Patent Publication Number: US-11386248-B2

Title: Method and device for simulating atomic dynamics

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is based on and hereby claims priority under 35 USC 119 to Chinese Patent Application No. 201811444034.4, filed on Nov. 29, 2018, in the China National Intellectual Property Administration, the entire contents of which are incorporated herein by reference. 
     FIELD 
     The present disclosure relates to a method and a device for simulating atomic dynamics, and in particular to a method and a device for simulating atomic dynamics by utilizing a deep neural network. 
     BACKGROUND 
     Molecular dynamics simulation aims to study physical movements of atoms and molecules. With the development of computer technology, the molecular dynamics simulation and the application thereof is attracting increasing attention. On one hand, the molecular dynamics simulation can quantitatively explore mechanisms and laws in nature. On the other hand, the molecular dynamics simulation promotes research and development in an economic, efficient and predictable way. 
     Known tools for molecular dynamics simulation include a Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) developed by Sandia National Laboratory. The LAMMPS can support systems consisting of millions of atoms/molecules in gas, liquid and solid form. In the LAMMPS, a simulation domain is divided into small three-dimensional sub-domains by utilizing spatial decomposition technology, and each sub-domain is to be processed by a processor. In spite of this, simulation by the LAMMPS still requires a lot of computing resources and time. 
     In addition, methods based on deep neural network have been greatly developed in various applications in recent years. In these methods, the deep neural network is typically trained based on a data set, and then the trained deep neural network is deployed in actual applications. 
     SUMMARY 
     The present disclosure provides a solution where molecular dynamics simulation is performed by utilizing a deep neural network. According to a first aspect of the present disclosure, a method of simulating atomic dynamics is provided. The method includes: setting initial positions for multiple specific atoms in a specific scene; calculating, based on the initial positions, positions of the multiple specific atoms at each time in a first time series by utilizing an LAMMPS configured with respect to the specific scene, as real positions; calculating, based on the initial positions, positions of the multiple specific atoms at each time in the first time series by utilizing a generative adversarial network, as predicted positions; improving a configuration of the generative adversarial network based on the real position and the predicted position at the same time; setting initial positions for multiple atoms to be simulated; and calculating positions of the multiple atoms to be simulated at each time in a second time series by utilizing the improved generative adversarial network. 
     According to another aspect of the present disclosure, a device for simulating atomic dynamics is provided. The device includes a memory and one or more processors. The processor is configured to: set initial positions for multiple specific atoms in a specific scene; calculate, based on the initial positions, positions of the multiple specific atoms at each time in a first time series by utilizing an LAMMPS configured with respect to the specific scene, as real positions; calculate, based on the initial positions, positions of the multiple specific atoms at each time in the first time series by utilizing a generative adversarial network, as predicted positions; improve a configuration of the generative adversarial network based on the real position and the predicted position at the same time; set initial positions for multiple atoms to be simulated; and calculate positions of the multiple atoms to be simulated at each time in a second time series by utilizing the improved generative adversarial network. 
     According to another aspect of the present disclosure, a recording medium having stored thereon a program is provided. The program, when executed by a computer, causes the computer to perform the method for simulating atomic dynamics described above. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a general flowchart showing a method of simulating atomic dynamics according to the present disclosure; 
         FIG. 2  is a flowchart showing a process of training a generative adversarial network; 
         FIG. 3  is a schematic block diagram of the generative adversarial network; 
         FIG. 4  shows a specific process of step S 240  shown in  FIG. 2 ; 
         FIG. 5  schematically shows a generative unit implemented with a neural network; 
         FIG. 6  schematically shows a discriminative unit implemented with a neural network; 
         FIG. 7  is a flowchart showing a process of simulating atomic dynamics by utilizing the trained generative adversarial network; and 
         FIG. 8  is a block diagram showing a configuration example of computer hardware for implementing the present disclosure. 
     
