High pressure four-way valve Patent

High pressure four-way valve with O ring adapted to pass across inlet por

Pure non-local machine-learned density functional theory for electron correlation

Density-functional theory (DFT) is a rigorous and (in principle) exact framework for the description of the ground state properties of atoms, molecules and solids based on their electron density. While computationally efficient density-functional approximations (DFAs) have become essential tools in computational chemistry, their (semi-)local treatment of electron correlation has a number of well-known pathologies, e.g. related to electron selfinteraction. Here, we present a type of machine-learning (ML) based DFA (termed Kernel Density Functional Approximation, KDFA) that is pure, non-local and transferable, and can be efficiently trained with fully quantitative reference methods. The functionals retain the meanfield computational cost of common DFAs and are shown to be applicable to non-covalent, ionic and covalent interactions, as well as across different system sizes. We demonstrate their remarkable possibilities by computing the free energy surface for the protonated water dimer at hitherto unfeasible gold-standard coupled cluster quality on a single commodity workstation

Towards Density Functional Approximations from Coupled Cluster Correlation Energy Densities

(Semi)local density functional approximations (DFAs) are the workhorse electronic structure methods in condensed matter theory and surface science. The correlation energy density ϵc(r) (a spatial function that yields the correlation energy Ec upon integration) is central to defining such DFAs. Unlike Ec, ϵc(r) is not uniquely defined, however. Indeed, there are infinitely many functions that integrate to the correct Ec for a given electron density ρ. The challenge for constructing useful DFAs is thus to find a suitable connection between ϵc(r) and ρ. Herein, we present a new such approach by deriving ϵc(r) directly from the coupled-cluster (CC) energy expression. The corresponding energy densities are analyzed for prototypical two-electron systems. As a proof-of-principle, we construct a semilocal functional to approximate the numerical CC correlation energy densities. Importantly, the energy densities are not simply used as reference data but guide the choice of the functional form, leading to a remarkably simple and accurate correlation functional for the helium isoelectronic series. While the resulting functional is not transferable to many-electron systems (due to a lack of same-spin correlation), these results underscore the potential of the presented approach

Modeling the Structural Response from a Propagating High Explosive Using Smooth Particle Hydrodynamics

This report primarily concerns the use of two massively parallel finite element codes originally written and maintained at Lawrence Livermore National Laboratory. ALE3D is an explicit hydrodynamics code commonly employed to simulate wave propagation from high energy scenarios and the resulting interaction with nearby structures. This coupled response ensures that a structure is accurately applied with a blast loading varying both in space and time. Figure 1 illustrates the radial outward propagation of a pressure wave due to a center detonated spherical explosive originating from the lower left. The radial symmetry seen in this scenario is lost when instead a cylindrocal charge is detonated. Figure 2 indicates that a stronger, faster traveling pressure wave occurs in the direction of the normal axis to the cylinder. The ALE3D name is derived because of the use of arbitrary-Lagrange-Eulerian elements in which the mesh is allowed to advect; a process through which the mesh is modified to alleviate tanlging and general mesh distortion often cuased by high energy scenarios. The counterpart to an advecting element is a Lagrange element, whose mesh moves with the material. Ideally all structural components are kept Lagrange as long as possible to preserve accuracy of material variables and minimize advection related errors. Advection leads to mixed zoning, so using structural Lagrange elements also improves the visualization when post processing the results. A simplified representation of the advection process is shown in Figure 3. First the mesh is distorted due to material motion during the Lagrange step. The mesh is then shifted to an idealized and less distorted state to prevent irregular zones caused by the Lagrange motion. Lastly, the state variables are remapped to the elements of the newly constructed mesh. Note that Figure 3 represents a purely Eulerian mesh relaxation because the mesh is relocated back to the pre-Lagrange position. This is the case when the material flows through a still mesh. This is not typically done in an ALE3D analysis, especially if Lagrange elements exist. Deforming Lagrange elements would certainly tangle with a Eulerian mesh eventually. The best method in this case is to have an advecting mesh positioned as some relaxed version of the pre and post Lagrange step; this gives the best opportunity of modeling a high energy event with a combination of Lagrange and ALE elements. Dyne3D is another explicit dynamic analysis code, ParaDyn being the parallel version. ParaDyn is used for predicting the transient response of three dimensional structures using Lagrangian solid mechanics. Large deformation and mesh tangling is often resolved through the use of an element deletion scheme. This is useful to accommodate component failure, but if it is done purely as a means to preserve a useful mesh it can lead to problems because it does not maintain continuity of the material bulk response. Whatever medium exists between structural components is typically not modeled in ParaDyn. Instead, a structure either has a known loading profile applied or given initial conditions. The many included contact algorithms can calculate the loading response of materials if and when they collide. A recent implementation of an SPH module in which failed or deleted material nodes are converted to independent particles is currently being utilized for a variety of spall related problems and high velocity impact scenarios. Figure 4 shows an example of a projectile, given an initial velocity, and how it fails the first plate which generates SPH particles which then interact with and damage the second plate

Reorganization energies of flexible organic molecules as a challenging target for machine learning enhanced virtual screening

The molecular reorganization energy λ strongly influences the charge carrier mobility of organic semiconductors and is therefore an important target for molecular design. Machine learning (ML) models generally have the potential to strongly accelerate this design process (e.g. in virtual screening studies) by providing fast and accurate estimates of molecular properties. While such models are well established for simple properties (e.g. the atomization energy), λ poses a significant challenge in this context. In this paper, we address the questions of how ML models for λ can be improved and what their benefit is in high-throughput virtual screening (HTVS) studies. We find that, while improved predictive accuracy can be obtained relative to a semiempirical baseline model, the improvement in molecular discovery is somewhat marginal. In particular, the ML enhanced screenings are more effective in identifying promising candidates but lead to a less diverse sample. We further use substructure analysis to derive a general design rule for organic molecules with low λ from the HTVS results

