Structure–property relations of silicon oxycarbides studied using a machine learning interatomic potential

Silicon oxycarbides show outstanding versatility due to their highly tunable composition and microstructure. Consequently, a key challenge is a thorough knowledge of structure–property relations in the system. In this work, we fit an atomic cluster expansion potential to a set of actively learned density‐functional theory training data spanning a wide configurational space. We demonstrate the ability of the potential to produce realistic amorphous structures and rationalize the formation of different morphologies of the turbostratic free carbon phase. Finally, we relate the materials stiffness to its composition and microstructure, finding a delicate dependence on Si‐C bonds that contradicts commonly assumed relations to the free carbon phase

Lineage-specific requirements of β-catenin in neural crest development

β-Catenin plays a pivotal role in cadherin-mediated cell adhesion. Moreover, it is a downstream signaling component of Wnt that controls multiple developmental processes such as cell proliferation, apoptosis, and fate decisions. To study the role of β-catenin in neural crest development, we used the Cre/loxP system to ablate β-catenin specifically in neural crest stem cells. Although several neural crest–derived structures develop normally, mutant animals lack melanocytes and dorsal root ganglia (DRG). In vivo and in vitro analyses revealed that mutant neural crest cells emigrate but fail to generate an early wave of sensory neurogenesis that is normally marked by the transcription factor neurogenin (ngn) 2. This indicates a role of β-catenin in premigratory or early migratory neural crest and points to heterogeneity of neural crest cells at the earliest stages of crest development. In addition, migratory neural crest cells lateral to the neural tube do not aggregate to form DRG and are unable to produce a later wave of sensory neurogenesis usually marked by the transcription factor ngn1. We propose that the requirement of β-catenin for the specification of melanocytes and sensory neuronal lineages reflects roles of β-catenin both in Wnt signaling and in mediating cell–cell interactions

From electrons to phase diagrams with classical and machine learning potentials: automated workflows for materials science with pyiron

We present a comprehensive and user-friendly framework built upon the pyiron integrated development environment (IDE), enabling researchers to perform the entire Machine Learning Potential (MLP) development cycle consisting of (i) creating systematic DFT databases, (ii) fitting the Density Functional Theory (DFT) data to empirical potentials or MLPs, and (iii) validating the potentials in a largely automatic approach. The power and performance of this framework are demonstrated for three conceptually very different classes of interatomic potentials: an empirical potential (embedded atom method - EAM), neural networks (high-dimensional neural network potentials - HDNNP) and expansions in basis sets (atomic cluster expansion - ACE). As an advanced example for validation and application, we show the computation of a binary composition-temperature phase diagram for Al-Li, a technologically important lightweight alloy system with applications in the aerospace industry

From electrons to phase diagrams with machine learning potentials using pyiron based automated workflows

We present a comprehensive and user-friendly framework built upon the pyiron integrated development environment (IDE), enabling researchers to perform the entire Machine Learning Potential (MLP) development cycle consisting of (i) creating systematic DFT databases, (ii) fitting the Density Functional Theory (DFT) data to empirical potentials or MLPs, and (iii) validating the potentials in a largely automatic approach. The power and performance of this framework are demonstrated for three conceptually very different classes of interatomic potentials: an empirical potential (embedded atom method - EAM), neural networks (high-dimensional neural network potentials - HDNNP) and expansions in basis sets (atomic cluster expansion - ACE). As an advanced example for validation and application, we show the computation of a binary composition-temperature phase diagram for Al-Li, a technologically important lightweight alloy system with applications in the aerospace industry

The molecular machinery of myelin gene transcription in Schwann cells

During late fetal life, Schwann cells in the peripheral nerves, singled out by the larger axons will transit through a promyelinating stage before exiting the cell cycle and initiating myelin formation. A network of extra- and intracellular signaling pathways, regulating a transcriptional program of cell differentiation, governs this progression of cellular changes, culminating in a highly differentiated cell. In this review we focus on the roles of a number of transcription factors not only in myelination, during normal development, but also in demyelination, following nerve trauma. These factors include specification factors involved in early development of Schwann cells from neural crest (Sox10) as well as factors specifically required for transitions into the promyelinating and myelinating stages (Oct6/Scip and Krox20/Egr2). From this description we can glean the first, still very incomplete, contours of a gene regulatory network that governs myelination and demyelination during development and regeneration