    
    
     DETAILED DESCRIPTION OF EMBODIMENTS 
       FIG. 1  is a general flowchart showing a method of simulating atomic dynamics according to the present disclosure. 
     As shown in  FIG. 1 , a generative adversarial network (GAN) is trained by performing steps S 110  to S 130 , and the trained GAN is applied in actual simulations in step S 140 . For clarity, an atom in the training phase is hereinafter referred to as “training atom”, and an atom in the actual simulation phase is hereinafter referred to as “atom to be simulated”. 
     Specifically, in step S 110 , positions of training atoms at a plurality of moments after an initial moment are calculated by utilizing the LAMMPS, as real positions of the training atoms at the corresponding moments. In the present disclosure, a position of an atom may be represented by a three-dimensional coordinate of the atom. Preferably, the three-dimensional coordinate of the atom is normalized within an interval of [0, 1]. 
     In step S 120 , positions of the training atoms at the same moments are acquired by utilizing the GAN, as predicted positions of the training atoms at the corresponding moments. The GAN may be implemented with a deep neural network. 
     In step S 130 , the GAN is trained based the real position and the predicted position of the training atom at the same moment. 
     Next, in step S 140 , positions of atoms which are actual simulation targets at future moments are calculated by utilizing the trained GAN. The position of the atom to be simulated at any future moment may be obtained from the calculation result, thereby obtaining the future movement state of the atom. 
     It should be noted that, in the present disclosure, the future position of an atom is an example of the atomic dynamics, and simulation of the atomic dynamics is not limited to simulation of the future position of the atom. For example, nuclear charge of the atom may be simulated. 
     As shown in  FIG. 1 , in the method according to the present disclosure, the atomic dynamics is simulated by utilizing a trained deep neural network, which has advantages of small amount of computations and low complexity in actual simulations, requiring fewer computing resources and less time compared with the conventional tool LAMMPS. 
     The process of training the GAN is described in detail below with reference to  FIGS. 2 to 4 .  FIG. 2  is a flowchart showing a process for training a GAN. 
     As shown in  FIG. 2 , in step S 210 , initial positions for multiple training atoms at an initial moment are set with respect to a specific scene. For example, in a case that microscopic properties of copper are simulated and studied, initial positions for multiple copper atoms are set based on states such as initial temperature, pressure and density. 
     In step S 220 , based on the initial positions, real positions of the multiple training atoms at a plurality of moments after the initial moment are calculated by utilizing the LAMMPS. For example, the plurality of moments may be set at a predetermined interval or may be set irregularly. 
     In step S 230 , predicted positions of the multiple training atoms at the same moments are calculated by utilizing the GAN based on the initial positions. Since the GAN has not yet been trained, the initial configuration of the GAN is applied. 
     Next, in step S 240 , the GAN is trained based on the real position and the predicted position at the same moment obtained in steps S 220  and S 230 , such that the predicted position calculated by utilizing the GAN can be as close as possible to the real position calculated by utilizing the LAMMPS. The training for the GAN is completed when the predicted position can be deemed to be substantially the same as the real position. 
       FIG. 3  is a schematic block diagram of a GAN.  FIG. 4  shows a specific process of step S 240  shown in  FIG. 2 . 
     As shown in  FIG. 3 , the GAN includes a generative unit  310  and a discriminative unit  320 . In the present disclosure, the generative unit  310  and the discriminative unit  320  may be implemented with neural networks. The generative unit  310  is configured to generate predicted positions for training atoms at the next moment based on the input initial positions (or current positions) of the training atoms. The discriminative unit  320  is configured to receive the predicted positions from the generative unit  310 , and determine a probability that the received predicted position is the real position at the next moment. In a case that the determined probability is greater than 50%, the discriminative unit  320  outputs a determination result of “1” representing that the predicted position is the real position. In a case that the determined probability is less than 50%, the discriminative unit  320  outputs a determination result of “0” representing that the predicted position is not the real position. 
     The discriminative unit  320  makes efforts to determine whether the received predicted position is the real position at the same moment. If the determined probability is large (greater than 50%), the discriminative unit  320  is more convinced that the predicted position is the real position. If the determined probability is small (less than 50%), the discriminative unit  320  is more convinced that the predicted position is not the real position. 
     The generative unit  310  makes efforts to generate the predicted position as close as possible to the real position, so that the discriminative unit  320  has difficulty in distinguishing the predicted position from the real position. In other words, if the discriminative unit  320  has difficulty in determining whether the received predicted position is the real position, that is, if the probability determined by the discriminative unit  320  is 50% or approximate to 50%, the generative unit  310  reaches an ideal state. 
     The generative unit  310  and the discriminative unit  320  constituting the GAN are trained based on the objects as described above. As shown in  FIG. 4 , the discriminative unit  320  is firstly trained in step S 410 , so that the discriminative unit  320  is capable of determining whether the predicted position received from the generative unit  310  is the real position at the same moment, i.e., such that the discriminative unit  320  is capable of determining whether the probability is greater than 50% or less than 50%. 
     It is assumed that the number of training atoms is m, and real positions of the m training atoms calculated by utilizing the LAMMPS are expressed as x 1 , x 2 , . . . , x m  respectively. The discriminative unit  320  may be trained in a manner of maximizing the following loss function L D . 
     
