O(N)Stochastic Evaluation of Many-Body van der Waals Energies in Large Complex Systems

We propose a new strategy to solve the Tkatchenko-Scheffler Many-Body Dispersion (MBD) model’s equations. Our approach overcomes the original O(N**3) computational complexity that limits its applicability to large molecular systems within thecontext of O(N) Density Functional Theory (DFT). First, in order to generate the required frequency-dependent screenedpolarizabilities, we introduce an efficient solution to the Dyson-like self-consistent screening equations. The scheme reducesthe number of variables and, coupled to a DIIS extrapolation, exhibits linear-scaling performances. Second, we apply astochastic Lanczos trace estimator resolution to the equations evaluating the many-body interaction energy of coupled quantumharmonic oscillators. While scaling linearly, it also enables communication-free pleasingly-parallel implementations. As the resulting O(N) stochastic massively parallel MBD approach is found to exhibit minimal memory requirements, it opens up the possibility of computing accurate many-body van der Waals interactions of millions-atoms’ complex materials and solvated biosystems with computational times in the range of minutes.

O(N)Stochastic Evaluation of Many-Body van der Waals Energies in Large Complex Systems

We propose a new strategy to solve the Tkatchenko-Scheffler Many-Body Dispersion (MBD) model’s equations. Our approach overcomes the original O(N**3) computational complexity that limits its applicability to large molecular systems within thecontext of O(N) Density Functional Theory (DFT). First, in order to generate the required frequency-dependent screenedpolarizabilities, we introduce an efficient solution to the Dyson-like self-consistent screening equations. The scheme reducesthe number of variables and, coupled to a DIIS extrapolation, exhibits linear-scaling performances. Second, we apply astochastic Lanczos trace estimator resolution to the equations evaluating the many-body interaction energy of coupled quantumharmonic oscillators. While scaling linearly, it also enables communication-free pleasingly-parallel implementations. As the resulting O(N) stochastic massively parallel MBD approach is found to exhibit minimal memory requirements, it opens up the possibility of computing accurate many-body van der Waals interactions of millions-atoms’ complex materials and solvated biosystems with computational times in the range of minutes.

Complex pathways and memory in compressed corrugated sheets

The nonlinear response of driven complex materials—disordered magnets, amorphous media, and crumpled sheets—features intricate transition pathways where the system repeatedly hops between metastable states. Such pathways encode memory effects and may allow information processing, yet tools are lacking to experimentally observe and control these pathways, and their full breadth has not been explored. Here we introduce compression of corrugated elastic sheets to precisely observe and manipulate their full, multistep pathways, which are reproducible, robust, and controlled by geometry. We show how manipulation of the boundaries allows us to elicit multiple targeted pathways from a single sample. In all cases, each state in the pathway can be encoded by the binary state of material bits called hysterons, and the strength of their interactions plays a crucial role. In particular, as function of increasing interaction strength, we observe Preisach pathways, expected in systems of independently switching hysterons; scrambled pathways that evidence hitherto unexplored interactions between these material bits; and accumulator pathways which leverage these interactions to perform an elementary computation. Our work opens a route to probe, manipulate, and understand complex pathways, impacting future applications in soft robotics and information processing in materials.

Revealing nano-scale lattice distortions in implanted material with 3D Bragg ptychography

Small ion-irradiation-induced defects can dramatically alter material properties and speed up degradation. Unfortunately, most of the defects irradiation creates are below the visibility limit of state-of-the-art microscopy. As such, our understanding of their impact is largely based on simulations with major unknowns. Here we present an x-ray crystalline microscopy approach, able to image with high sensitivity, nano-scale 3D resolution and extended field of view, the lattice strains and tilts in crystalline materials. Using this enhanced Bragg ptychography tool, we study the damage helium-ion-irradiation produces in tungsten, revealing a series of crystalline details in the 3D sample. Our results lead to the conclusions that few-atom-large ‘invisible’ defects are likely isotropic in orientation and homogeneously distributed. A partially defect-denuded region is observed close to a grain boundary. These findings open up exciting perspectives for the modelling of irradiation damage and the detailed analysis of crystalline properties in complex materials.