Data-efficient machine learning for molecular crystal structure prediction

The combination of modern machine learning (ML) approaches with high-quality data from quantum mechanical (QM) calculations can yield models with an unrivalled accuracy/cost ratio. However, such methods are ultimately limited by the computational effort required to produce the reference data. In particular, reference calculations for periodic systems with many atoms can become prohibitively expensive for higher levels of theory. This trade-off is critical in the context of organic crystal structure prediction (CSP). Here, a data-efficient ML approach would be highly desirable, since screening a huge space of possible polymorphs in a narrow energy range requires the assessment of a large number of trial structures with high accuracy. In this contribution, we present tailored Δ-ML models that allow screening a wide range of crystal candidates while adequately describing the subtle interplay between intermolecular interactions such as H-bonding and many-body dispersion effects. This is achieved by enhancing a physics-based description of long-range interactions at the density functional tight binding (DFTB) level—for which an efficient implementation is available—with a short-range ML model trained on high-quality first-principles reference data. The presented workflow is broadly applicable to different molecular materials, without the need for a single periodic calculation at the reference level of theory. We show that this even allows the use of wavefunction methods in CSP

Regularized second-order correlation methods for extended systems

Second-order Møller–Plesset perturbation theory (MP2) constitutes the simplest form of many-body wavefunction theory and often provides a good compromise between efficiency and accuracy. There are, however, well-known limitations to this approach. In particular, MP2 is known to fail or diverge for some prototypical condensed matter systems like the homogeneous electron gas (HEG) and to overestimate dispersion-driven interactions in strongly polarizable systems. In this paper, we explore how the issues of MP2 for metallic, polarizable, and strongly correlated periodic systems can be ameliorated through regularization. To this end, two regularized second-order methods (including a new, size-extensive Brillouin–Wigner approach) are applied to the HEG, the one-dimensional Hubbard model, and the graphene–water interaction. We find that regularization consistently leads to improvements over the MP2 baseline and that different regularizers are appropriate for the various systems

Heterogeneous Catalysis in Grammar School

The discovery of new catalytically active materials is one of the holy grails of computational chemistry as it has the potential to accelerate the adoption of renewable energy sources and reduce the energy consumption of chemical industry. Indeed, heterogeneous catalysis is essential for the production of synthetic fuels and many commodity chemicals. Consequently, novel solid catalysts with higher activity and selectivity, increased sustainability and longevity, or improved prospects for rejuvenation and cyclability are needed for a diverse range of processes. Unfortunately, computational catalyst discovery is a daunting task, among other reasons because it is often unclear whether a proposed material is stable or synthesizable. This perspective proposes a new approach to this challenge, namely the use of generative grammars. We outline how grammars can guide the search for stable catalysts in a large chemical space and sketch out several research directions that would make this technology applicable to real materials

How Robust are Modern Graph Neural Network Potentials in Long and Hot Molecular Dynamics Simulations?

Graph neural networks (GNNs) have emerged as a powerful machine learning approach for the prediction of molecular properties. In particular, recently proposed advanced GNN models promise quantum chemical accuracy at a fraction of the computational cost. While the capabilities of such advanced GNNs have been extensively demonstrated on benchmark datasets, there have been few applications in real atomistic simulations. Here, we therefore put the robustness of GNN interatomic potentials to the test, using the recently proposed GemNet architecture as a testbed. Models are trained on the QM7-x database of organic molecules and used to perform extensive MD simulations. We find that low test set errors are not sufficient for obtaining stable dynamics and that severe pathologies sometimes only become apparent after hundreds of ps of dynamics. Nonetheless, highly stable and transferable GemNet potentials can be obtained with sufficiently large training sets

Single-Reference Coupled Cluster Theory for Multi-Reference Problems

Coupled cluster (CC) theory is widely accepted as the most accurate and generally applicable approach in quantum chemistry. CC calculations are usually performed with single Slater-determinant references, e.g., canonical Hartree-Fock (HF) wavefunctions, though any single determinant can be used. This is an attractive feature because typical CC calculations are straightforward to apply, as there is no potentially ambiguous user input required. On the other hand, there can be concern that CC approximations give unreliable results when the reference determinant provides a poor description of the system of interest, i.e., when the HF or any other single determinant ground state has a relatively low weight in the full CI expansion. However, in many cases, the reported “failures” of CC can be attributed to an unfortunate choice of reference determinant, rather than intrinsic shortcomings of CC itself. This is connected to well-known effects like spin-contamination, wavefunction instability, and symmetry-breaking. In this contribution, a particularly difficult singlet/triplet splitting problem in two phenyldinitrene molecules is investigated, where CC with singles, doubles and perturbative triples [CCSD(T)] was reported to give poor results. This is analyzed by using different reference determinants for CCSD(T), as well as performing higher level CCSDT-3 and CCSDT calculations. We show that doubly electron attached and doubly ionized equation-of-motion (DEA/DIP-EOM) approaches are powerful alternatives for treating such systems. These are operationally single-determinant methods that adequately take the multi-reference nature of these molecules into account. Our results indicate that CC remains a powerful tool for describing systems with both static correlation and dynamic correlation, when pitfalls associated with the choice of the reference determinant are avoided