       
         
           
             
               
                 
                   
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     wherein x i  represents a real position of an i-th training atom (i=1, 2, . . . , m), G(x i ) represents predicted position of the i-th training atom generated by the generative unit  310 , and D ( ) represents a probability that a position indicated by the term within the parentheses is the real position. 
     Then, the generative unit  310  is trained in step S 420 , so that the discriminative unit  320  has difficulty in determining whether the predicted position generated by the generative unit  310  is the real position at the same moment, i.e., so that the probability determined by the discriminative unit  320  is 50% or approximate to 50%. 
     Specifically, the generative unit  310  may be trained in a manner of minimizing the following loss function L G . 
     
       
         
           
             
               
                 
                   
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     wherein G(x i ) represents predicted position of the i-th training atom generated by the generative unit  310 , D(G(x i )) represents a probability determined by the discriminative unit  320  that the predicted position is the real position, L MSE  represents mean square error between the real position and the predicted position at the same moment, and λ represents a weighting parameter which may be set as for example 0.5 or 1. It can be seen from the formula (2) that the mean square error L MSE  is required to be minimized in order to minimize the loss function L G . Further, the L MSE  may be calculated from the following formula (3).
 
 L   MSE =Σ i=1   m ( x   i   −G ( x   i )) 2   (3)
 
