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

Metadynamics is an atomistic simulation technique that allows, within the same framework, acceleration of rare events and estimation of the free energy of complex molecular systems. It is based on iteratively ‘filling’ the potential energy of the system by a sum of Gaussians centred along the trajectory followed by a suitably chosen set of collective variables (CVs), thereby forcing the system to migrate from one minimum to the next. The power of metadynamics is demonstrated by the large number of extensions and variants that have been developed. The first scope of this Technical Review is to present a critical comparison of these variants, discussing their advantages and disadvantages. The effectiveness of metadynamics, and that of the numerous alternative methods, is strongly influenced by the choice of the CVs. If an important variable is neglected, the resulting estimate of the free energy is unreliable, and predicted transition mechanisms may be qualitatively wrong. The second scope of this Technical Review is to discuss how the CVs should be selected, how to verify whether the chosen CVs are sufficient or redundant, and how to iteratively improve the CVs using machine learning approaches. Metadynamics is a technique to enhance the probability of observing rare events, such as chemical reactions and phase transitions, in molecular dynamics simulations. This Technical Review surveys the technique, addressing the critical issues that are met in practical applications.

Using metadynamics to explore complex free-energy landscapes

The animal reservoir of SARS-CoV-2 is unknown despite reports of SARS-CoV-2-related viruses in Asian Rhinolophus bats1–4, including the closest virus from R. affinis, RaTG13 (refs. 5,6), and pangolins7–9. SARS-CoV-2 has a mosaic genome, to which different progenitors contribute. The spike sequence determines the binding affinity and accessibility of its receptor-binding domain to the cellular angiotensin-converting enzyme 2 (ACE2) receptor and is responsible for host range10–12. SARS-CoV-2 progenitor bat viruses genetically close to SARS-CoV-2 and able to enter human cells through a human ACE2 (hACE2) pathway have not yet been identified, although they would be key in understanding the origin of the epidemic. Here we show that such viruses circulate in cave bats living in the limestone karstic terrain in northern Laos, in the Indochinese peninsula. We found that the receptor-binding domains of these viruses differ from that of SARS-CoV-2 by only one or two residues at the interface with ACE2, bind more efficiently to the hACE2 protein than that of the SARS-CoV-2 strain isolated in Wuhan from early human cases, and mediate hACE2-dependent entry and replication in human cells, which is inhibited by antibodies that neutralize SARS-CoV-2. None of these bat viruses contains a furin cleavage site in the spike protein. Our findings therefore indicate that bat-borne SARS-CoV-2-like viruses that are potentially infectious for humans circulate in Rhinolophus spp. in the Indochinese peninsula. A study reports the detection and characterization of SARS-CoV-2-like viruses in Laotian cave-dwelling bats that are also demonstrated to infect human cells through the ACE2 pathway. 

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Bat coronaviruses related to SARS-CoV-2 and infectious for human cells

Rather than numerically solving the computationally demanding equations of quantum or statistical mechanics, machine learning methods can infer approximate solutions, interpolating previously acquired property data sets of molecules and materials. The case is made for quantum machine learning: An inductive molecular modeling approach which can be applied to quantum chemistry problems.

Quantum Machine Learning in Chemical Compound Space.

Machine learning models are poised to make a transformative impact on chemical sciences by dramatically accelerating computational algorithms and amplifying insights available from computational chemistry methods. However, achieving this requires a confluence and coaction of expertise in computer science and physical sciences. This Review is written for new and experienced researchers working at the intersection of both fields. We first provide concise tutorials of computational chemistry and machine learning methods, showing how insights involving both can be achieved. We follow with a critical review of noteworthy applications that demonstrate how computational chemistry and machine learning can be used together to provide insightful (and useful) predictions in molecular and materials modeling, retrosyntheses, catalysis, and drug design.

Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems.

The PLUMED consortium unifies developers and contributors to PLUMED, an open-source library for enhanced- sampling, free-energy calculations and the analysis of molecular dynamics simulations. Here, we outline
our efforts to promote transparency and reproducibility by disseminating protocols for enhanced-sampling molecular simulations.

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B. Giovanni

Papers

Promoting transparency and reproducibility in enhanced molecular simulations