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Nowadays, the interoperability and interfacing of codes and libraries have become crucial aspects of software development and engineering, and the basis for enabling and simplifying the sharing of methods and tools, both within and among communities. One of the most important bottlenecks that arises when developing and maintaining an interface of a library with an already existing software, is to keep it aligned with the development route of the latter. This might include significant changes, such as changes in the data structures used by the library, which are communicated through the interface. In this paper, an approach for inserting a new algorithm into existing software is presented, through a minimally invasive interface, that takes over an already present algorithm, and thus changes its original purpose. The approach is applied to the well-established Activation-Relaxation Technique nouveau (ARTn) algorithm, that is revisited and re-engineered to bias and take over the FIRE minimization algorithm, as presently implemented in two community codes for atomistic simulations, namely Quantum ESPRESSO (PWscf) and LAMMPS. ARTn is a well established single-ended saddle-point search algorithm that allows for the exploration of potential energy surfaces. The resulting algorithm acts as a plugin, and is distributed in the form of an external library (pARTn). Program Title: plugin Activation Relaxation Technique nouveau (pARTn) CPC Library link to program files: https://doi.org/10.17632/xpwr56d2yk.1 Developer's repository link: https://gitlab.com/mammasmias/artn-plugin Licensing provisions: Apache-2.0/GPLv3 Programming language: Modern Fortran and C++ Nature of problem: Original ARTn implementation was difficult to interface, port and maintain. Solution method: Full refactoring and re-engineering into a library, which utilizes and biases a minimization algorithm already implemented in the engine, to make it behave as ARTn. The biasing is done by reverse-engineering specific external conditions that drive the displacement of atoms. [ABSTRACT FROM AUTHOR]

We present an optimized implementation of the recently proposed symmetric gradient domain machine learning (sGDML) model. The sGDML model is able to faithfully reproduce global potential energy surfaces (PES) for molecules with a few dozen atoms from a limited number of user-provided reference molecular conformations and the associated atomic forces. Here, we introduce a Python software package to reconstruct and evaluate custom sGDML force fields (FFs), without requiring in-depth knowledge about the details of the model. A user-friendly command-line interface offers assistance through the complete process of model creation, in an effort to make this novel machine learning approach accessible to broad practitioners. Our paper serves as a documentation, but also includes a practical application example of how to reconstruct and use a PBE0+MBD FF for paracetamol. Finally, we show how to interface sGDML with the FF simulation engines ASE (Larsen et al., 2017) and i-PI (Kapil et al., 2019) to run numerical experiments, including structure optimization, classical and path integral molecular dynamics and nudged elastic band calculations. [ABSTRACT FROM AUTHOR]

We have developed a software package based on a genetic algorithm that fits an analytic function to a given set of data points. The code, called GAFit, was also interfaced with the CHARMM and MOPAC programs in order to facilitate force field parameterizations and fittings of specific reaction parameters (SRP) for semiempirical Hamiltonians. The present tool may be applied to a wide range of fitting problems, though it has been especially designed to significantly reduce the hard work involved in the development of potential energy surfaces for complex systems. For this purpose, it has been equipped with several programs to help the user in the preparation of the input files. We showcase the application of the computational tool to several chemical-relevant problems: force-field parameterization, with emphasis on nonbonded energy terms or intermolecular potentials, derivation of SRP for semiempirical Hamiltonians, and fittings of generic analytical functions. Program summary Program title : GAFit Program Files doi : http://dx.doi.org/10.17632/9gy6bjcwk5.1 Licensing provisions : GNU General Public License 3 (GPL) Programming language : Fortran 90, C, Perl and Java Nature of problem : Potential energy surfaces (PESs) have a key role in reaction dynamics and molecular dynamics. Chemical dynamics simulations can be performed by using analytical PESs or by direct dynamics, that is, by computing energies and forces “on-the-fly” by molecular structure calculations. The development of analytical PESs is, quite often, a very complex and tedious task. There are several programs in the literature that may help to develop PESs but, as far as we know, none of them show, at the same time, great generality and flexibility. Solution method : GAFit was designed to help users develop analytical potential energy functions. We made special efforts to write a code that exhibits the three features mentioned above. For this reason, we have also interfaced GAFit with the CHARMM and MOPAC programs. The former is one of the popular packages for molecular dynamics and the latter is a semiempirical quantum chemistry program that may be conveniently used for direct dynamics simulations. Because, in general, the fitting of PESs involves a simultaneous optimization of many nonlinear parameters, we have selected a genetic algorithm as the driver for the parameterizations. As shown in the paper, GAFit may be applied to other fitting problems. [ABSTRACT FROM AUTHOR]