     The generative unit  310  and the discriminative unit  320  may be alternately and iteratively trained by repeatedly performing steps S 410  and S 420 . In a case that the probability determined by the discriminative unit  320  is 50% or approximate to 50%, it is indicated that the discriminative unit  320  has difficulty in distinguishing the predicted position from the real position. In this case, the predicted position generated by the generative unit  310  can be considered to be the same as the real position calculated by utilizing the LAMMPS. Since the generative unit  310  is capable of generating the same output as the LAMMPS, the generative unit  310  can be applied in actual simulations to take place of the LAMMPS. As such, the training for the GAN is completed. 
       FIGS. 5 and 6  respectively schematically show neural network structures of the generative unit  310  and the discriminative unit  320 , in which a circle represents a neural cell. As shown in  FIG. 5 , a position coordinate x(t) of an atom at time t is input at an input end of the generative unit  310 , and a predicted position coordinate x′(t+n) of the atom at time t+n is output at an output end of the generative unit  310 . As shown in  FIG. 6 , the predicted position coordinate x′(t+n) output by the generative unit  310  and a real position coordinate x(t+n) of the atom at time t+n calculated by the LAMMPS are input at an input end of the discriminative unit  320 , and a determination result “0” or “1” is output at an output end of the discriminative unit  320 . 
     It should be noted that, as described in the above, the position of the atom is just an example of the atomic dynamics, and simulating the atomic dynamics is not limited to simulating the future position of the atom. For example, properties such as the nuclear charge of the atom may also be simulated. Therefore, the position coordinates such as x(t) and x′(t+n) shown in  FIGS. 5 and 6  may be extended to include other properties of the atom. 
     The process of applying the trained GAN in actual simulations is described below with reference to  FIG. 7 , which is a flowchart showing a process of simulating atomic dynamics by utilizing the trained GAN. 
     In step S 710 , initial positions of multiple atoms which are actual simulation targets at an initial moment are set. Next, in step S 720 , positions of atoms to be simulated at one or more moments after the initial moment are acquired by utilizing the trained GAN based on the initial positions, as the simulation result. The one or more moments at which the simulation result is expected may be set based on practical requirements. 
     In step S 730 , properties of a substance made up of the atoms are determined by analyzing the simulation result. For example, it is assumed that positions of multiple copper atoms at a plurality of moments are acquired as the simulation result in step S 720 . Information on the copper atoms, such as movement track, movement direction and speed, may be obtained by analyzing the positions of the copper atoms at the moments in step S 730 , thereby obtaining properties of copper made up of the copper atoms, such as temperature, form and density. 
     Technical solutions of the present disclosure are described above in combination with the embodiments. With the technical solutions of the present disclosure, the trained GAN can produce the same simulation result as the LAMMPS, which has advantages of small amount of computations and low complexity in actual applications, requiring fewer computing resources and less time. 
     The method described in the above embodiments may be performed by software, hardware, or a combination of software and hardware. Programs included in the software may be stored in advance in a storage medium provided inside or outside the device. As an example, during execution, these programs are written to a random-access memory (RAM) and executed by a processor (such as CPU), so as to perform the processes described herein. 
       FIG. 8  is a block diagram showing a configuration example of computer hardware for performing the method according to the present disclosure based on programs. The computer hardware is an example of the device for simulating atomic dynamics according to the present disclosure. 
     As shown in  FIG. 8 , in a computer  800 , a central processing unit (CPU)  801 , a read-only memory (ROM)  802 , and a random-access memory (RAM)  803  are connected to each other via a bus  804 . 
     An input/output interface  805  is further connected to the bus  804 . The input/output interface  805  is connected with the following components: an input unit  806  including keyboard, mouse, microphone and the like; an output unit  807  including display, speaker and the like; a storage unit  808  including hard disk, nonvolatile memory and the like; a communication unit  809  including network interface card (such as local area network (LAN) card, modem); and a driver  810  that drives a removable medium  811  such as magnetic disk, optical disk, magneto-optical disk, or semiconductor memory. 
     In the computer having the above configuration, the CPU  801  loads a program stored in the storage unit  808  into the RAM  803  via the input/output interface  805  and the bus  804 , and executes the program so as to perform the method described above. 
     A program to be executed by the computer (CPU  801 ) may be recorded on the removable medium  811  as a package medium including a magnetic disk (including floppy disk), an optical disk (including compact disk-read only memory (CD-ROM)), digital versatile disk (DVD), and the like), a magneto-optical disk, or a semiconductor memory, and the like. In addition, the program to be executed by the computer (CPU  801 ) may also be provided via wired or wireless transmission medium, such as local area network, the Internet, or digital satellite broadcast. 
     In a case where the removable medium  811  is installed in the driver  810 , the program may be installed in the storage unit  808  via the input/output interface  805 . In addition, the program may be received by the communication unit  809  via the wired or wireless transmission medium, and then the program may be installed in the storage unit  808 . Alternatively, the program may be installed in the ROM  802  or the storage unit  808  in advance. 
     The program to be executed by the computer may be a program that executes the process according to the order described in the present specification or may be a program that executes the process in parallel or executes the process when needed (for example, when called). 
     Units or devices described herein are merely logical in nature and do not strictly correspond to physical devices or entities. For example, the functionality of each unit described herein may be implemented by multiple physical entities or the functionality of multiple units described herein may be implemented by a single physical entity. In addition, it should be noted that features, components, elements or steps, and the like described in an embodiment are not limited to this embodiment, but may also be applied to other embodiments, for example, may substitute for specific features, components, elements, or steps, and the like in other embodiments or may be combined with them. 
     The scope of the present disclosure is not limited to those embodiments described herein. It should be understood by those skill in the art that various modifications or changes in the embodiments discussed herein can be made without departing from the spirit and principle of the present disclosure, depending on design requirements and other factors. The scope of the present disclosure is defined by the appended claims and their equivalents. 
     According to aspects of embodiments,
         (1) A method for simulating atomic dynamics, including:   setting initial positions for multiple specific atoms in a specific scene;   calculating, based on the initial positions, positions of the multiple specific atoms at each time in a first time series by utilizing a Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) configured with respect to the specific scene, as real positions;   calculating, based on the initial positions, positions of the multiple specific atoms at each time in the first time series by utilizing a generative adversarial network, as predicted positions;   improving a configuration of the generative adversarial network based on the real position and the predicted position at the same time;   setting initial positions for multiple atoms to be simulated; and   calculating positions of the multiple atoms to be simulated at each time in a second time series by utilizing the improved generative adversarial network.   (2) The method according to (1), further including:   calculating the predicted positions of the multiple specific atoms at each time in the first time series by utilizing a generative unit in the generative adversarial network;   determining a probability that the predicted position is the real position at the same time by utilizing a discriminative unit in the generative adversarial network; and   improving configurations of the generative unit and the discriminative unit based on the predicted position and the real position at the same time, until the probability determined by the discriminative unit is 50%.   (3) The method according to (2), further including:   improving the discriminative unit in such a manner that the discriminative unit is capable of determining whether the predicted position is the real position at the same time.   (4) The method according to (3), further including:       

