13 research outputs found
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Impurity effects on solid-solid transitions in atomic clusters
We use the harmonic superposition approach to examine how a single atom substitution affects low-temperature anomalies in the vibrational heat capacity (C) of model nanoclusters. Each anomaly is linked to competing solidlike "phases", where crossover of the corresponding free energies defines a solid-solid transition temperature (T). For selected Lennard-Jones clusters we show that T and the corresponding CV peak can be tuned over a wide range by varying the relative atomic size and binding strength of the impurity, but excessive atom-size mismatch can destroy a transition and may produce another. In some tunable cases we find up to two additional C peaks emerging below Ts, signalling one- or two-step delocalisation of the impurity within the ground-state geometry. Results for NiX and AuX clusters (X = Au, Ag, Al, Cu, Ni, Pd, Pt, Pb), modelled by the many-body Gupta potential, further corroborate the possibility of tuning, engineering, and suppressing finite-system analogues of a solid-solid transition in nanoalloys.This work was funded by the ERC and EPSRC grant EP/J010847/1. BEH also acknowledges the Gates Cambridge Trust for financial support
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Energy Landscapes for Proteins: From Single Funnels to Multifunctional Systems
This report advances the hypothesis that multifunctional systems may be associated with multifunnel potential and free energy landscapes, with particular focus on biomolecules. It compares systems that exhibit single, double, and multiple competing structures, and contrasts multifunnel landscapes associated with misfolded amyloidogenic oligomers, which presumably do not arise as an evolutionary target. In this context, intrinsically disordered proteins could be considered intrinsically multifunctional molecules, associated with multifunnel landscapes. Potential energy landscape theory enables biomolecules to be treated in a common framework together with self‐organizing and multifunctional systems based on inorganic materials, atomic and molecular clusters, crystal polymorphs, and soft matter.epsr
Machine Learning for Molecular Dynamics on Long Timescales
Molecular dynamics (MD) simulation is widely used to analyze the properties of molecules and materials. Most practical applications, such as comparison with experimental measurements, designing drug molecules, or optimizing materials, rely on statistical quantities, which may be prohibitively expensive to compute from direct long-time MD simulations. Classical machine learning (ML) techniques have already had a profound impact on the field, especially for learning low-dimensional models of the long-time dynamics and for devising more efficient sampling schemes for computing long-time statistics. Novel ML methods have the potential to revolutionize long timescale MD and to obtain interpretable models. ML concepts such as statistical estimator theory, end-to-end learning, representation learning, and active learning are highly interesting for the MD researcher and will help to develop new solutions to hard MD problems. With the aim of better connecting the MD and ML research areas and spawning new research on this interface, we define the learning problems in long timescale MD, present successful approaches, and outline some of the unsolved ML problems in this application field
Learning molecular dynamics with simple language model built upon long short-term memory neural network
KRAS(G12C)–AMG 510 interaction dynamics revealed by all-atom molecular dynamics simulations
Author Correction: VAMPnets for deep learning of molecular kinetics
In the original version of this Article, financial support was not fully acknowledged. The PDF and HTML versions of the Article have now been corrected to include funding from the Deutsche Forschungsgemeinschaft Grant SFB958/A04