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1. Delta Variant with P681R Critical Mutation Revealed by Ultra-Large Atomic-Scale Ab Initio Simulation: Implications for the Fundamentals of Biomolecular Interactions

   https://doi.org/10.3390/v14030465  

   Adhikari, Puja ; Jawad, Bahaa ; Rao, Praveen ; Podgornik, Rudolf ; Ching, Wai-Yim ( March 2022 , Viruses)

   The SARS-CoV-2 Delta variant is emerging as a globally dominant strain. Its rapid spread and high infection rate are attributed to a mutation in the spike protein of SARS-CoV-2 allowing for the virus to invade human cells much faster and with an increased efficiency. In particular, an especially dangerous mutation P681R close to the furin cleavage site has been identified as responsible for increasing the infection rate. Together with the earlier reported mutation D614G in the same domain, it offers an excellent instance to investigate the nature of mutations and how they affect the interatomic interactions in the spike protein. Here, using ultra large-scale ab initio computational modeling, we study the P681R and D614G mutations in the SD2-FP domain, including the effect of double mutation, and compare the results with the wild type. We have recently developed a method of calculating the amino-acid–amino-acid bond pairs (AABP) to quantitatively characterize the details of the interatomic interactions, enabling us to explain the nature of mutation at the atomic resolution. Our most significant finding is that the mutations reduce the AABP value, implying a reduced bonding cohesion between interacting residues and increasing the flexibility of these amino acids to cause the damage. The possibility of using this unique mutation quantifiers in a machine learning protocol could lead to the prediction of emerging mutations. 

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2. E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials

   https://doi.org/10.1038/s41467-022-29939-5  

   Batzner, Simon ; Musaelian, Albert ; Sun, Lixin ; Geiger, Mario ; Mailoa, Jonathan P. ; Kornbluth, Mordechai ; Molinari, Nicola ; Smidt, Tess E. ; Kozinsky, Boris ( May 2022 , Nature Communications)

   This work presents Neural Equivariant Interatomic Potentials (NequIP), an E(3)-equivariant neural network approach for learning interatomic potentials from ab-initio calculations for molecular dynamics simulations. While most contemporary symmetry-aware models use invariant convolutions and only act on scalars, NequIP employs E(3)-equivariant convolutions for interactions of geometric tensors, resulting in a more information-rich and faithful representation of atomic environments. The method achieves state-of-the-art accuracy on a challenging and diverse set of molecules and materials while exhibiting remarkable data efficiency. NequIP outperforms existing models with up to three orders of magnitude fewer training data, challenging the widely held belief that deep neural networks require massive training sets. The high data efficiency of the method allows for the construction of accurate potentials using high-order quantum chemical level of theory as reference and enables high-fidelity molecular dynamics simulations over long time scales.

    

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3. INDEEDopt: a deep learning-based ReaxFF parameterization framework

   https://doi.org/10.1038/s41524-021-00534-4  

   Sengul, Mert Y. ; Song, Yao ; Nayir, Nadire ; Gao, Yawei ; Hung, Ying ; Dasgupta, Tirthankar ; van Duin, Adri C. T. ( May 2021 , npj Computational Materials)

   Empirical interatomic potentials require optimization of force field parameters to tune interatomic interactions to mimic ones obtained by quantum chemistry-based methods. The optimization of the parameters is complex and requires the development of new techniques. Here, we propose an INitial-DEsign Enhanced Deep learning-based OPTimization (INDEEDopt) framework to accelerate and improve the quality of the ReaxFF parameterization. The procedure starts with a Latin Hypercube Design (LHD) algorithm that is used to explore the parameter landscape extensively. The LHD passes the information about explored regions to a deep learning model, which finds the minimum discrepancy regions and eliminates unfeasible regions, and constructs a more comprehensive understanding of physically meaningful parameter space. We demonstrate the procedure here for the parameterization of a nickel–chromium binary force field and a tungsten–sulfide–carbon–oxygen–hydrogen quinary force field. We show that INDEEDopt produces improved accuracies in shorter development time compared to the conventional optimization method.

    

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4. MAISE: Construction of neural network interatomic models and evolutionary structure optimization

   https://doi.org/10.17632/vfzgt2gnsh.1  

   Hajinazar, S. ; Thorn, A. ; Sandoval, E.D. ; Kharabadze, S. ; Kolmogorov, A.N. ( October 2020 , Computer physics communications)

   null (Ed.)

   Module for ab initio structure evolution (MAISE) is an open-source package for materials modeling and prediction. The code’s main feature is an automated generation of neural network (NN) interatomic potentials for use in global structure searches. The systematic construction of Behler–Parrinello-type NN models approximating ab initio energy and forces relies on two approaches introduced in our recent studies. An evolutionary sampling scheme for generating reference structures improves the NNs’ mapping of regions visited in unconstrained searches, while a stratified training approach enables the creation of standardized NN models for multiple elements. A more flexible NN architecture proposed here expands the applicability of the stratified scheme for an arbitrary number of elements. The full workflow in the NN development is managed with a customizable ‘MAISE-NET’ wrapper written in Python. The global structure optimization capability in MAISE is based on an evolutionary algorithm applicable for nanoparticles, films, and bulk crystals. A multitribe extension of the algorithm allows for an efficient simultaneous optimization of nanoparticles in a given size range. Implemented structure analysis functions include fingerprinting with radial distribution functions and finding space groups with the SPGLIB tool. This work overviews MAISE’s available features, constructed models, and confirmed predictions. 

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5. Deep neural network based quantum simulations and quasichemical theory for accurate modeling of molten salt thermodynamics

   https://doi.org/10.1039/d2sc02227c  

   Shi, Yu ; Lam, Stephen T. ; Beck, Thomas L. ( July 2022 , Chemical Science)

   With dual goals of efficient and accurate modeling of solvation thermodynamics in molten salt liquids, we employ ab initio molecular dynamics (AIMD) simulations, deep neural network interatomic potentials (NNIP), and quasichemical theory (QCT) to calculate the excess chemical potentials for the solute ions Na + and Cl − in the molten NaCl liquid. NNIP-based molecular dynamics simulations accelerate the calculations by 3 orders of magnitude and reduce the uncertainty to 1 kcal mol −1 . Using the Density Functional Theory (DFT) level of theory, the predicted excess chemical potential for the solute ion pair is −178.5 ± 1.1 kcal mol −1 . A quantum correction of 13.7 ± 1.9 kcal mol −1 is estimated via higher-level quantum chemistry calculations, leading to a final predicted ion pair excess chemical potential of −164.8 ± 2.2 kcal mol −1 . The result is in good agreement with a value of −163.5 kcal mol −1 obtained from thermo-chemical tables. This study validates the application of QCT and NNIP simulations to the molten salt liquids, allowing for significant insights into the solvation thermodynamics crucial for numerous molten salt applications. 

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