Molecular-Dynamics Simulations of the Emergence of Surface Roughness in a Polymer under Compression

Roughness of surfaces is both surprisingly ubiquitous on all length scales and extremely relevant practically. The appearance of multi-scale roughness has been linked to avalanches and plastic deformation in metals. However, other, more-complex materials have mechanisms of plasticity that are significantly different from those of metals. We investigated the emergence of roughness in a polymer under compression. We performed molecular-dynamics simulations of a slab of solid polyvinyl alcohol that was compressed bi-axially, and we characterised the evolution of the surface roughness. We found significantly different behaviour than what was previously observed in similar simulations of metals. We investigated the differences and argue that the visco-elasticity of the material plays a crucial role.

Functional Nanoscale Phase Separation and Intertwined Order in Quantum Complex Materials

Nanoscale phase separation (NPS), characterized by particular types of correlated disorders, plays an important role in the functionality of high-temperature superconductors (HTS). Our results show that multiscale heterogeneity is an essential ingredient of quantum functionality in complex materials. Here, the interactions developing between different structural units cause dynamical spatiotemporal conformations with correlated disorder; thus, visualizing conformational landscapes is fundamental for understanding the physical properties of complex matter and requires advanced methodologies based on high-precision X-ray measurements. We discuss the connections between the dynamical correlated disorder at nanoscale and the functionality in oxygen-doped perovskite superconducting materials.

Modeling of shock-wave loading of carbides as mixtures of components

The results of numerical experiments on the modeling of shock wave loading of solid and porous carbides with various stoichiometric compositions are presented. The model is based on the assumption that all the components of the mixture, including gas, have similar pressure, velocity and temperature. The model allows describing the behavior of porous materials and mixes in a wide range of porosity and pressures with precision of experiment. The behavior of complex materials such as carbides is considered as a mixture. The model accurately describes the behavior of the carbides with equal shares of the components of WC, TiC, TaC, NbC and the behavior of boron carbide B4C. Comparison for data of calculation and experimental data was held for carbides with different porosity.

Materials genes of heterogeneous catalysis from clean experiments and artificial intelligence

The performance in heterogeneous catalysis is an example of a complex materials function, governed by an intricate interplay of several processes (e.g., the different surface chemical reactions, and the dynamic restructuring of the catalyst material at reaction conditions). Modeling the full catalytic progression via first-principles statistical mechanics is impractical, if not impossible. Instead, we show here how a tailored artificial-intelligence approach can be applied, even to a small number of materials, to model catalysis and determine the key descriptive parameters (“materials genes”) reflecting the processes that trigger, facilitate, or hinder catalyst performance. We start from a consistent experimental set of “clean data,” containing nine vanadium-based oxidation catalysts. These materials were synthesized, fully characterized, and tested according to standardized protocols. By applying the symbolic-regression SISSO approach, we identify correlations between the few most relevant materials properties and their reactivity. This approach highlights the underlying physicochemical processes, and accelerates catalyst design. Impact statement Artificial intelligence (AI) accepts that there are relationships or correlations that cannot be expressed in terms of a closed mathematical form or an easy-to-do numerical simulation. For the function of materials, for example, catalysis, AI may well capture the behavior better than the theory of the past. However, currently the flexibility of AI comes together with a lack of interpretability, and AI can only predict aspects that were included in the training. The approach proposed and demonstrated in this IMPACT article is interpretable. It combines detailed experimental data (called "clean data") and symbolic regression for the identification of the key descriptive parameters (called "materials genes") that are correlated with the materials function. The approach demonstrated here for the catalytic oxidation of propane will accelerate the discovery of improved or novel materials while also enhancing physical understanding.

Thermal transport and phase transitions of zirconia by on-the-fly machine-learned interatomic potentials

Machine-learned interatomic potentials enable realistic finite temperature calculations of complex materials properties with first-principles accuracy. It is not yet clear, however, how accurately they describe anharmonic properties, which are crucial for predicting the lattice thermal conductivity and phase transitions in solids and, thus, shape their technological applications. Here we employ a recently developed on-the-fly learning technique based on molecular dynamics and Bayesian inference in order to generate an interatomic potential capable to describe the thermodynamic properties of zirconia, an important transition metal oxide. This machine-learned potential accurately captures the temperature-induced phase transitions below the melting point. We further showcase the predictive power of the potential by calculating the heat transport on the basis of Green–Kubo theory, which allows to account for anharmonic effects to all orders. This study indicates that machine-learned potentials trained on the fly offer a routine solution for accurate and efficient simulations of the thermodynamic properties of a vast class of anharmonic materials.