Bulletin of the American Physical Society
APS March Meeting 2019
Volume 64, Number 2
Monday–Friday, March 4–8, 2019; Boston, Massachusetts
Session P21: Emerging Trends in Molecular Dynamics Simulations and Data Analytics IIIFocus

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Sponsoring Units: DCOMP Chair: Roberto Car, Princeton University Room: BCEC 157B 
Wednesday, March 6, 2019 2:30PM  3:06PM 
P21.00001: Deep Learning for MultiScale Molecular Modelling Invited Speaker: Weinan E Deep learning has emerged as a promising tool for a variety of applications 
Wednesday, March 6, 2019 3:06PM  3:42PM 
P21.00002: Predictive Atomistic Simulations of Materials using SNAP DataDriven Potentials Invited Speaker: Aidan Thompson Molecular dynamics (MD) is a powerful materials simulation method whose accuracy is limited by the interatomic potential (IAP). In many materials science applications suitably accurate potentials simply do not exist. SNAP is an automated methodology for generating applicationspecific IAPs using large and diverse datasets of quantum electronic structure calculations. The SNAP IAP is formulated in terms of a set of general fourbody geometric invariants that characterize the local neighborhood of each atom. This approach has been used to develop potentials for diverse applications, including metal plasticity (Ta), defects in IIIV semiconductors (InP), and fusion energy materials (W/Be/He/H). In each case, the SNAP IAP is fit to density functional theory calculations of energy, force, and stress for many small configurations of atoms. Crossvalidation analysis and evaluation on test problems are used to further improve IAP fidelity and robustness. Varying the number of geometric descriptors allows a continuous tradeoff between computational cost and accuracy. The resultant potentials enable highfidelity MD simulations of these materials, providing insight into their behavior on lengthscales and timescales unreachable by other methods. The relatively large peratom computational cost of SNAP is offset by combining LAMMPS' spatial parallel algorithms with Kokkosbased hierarchical multithreading, enabling the efficient use of large CPU and GPU clusters, allocating only a few atoms to each node. Recent extensions of the SNAP approach include multielement geometric descriptors and the use of higherorder terms. 
Wednesday, March 6, 2019 3:42PM  3:54PM 
P21.00003: Machine learning of reaction pathways in chemical vapor deposition of MoS_{2} monolayers Aravind Krishnamoorthy, Pankaj Rajak, Aiichiro Nakano, Rajiv Kalia, Priya Vashishta Scalable synthesis of two dimensional (2D) materials is a major bottleneck to more widespread adoption of layered materialbased devices. Chemical vapor deposition (CVD) has emerged as a viable method for largescale synthesis of 2D materials. However, neither experiment nor theory has been able to decipher mechanisms and selection rules, or make predictions of optimized growth parameters. Experimental challenges stem from the use of probes like TEM to characterize CVD growth reactions in situ under elevated temperatures and pressures. Computational synthesis, which simulates CVD processes using reactive molecular dynamics methods provides the atomistic resolution necessary for the deduction of reaction mechanisms. Here we use neural networks trained on trajectories from several hundred simulations of computational synthesis of MoS_{2} monolayers to uncover the dependence of product stoichiometry, crystallinity and phase distribution on reaction parameters like temperature, sulfur and hydrogen partial pressures, thus paving the way for rational design of CVD synthesis techniques. 
Wednesday, March 6, 2019 3:54PM  4:06PM 
P21.00004: Development of artificial neural network potential for hexagonal boron nitride with and without defects Talat S. Rahman, Duy Le Recently, defects in hexagonal boron nitride (hBN) have been shown to play an important role in determining its novel chemical and optical properties, which may have a variety of possible technological applications. Characterization of the structure and dynamics of these defects would be most facilitated by the availability of accurate interatomic potentials that enable large length and time scale simulations. In this work, we will summarize our development of an artificial neural network (ANN) potential for hBN with and without defects. The ANN potential was trained by using about 70000 data points obtained from ab initio molecular dynamic simulations of point defect on a (6x6) hBN layer. The trained ANN potential is capable of producing system energetics in agreement with that obtained from density functional theory (within few meV per atom for both training and validation). The structure and dynamics of defects and grain boundaries in hBN using the ANN potential will be presented and results compared with available experimental data. 
Wednesday, March 6, 2019 4:06PM  4:18PM 
P21.00005: Activelearning strategy for the development of applicationspecific machinelearning force fields Huan Tran, Rohit Batra, James Chapman, Chiho Kim, Anand Chandrashekaran, Ramamurthy Ramprasad Emerging datadriven approaches in materials science have triggered the development of a plethora of machinelearning (ML) force fields (FFs). In practice, they are constructed by training a statistical model on a reference database to predict potential energy or atomic forces. While most of the FFs can accurately recover the reference data, some of them are becoming useful for actual molecular dynamics simulations. In this work, we develop a simple activelearning strategy for the development of ML FFs targeted at specific simulations (applications). The strategy involves (1) preparing and fingerprinting a diverse reference database of atomic configurations and forces, (2) generating a pool of ML FFs by learning the reference data, (3) validating the FFs against a series of targeted applications, and (4) selectively and recursively improving the FFs that remain unsuitable for a given application while keeping their performance on other applications uncompromised. We demonstrate this strategy by developing a series Al and Cu ML FFs that can simultaneously be used for various applications, including (elastic) stress/strain analysis, stackingfault energy calculations, and melting simulations. This strategy is generalizable, i.e., it may be used for other materials as well. 