Atomistic modelling of crystalline and amorphous Cu-Zr and Si-O-C using machine learning interatomic potentials

Modelling and simulation on the atomic scale play a pivotal role for the understanding of complex materials. In this field, machine learning interatomic potentials (MLIPs) are rapidly evolving tools, which allow the description of interatomic interactions with an accuracy approaching that of quantum mechanical methods. At the same time, they are computationally much more efficient, opening the possibility for large-scale molecular dynamics (MD) simulations with unprecedented fidelity. However, current research in the field is often focused on methodical advancements and uses simple single element test cases for this purpose. This thesis treats the development and application of MLIPs, more specifically highly efficient Atomic Cluster Expansion potentials (ACEPs), for structurally and chemically complex systems, namely Cu-Zr and silicon oxycarbide (Si-O-C). Both are representatives of important material classes, metals and glass-ceramics. Cu-Zr has a plethora of intermetallic phases and is a well known metallic glass (MG) former. The performance of the developed potential is compared to previously published classical potentials and experimental data. Using the new MLIP, the concentration-temperature phase diagram of the material is calculated and found to be in good agreement with experiments. Furthermore, the MG structure is investigated, revealing a massively different short-range order compared to classical interatomic potentials (IPs), and tensile tests of a glass-crystal matrix sample show the occurrence of martensitic phase transitions in B2-CuZr. Si-O-C has a highly tunable composition and microstructure. Consequently, training data for this material needs to cover a wide configuration space, which is achieved with an active learning strategy based on structural units present in the bulk material. The developed ACEP is the first publicly available IP for the system and employed to investigate the atomistic structure and its relation to the elastic properties. Contrary to common assumptions, graphite agglomerates in the system are of low importance for the Young’s modulus. Instead, strong correlation to SiO4 tetrahedra and SiC bonds are found. Finally, different types of MLIPs are evaluated. During the work on Cu-Zr and Si-O-C equivariant structure descriptions and message-passing graph neural networks emerged as promising methods to reach ever improving accuracies. Novel NequIP, Allegro and MACE MLIPs implementing them are compared to the well established High-Dimensional Neural Network Potentials (HDNNPs), Gaussian Approximation Potentials (GAPs), Moment Tensor Potentials (MTPs) and ACEPs. The tests reveal the large data requirements for HDNNPs and emphasize the tradeoff between achievable accuracies and computational cost. ACEPs still represent a good compromise in this regard

Potential and training data for 'Structure-property relations of silicon oxycarbides studied using a machine learning interatomic potential'

<p>Fitted potential, training and testing data.</p&gt

General purpose potential for glassy and crystalline phases of Cu-Zr alloys based on the ACE formalism

A general purpose machine-learning interatomic potential (MLIP) for the Cu-Zr system is presented based on the atomic cluster expansion formalism [R. Drautz, Phys. Rev. B 99, 014104 (2019)]. By using an extensive set of Cu-Zr training data generated withdensity functional theory, this potential describes a wide range of properties of crystalline as well as amorphous phases within the whole compositional range. Therefore, the machine learning interatomic potential (MLIP) can reproduce the experimental phase diagram and amorphous structure with considerably improved accuracy. A massively different short-range order compared to classica interatomic potentials is found in glassy Cu-Zr samples, shedding light on the role of the full icosahedral motif in the material. Tensile tests of B2-CuZr inclusions in an Cu50⁢Zr50 amorphous matrix reveal the occurrence of martensitic phase transformations in this crystal-glass nanocomposite

Structure-property relations of silicon oxycarbides studied using a machine learning interatomic potential

Silicon oxycarbides show outstanding versatility due to their highly tunable composition and microstructure. Consequently, a key challenge is a thorough knowledge of structure-property relations in the system. In this work, we fit an atomic cluster expansion potential to a set of actively learned DFT training data spanning a wide configurational space. We demonstrate the ability of the potential to produce realistic amorphous structures and rationalize the formation of different morphologies of the turbostratic free carbon phase. Finally, we relate the materials stiffness to its composition and microstructure, finding a delicate dependence on Si-C bonds that contradicts commonly assumed relations to the free carbon phase