training size; GM-NNs are faster in execution. Active learning is partially beneficial and should be complemented with conventional human-based training set generation. [ABSTRACT FROM AUTHOR]

Transition paths characterize chemical reaction mechanisms. In this paper, we present a new method to find mean reaction paths based on the free energy. A nudged elastic band (NEB) is optimized using gradients and Hessians of the free energy, which are obtained from umbrella integration. The transition state can be refined by a Newton-Raphson search starting from the highest point of the NEB path. All optimizations are done using Cartesian coordinates. Independent molecular dynamics (MD) runs are performed at each image used to discretize the path. This makes the method intrinsically parallel. In contrast to other free energy methods, the algorithm does not become more expensive when including more degrees of freedom in the active space. The method is applied to the alanine-dipeptide as a test case and compared to pathways that have been derived from metadynamics and forward flux sampling. [ABSTRACT FROM AUTHOR]

Umbrella integration is a method to analyze umbrella sampling simulations. It calculates free-energy changes from distributions obtained from molecular dynamics. While it can be formulated on the full sampled distributions, they are generally approximated by normal distributions. This is equivalent to the truncation of a power series of the free energy with respect to the reaction coordinate after the quadratic term or by a truncation of a cumulant expansion. Here, expressions for additional terms in the power series are derived. They can be calculated from the central moments of the distributions. This extension allows to test the approximations in applications. [ABSTRACT FROM AUTHOR]

Umbrella sampling simulations, or biased molecular dynamics, can be used to calculate the free-energy change of a chemical reaction. We investigate the sources of different sampling errors and derive approximate expressions for the statistical errors when using harmonic restraints and umbrella integration analysis. This leads to generally applicable rules for the choice of the bias potential and the sampling parameters. Numerical results for simulations on an analytical model potential are presented for validation. While the derivations are based on umbrella integration analysis, the final error estimate is evaluated from the raw simulation data, and it may therefore be generally applicable as indicated by tests using the weighted histogram analysis method. [ABSTRACT FROM AUTHOR]

We present a method to analyze biased molecular-dynamics and Monte Carlo simulations, also known as umbrella sampling. In the limiting case of a strong bias, this method is equivalent to thermodynamic integration. It employs only quantities with easily controllable equilibration and greatly reduces the statistical errors compared to the standard weighted histogram analysis method. We show the success of our approach for two examples, one analytic function, and one biological system. [ABSTRACT FROM AUTHOR]

We have investigated the free energy surface of alanine tripeptide in water. To elucidate the secondary structure of the amide chain, information on the free energy surface with explicit water at room temperature, and the multidimensional reaction coordinates are required. We studied the minimum free energy paths (MFEPs) connecting reactants, transition structures (TS) and products. To solve this problem, we used the free energy reaction root mapping (FERRMap) method. This is an automated search method to find MFEPs by using umbrella integration and the scaled hypersphere search method. We calculated the four-dimensional free energy surface for alanine tripeptide in water using FERRMap and found 61 equilibrium structures (EQ) connected by 133 TS points. After elucidating the MFEP network, we analysed the structures of the EQ points and the MFEPs connecting beta-sheet structures and beta-turn structures or left-handed helix structures. [ABSTRACT FROM AUTHOR]

This special topics issue offers a broad perspective on the recent developments and reviews of state-of-the-art particle methods in science and engineering applications. This issue grew out of contributions delivered during the closing conference of the Collaborative Research Centre (CRC) 716 that for the last twelve years was contributing important works to the area of "dynamical simulations of systems with large particle numbers". Since particle methods are present in many fields of science and engineering, the papers collected here span a considerable range of subjects and questions, but they also illustrate numerous connections to both fundamental science and technological/industrial applications. In addition reviews display the current state of three software packages that have been developed in this CRC in the area of simulations and visualization. [ABSTRACT FROM AUTHOR]

Abstract: Epidermal growth factor receptor (EGFR; ErbB1, HER1 in humans) is a receptor tyrosine kinase triggering signals across the plasma membranes of cells to determine cell fate. We have used molecular dynamics simulations to investigate structural models of ErbB1 ectodomains. We show that, with minor rearrangements, the ErbB1 back-to-back dimer can align almost flat on the cell membrane. This is in contrast to the traditional picture of ErbB1 dimers standing proud of the membrane, but in line with recent FRET and EM experiments. Interaction with the membrane leads to conformational changes in the dimer, which further stabilize the back-to-back interface. On the membrane, two dimers can associate forming a tetramer. This is enabled by a head-to-head interface, involving the ligand binding side of the ectodomain, and which significantly enhances ligand binding. A weak head-to-head interface has been seen in crystal structures, but is found to stabilise appreciably in our simulation. We also find that the domains IV, connecting the receptor to the membrane, weakly interact with each other. These simulations illustrate some of the flexibility of the ErbB1 ectodomains, and may help to explain recent experimental results. [Copyright &y& Elsevier]

The ability of epidermal growth factor receptor (EGFR) to control cell fate is defined by its affinity for ligand. Current models suggest that ligand-binding heterogeneity arises from negative cooperativity in signaling receptor dimers, for which the asymmetry of the extracellular region of the Drosophila EGFR has recently provided a structural basis. However, no asymmetry is apparent in the isolated extracellular region of the human EGFR. Human EGFR also differs from the Drosophila EGFR in that negative cooperativity is found only in full-length receptors in cells. To gain structural insights into the human EGFR in situ, we developed an approach based on quantitative Förster resonance energy transfer (FRET) imaging, combined with Monte Carlo and molecular dynamics simulations, to probe receptor conformation in epithelial cells. We experimentally demonstrate a high-affinity ligand-binding human EGFR conformation consistent with the extracellular region aligned flat on the plasma membrane. We explored the relevance of this conformation to ligand-binding heterogeneity and found that the asymmetry of this structure shares key features with that of the Drosophila EGFR, suggesting that the structural basis for negative cooperativity is conserved from invertebrates to humans but that in human EGFR the extracellular region asymmetry requires interactions with the plasma membrane. [ABSTRACT FROM AUTHOR]