The permutationally invariant combined hyperbolic inverse power representation (CHIPR) polynomial expansion in terms of hyperbolic secant basis proves to be an efficient model in describing the multi state coupled potential energy surface (MSCPES) and this fact was tested on the O 2 H + triatomic ion and its diatomic sub units upon dissociation with an unmatchable accuracy of fitting in comparison with the previous works (below 1 cm − 1 in the case of diatomic function and below 300 cm − 1 in the case of triatomic function). In general, this model is capable of modeling A 3 (H 3 , O 3 etc.) and ABC (OCS, HCO + etc.) triatomic system in addition to the A 2 B (O 2 H +) system. This paper presents a Mathematica notebook (M-CHIPR.nb file) to perform the fitting of di and triatomic potentials in a user friendly manner with proper explanations written along side wherever required in the.nb file supplied. The advantage of this code is that the computing clusters are not required to use it and can be run in normal pc with good processor and memory, apart from its numerical accuracy and the system independent consistency over the similar programs written in other languages. Program title: M-CHIPR CPC Library link to program files: https://doi.org/10.17632/4mbydkzyts.1 Licensing provisions: GPLv3 Programming language: Wolfram Mathematica version12 or higher Nature of problem: Finding an multi-variable Mathematica combined hyperbolic inverse power representation (M-CHIPR) function to describe a multi state coupled potential energy surface for a typical diatomic and triatomic molecule via extensive nonlinear regression method involving hundreds of optimizable variables with minimal fitting error with respect to the raw data points. Solution method: Default Mathematica implementation of nonlinear least square fitting of ab initio points to obtain a well optimized model function having ability to accurately predicting the data behavior. Additional comments including restrictions and unusual features: The entire work was tested on Apple Mac pc with Intel i7 processor having 4 cores. The version of Mathematica is 12 and therefore, the users are advised to test the supplied notebook with the same version and beyond to avoid incompatibility issues. [ABSTRACT FROM AUTHOR]

Abstract: Potential energy surfaces calculated with self-consistent mean-field methods are a very powerful tool, since their solutions are, in theory, global minima of the non-constrained subspace. However, this minimization leads to an incertitude concerning the saddle points, that can sometimes be no longer saddle points in larger constrained subspaces (fake saddle points), or can be missing on a trajectory (missing saddle points). These phenomena are the consequences of discontinuities of the self-consistent potential energy surfaces (SPESs). These discontinuities may have important consequences, since they can, for example, hide the real height of an energy barrier, and avoid any use of an SPES for further dynamical calculations, barrier penetrability estimations, or trajectory predictions. Discontinuities are not related to the quality of the production of an SPES, since even a perfectly converged SPES with an ideally fine mesh can be discontinuous. In this paper we explain what the discontinuities are, their consequences, and their origins. We then propose a numerical method to detect and identify discontinuities on a given SPES, and finally we discuss what the best ways are to transform a discontinuous SPES into a continuous one. [Copyright &y& Elsevier]