Wednesday, March 6, 2019 4:18PM  4:30PM 
P21.00006: Deep Generative Model of Interfacial Structures in Phase Transformation of
an MoWSe_{2} Monolayer Pankaj Rajak, Aravind Krishnamoorthy, Aiichiro Nakano, Rajiv Kalia, Priya Vashishta Optical and electrical properties of twodimensional layered materials can be tuned by mechanical straining, which induces transformations from semiconducting to metallic phases. We use deep generative variational autoencoder (VAE) model, trained by molecular dynamics simulation data of dynamic fracture in an MoWSe_{2} monolayer, to predict transition pathways consisting of novel intermediate structures (a and b) between the semiconducting (2H) and metallic (1T) phases. In addition, a conditional variational autoencoder (CVAE) is used to generate intermediate structures such as a or b , and defects. Structures synthesized from VAE and CVAE are validated by quantum simulations based on density functional theory. Quantum simulations show that structures generated by VAE and CVAE are stable and can be used for nanoelectronics applications. 
Wednesday, March 6, 2019 4:30PM  4:42PM 
P21.00007: Magnetism and superconductivity in amorphous carobn Yuki Sakai, James Chelikowsky, Marvin L Cohen We study magnetic and superconducting properties of amorphous carbon based on molecular dynamics simulations. First we use spin constrained firstprinciples simulations to obtain amorphous carbon structures with a desired magnetization. We show that the existence of sp^{2}like threefold coordinated carbon atoms plays an important role in causing magnetism in amorphous carbon. We predict detailed geometries of threefold carbon atoms that induce the magnetic order in amorphous carbon. We also consider the effect of boron doping on superconducting properties of amorphous carbon. By considering amorphous structures with various sp^{2}:sp^{3} ratios, we find that sp^{3}hybridized atoms are necessary for high superconducting transition temperature in contrast to the magnetism. 
Wednesday, March 6, 2019 4:42PM  4:54PM 
P21.00008: Magnetostriction and LongRange Interactions in Coupled Spin and Lattice Dynamics Julien Tranchida, Mitchell A Wood, Attila Cangi, Stan G. Moore, Pascal Thibaudeau, Steven James Plimpton, Aidan Thompson A scalable and symplectic algorithm for coupled spin dynamics and molecular dynamics was 
Wednesday, March 6, 2019 4:54PM  5:06PM 
P21.00009: Employing autoencoders for configuration space sampling: Application to small molecules. Igor Poltavskyi, Alexandre Tkatchenko The behavior of molecular systems in different equilibrium physical processes or chemical reactions is governed by the free energy (FE). Calculations of FE require a thorough sampling of configuration space for given external conditions. Stateoftheart sampling techniques, based on molecular dynamics, are formally applicable to systems of arbitrary size. However, in practice, they suffer from the curse of dimensionality and the limitation of the time step by the fastest process present in the system. These make FE calculations for complex molecules, where the entropy effects are of utmost importance, extremely challenging and computationally demanding. Here we propose to use a machinelearning approach based on autoencoders to generate new sampling configurations. By transition to the feature space, we effectively decrease the dimensionality of the problem and resolve the time step limitation. Training autoencoders cost only a small fraction of statistically converged molecular dynamics simulations, paving the way to efficient calculations of thermodynamic properties for complex molecular systems. 
Wednesday, March 6, 2019 5:06PM  5:18PM 
P21.00010: Atomistic mechanisms of phase transitions from Machine Learning Rodrigo Freitas, Evan Reed Identifying and characterizing atomic mechanisms of phase changes is of fundamental importance in the study of the kinetics of the nucleation and growth process. This talk will describe a new approach in extracting information from atomistic simulations of phase transitions using Machine Learning (ML) methods. In this approach the local neighborhood of atoms is characterized in terms of symmetry functions that are used as input to a ML algorithm trained to identify atomic rearrangements leading to structural transformations. The application of the method is illustrated using Molecular Dynamics simulations of crystallization from the liquid or amorphous phase. We also discuss how meaningful physical properties can be extracted from the output of the ML algorithm. 
Wednesday, March 6, 2019 5:18PM  5:30PM 
P21.00011: Efficient Training of NeuralNetwork Interatomic Potentials with Atomic Forces Simon Batzner, Boris Kozinsky NeuralNetwork Interatomic Potentials have emerged as a promising method to accelerate time and length scales in Molecular Dynamics simulations of condensed systems. In the learning process, models are usually trained to a set of reference energies from electronicstructure calculations. In addition to total energies, local atomic forces are often also available by the Hellmann–Feynman theorem. Including atomic forces of quantummechanical accuracy in the training process can greatly enhance fidelity of the learned potential energy surface as it provides a wealth of additional information about its shape. However, this training scheme often comes at a much greater computational expense. In this talk, we address the speed and fidelity of integrating forces into the training process and examine strategies for rapid training of neuralnetwork potentials for complex systems involving large training sets. 
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