     improving the generative unit in such a manner that the discriminative unit has difficulty in determining whether the predicted position calculated by the generative unit is the real position at the same time.
         (5) The method according to (3), further including:   improving the generative unit in a manner of minimizing mean square error between the real position and the predicted position at the same time.   (6) The method according to (1), further including:   determining properties of a substance made up of the multiple atoms to be simulated by analyzing the positions of the multiple atoms to be simulated at each time in the second time series.   (7) The method according to (1), wherein the generative unit and the discriminative unit are implemented with neural networks.   (8) A device for stimulating atomic dynamics, including: a memory, and one or more processors configured to:   set initial positions for multiple specific atoms in a specific scene;   calculate, based on the initial positions, positions of the multiple specific atoms at each time in a first time series by utilizing a Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) configured with respect to the specific scene, as real positions;   calculate, based on the initial positions, positions of the multiple specific atoms at each time in the first time series by utilizing a generative adversarial network, as predicted positions;   improve a configuration of the generative adversarial network based on the real position and the predicted position at the same time;   set initial positions for multiple atoms to be simulated; and   calculate positions of the multiple atoms to be simulated at each time in a second time series by utilizing the improved generative adversarial network.   (9) The device according to (8), wherein the processor is further configured to:   calculate the predicted positions of the multiple specific atoms at each time in the first time series by utilizing a generative unit in the generative adversarial network;   determine a probability that the predicted position is the real position at the same time by utilizing a discriminative unit in the generative adversarial network; and   improve configurations of the generative unit and the discriminative unit based on the predicted position and the real position at the same time, until the probability determined by the discriminative unit is 50%.   (10) The device according to (9), wherein the processor is further configured to:   improve the discriminative unit in such a manner that the discriminative unit is capable of determining whether the predicted position is the real position at the same time; and   improve the generative unit in such a manner that the discriminative unit has difficulty in determining whether the predicted position calculated by the generative unit is the real position at the same time.   (11) A recording medium having stored thereon a program that, when executed by a computer, causes the computer to perform the method for simulating atomic dynamics according to any one of (1) to (7).