sGDML: Constructing Accurate and Data Efficient Molecular Force Fields Using Machine Learning

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., J. Phys. Condens. Matter 29, 273002 (2017)) and i-PI (Kapil et al., Comput. Phys. Commun. 236, 214-223 (2019)) to run numerical experiments, including structure optimization, classical and path integral molecular dynamics and nudged elastic band calculations

Molecular Force Fields with Gradient-Domain Machine Learning: Construction and Application to Dynamics of Small Molecules with Coupled Cluster Forces

We present the construction of molecular force fields for small molecules (less than 25 atoms) using the recently developed symmetrized gradient-domain machine learning (sGDML) approach [Chmiela et al., Nat. Commun. 9, 3887 (2018); Sci. Adv. 3, e1603015 (2017)]. This approach is able to accurately reconstruct complex high-dimensional potential-energy surfaces from just a few 100s of molecular conformations extracted from ab initio molecular dynamics trajectories. The data efficiency of the sGDML approach implies that atomic forces for these conformations can be computed with high-level wavefunction-based approaches, such as the "gold standard" CCSD(T) method. We demonstrate that the flexible nature of the sGDML model recovers local and non-local electronic interactions (e.g. H-bonding, proton transfer, lone pairs, changes in hybridization states, steric repulsion and $n\to\pi^*$ interactions) without imposing any restriction on the nature of interatomic potentials. The analysis of sGDML molecular dynamics trajectories yields new qualitative insights into dynamics and spectroscopy of small molecules close to spectroscopic accuracy

Distributions of Grain Parameters on the Surface of Aircraft Engine Turbine Blades

In the quality assurance system for components cast using the lost wax method, the object of evaluation is the grain size on the surface of the casting. This paper describes a new method for evaluating the primary grain parameters on the surface of aircraft engine turbine blades. Effectiveness of the method has been tested on two macrostructures distinguished by a high degree of diversity in the grain size. The grounds for evaluating the grain parameters consist of geometric measurement of the turbine blade using a laser profilometer and of approximation of the measurement results using a polynomial of a proper degree. The so obtained analytical non-planar surface serves as a reference point for an assessment of the parameters of grains observed on the real blade surface of a variable curvature. The aspects subjected to evaluation included: the grain areas, shape and elongation coefficients of grains on a non-planar surface of the blade airfoil, using measurements taken on a perpendicular projection by means of a stereoscopic microscope and image analysis methods, and by making calculations using the Mathematica® package

SchNet: A continuous-filter convolutional neural network for modeling quantum interactions

Deep learning has the potential to revolutionize quantum chemistry as it is ideally suited to learn representations for structured data and speed up the exploration of chemical space. While convolutional neural networks have proven to be the first choice for images, audio and video data, the atoms in molecules are not restricted to a grid. Instead, their precise locations contain essential physical information, that would get lost if discretized. Thus, we propose to use continuous-filter convolutional layers to be able to model local correlations without requiring the data to lie on a grid. We apply those layers in SchNet: a novel deep learning architecture modeling quantum interactions in molecules. We obtain a joint model for the total energy and interatomic forces that follows fundamental quantum-chemical principles. This includes rotationally invariant energy predictions and a smooth, differentiable potential energy surface. Our architecture achieves state-of-the-art performance for benchmarks of equilibrium molecules and molecular dynamics trajectories. Finally, we introduce a more challenging benchmark with chemical and structural variations that suggests the path for further work

Inhomogenity of the grain size of aircraft engine turbine polycrystalline blades

The determination of the behaviour of inhomogeneous materials with a complex microstructure requires taking into account the inhomogeneity of the grain size, as it is the basis for the process of designing and modelling effective behaviours. Therefore, the functional description of the inhomogeneity is becoming an important issue. The paper presents an analytical approach to the grain size inhomogeneity, based on the derivative of a logarithmic-logistic function. The solution applied enabled an effective evaluation of the inhomogeneity of two macrostructures of aircraft engine turbine blades, characterized by a high degree of diversity in the grain size. For the investigated single-modal and bimodal grain size distributions on a perpendicular projection and for grains with a non-planar surface, we identified the parameters that describe the degree of inhomogeneity of the constituents of weight distributions and we als o derived a formula describing the overall degree of homogeneity of bimodal distributions. The solution presented in the paper is of a general nature and it can be used to describe the degree of inhomogeneity of multi-modal distributions. All the calculations were performed using the Mathematica® package

Reconstructing Kernel-based Machine Learning Force Fields with Super-linear Convergence

Kernel machines have sustained continuous progress in the field of quantum chemistry. In particular, they have proven to be successful in the low-data regime of force field reconstruction. This is because many physical invariances and symmetries can be incorporated into the kernel function to compensate for much larger datasets. So far, the scalability of this approach has however been hindered by its cubical runtime in the number of training points. While it is known, that iterative Krylov subspace solvers can overcome these burdens, they crucially rely on effective preconditioners, which are elusive in practice. Practical preconditioners need to be computationally efficient and numerically robust at the same time. Here, we consider the broad class of Nystr\"om-type methods to construct preconditioners based on successively more sophisticated low-rank approximations of the original kernel matrix, each of which provides a different set of computational trade-offs. All considered methods estimate the relevant subspace spanned by the kernel matrix columns using different strategies to identify a representative set of inducing points. Our comprehensive study covers the full spectrum of approaches, starting from naive random sampling to leverage score estimates and incomplete Cholesky factorizations, up to exact SVD decompositions.Comment: 18 pages, 12 figures, preprin

Towards Exact Molecular Dynamics Simulations with Machine-Learned Force Fields

Molecular dynamics (MD) simulations employing classical force fields constitute the cornerstone of contemporary atomistic modeling in chemistry, biology, and materials science. However, the predictive power of these simulations is only as good as the underlying interatomic potential. Classical potentials often fail to faithfully capture key quantum effects in molecules and materials. Here we enable the direct construction of flexible molecular force fields from high-level ab initio calculations by incorporating spatial and temporal physical symmetries into a gradient-domain machine learning (sGDML) model in an automatic data-driven way. The developed sGDML approach faithfully reproduces global force fields at quantum-chemical CCSD(T) level of accuracy and allows converged molecular dynamics simulations with fully quantized electrons and nuclei. We present MD simulations, for flexible molecules with up to a few dozen atoms and provide insights into the dynamical behavior of these molecules. Our approach provides the key missing ingredient for achieving spectroscopic accuracy in molecular simulations

Modifications of the chemical composition and microstructure of flash smelting copper slags in the process of their reduction

Blister copper smelting in a flash smelting furnace results in generation of slag that contains high amounts of copper, iron and lead. Most commonly, this material is subjected to reduction with coke in an electrical furnace. In the present paper, results of investigations on reduction of slag with another reducer, i.e. anthracite dust, are discussed. Each experimental slag was analysed for its microstructure, chemical composition and phase composition. Based on the results, a decopperisation level of the study material was estimated. It was shown that anthracite dust might be considered as an alternative for currently used reducers