Abstract: The latest release of NWChem delivers an open-source computational chemistry package with extensive capabilities for large scale simulations of chemical and biological systems. Utilizing a common computational framework, diverse theoretical descriptions can be used to provide the best solution for a given scientific problem. Scalable parallel implementations and modular software design enable efficient utilization of current computational architectures. This paper provides an overview of NWChem focusing primarily on the core theoretical modules provided by the code and their parallel performance. Program summary: Program title: NWChem Catalogue identifier: AEGI_v1_0 Program summary URL: http://cpc.cs.qub.ac.uk/summaries/AEGI_v1_0.html Program obtainable from: CPC Program Library, Queen''s University, Belfast, N. Ireland Licensing provisions: Open Source Educational Community License No. of lines in distributed program, including test data, etc.: 11 709 543 No. of bytes in distributed program, including test data, etc.: 680 696 106 Distribution format: tar.gz Programming language: Fortran 77, C Computer: all Linux based workstations and parallel supercomputers, Windows and Apple machines Operating system: Linux, OS X, Windows Has the code been vectorised or parallelized?: Code is parallelized Classification: 2.1, 2.2, 3, 7.3, 7.7, 16.1, 16.2, 16.3, 16.10, 16.13 Nature of problem: Large-scale atomistic simulations of chemical and biological systems require efficient and reliable methods for ground and excited solutions of many-electron Hamiltonian, analysis of the potential energy surface, and dynamics. Solution method: Ground and excited solutions of many-electron Hamiltonian are obtained utilizing density-functional theory, many-body perturbation approach, and coupled cluster expansion. These solutions or a combination thereof with classical descriptions are then used to analyze potential energy surface and perform dynamical simulations. Additional comments: Full documentation is provided in the distribution file. This includes an INSTALL file giving details of how to build the package. A set of test runs is provided in the examples directory. The distribution file for this program is over 90 Mbytes and therefore is not delivered directly when download or Email is requested. Instead a html file giving details of how the program can be obtained is sent. Running time: Running time depends on the size of the chemical system, complexity of the method, number of cpu''s and the computational task. It ranges from several seconds for serial DFT energy calculations on a few atoms to several hours for parallel coupled cluster energy calculations on tens of atoms or ab-initio molecular dynamics simulation on hundreds of atoms. [Copyright &y& Elsevier]

In recent years, promising deep learning based interatomic potential energy surface (PES) models have been proposed that can potentially allow us to perform molecular dynamics simulations for large scale systems with quantum accuracy. However, making these models truly reliable and practically useful is still a very non-trivial task. A key component in this task is the generation of datasets used in model training. In this paper, we introduce the Deep Potential GENerator (DP-GEN), an open-source software platform that implements the recently proposed "on-the-fly" learning procedure (Zhang et al. 2019) and is capable of generating uniformly accurate deep learning based PES models in a way that minimizes human intervention and the computational cost for data generation and model training. DP-GEN automatically and iteratively performs three steps: exploration, labeling, and training. It supports various popular packages for these three steps: LAMMPS for exploration, Quantum Espresso, VASP, CP2K, etc. for labeling, and DeePMD-kit for training. It also allows automatic job submission and result collection on different types of machines, such as high performance clusters and cloud machines, and is adaptive to different job management tools, including Slurm, PBS, and LSF. As a concrete example, we illustrate the details of the process for generating a general-purpose PES model for Cu using DP-GEN. Program Title: DP-GEN Program Files doi: http://dx.doi.org/10.17632/sxybkgc5xc.1 Licensing provisions: LGPL Programming language: Python Nature of problem: Generating reliable deep learning based potential energy models with minimal human intervention and computational cost. Solution method: The concurrent learning scheme is implemented. Supports for sampling configuration space with LAMMPS, generating ab initio data with Quantum Espresso, VASP, CP2K and training potential models with DeePMD-kit are provided. Supports for different machines including workstations, high performance clusters and cloud machines are provided. Supports for job management tools including Slurm, PBS, LSF are provided. [ABSTRACT FROM AUTHOR]