Experimental procedures for chemical synthesis are commonly reported in prose in patents or in the scientific literature. The extraction of the details necessary to reproduce and validate a synthesis in a chemical laboratory is often a tedious task requiring extensive human intervention. We present a method to convert unstructured experimental procedures written in English to structured synthetic steps (action sequences) reflecting all the operations needed to successfully conduct the corresponding chemical reactions. To achieve this, we design a set of synthesis actions with predefined properties and a deep-learning sequence to sequence model based on the transformer architecture to convert experimental procedures to action sequences. The model is pretrained on vast amounts of data generated automatically with a custom rule-based natural language processing approach and refined on manually annotated samples. Predictions on our test set result in a perfect (100%) match of the action sequence for 60.8% of sentences, a 90% match for 71.3% of sentences, and a 75% match for 82.4% of sentences.

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We describe here in detail the recently introduced methodology for simulation of structural transitions in crystals. The applications of the new scheme are illustrated on various kinds of crystals and the advantages with respect to previous schemes are emphasized. The relevance of the new method for the problem of crystal structure prediction is also discussed.

Graphene oxide (GO) resistive memories offer the promise of low-cost environmentally sustainable fabrication, high mechanical flexibility and high optical transparency, making them ideally suited to future flexible and transparent electronics applications. However, the dimensional and temporal scalability of GO memories, i.e., how small they can be made and how fast they can be switched, is an area that has received scant attention. Moreover, a plethora of GO resistive switching characteristics and mechanisms has been reported in the literature, sometimes leading to a confusing and conflicting picture. Consequently, the potential for graphene oxide to deliver high-performance memories operating on nanometer length and nanosecond time scales is currently unknown. Here we address such shortcomings, presenting not only the smallest (50 nm), fastest (sub-5 ns), thinnest (8 nm) GO-based memory devices produced to date, but also demonstrate that our approach provides easily accessible multilevel (4-level, 2-bit per cell) storage capabilities along with excellent endurance and retention performance-all on both rigid and flexible substrates. Via comprehensive experimental characterizations backed-up by detailed atomistic simulations, we also show that the resistive switching mechanism in our Pt/GO/Ti/Pt devices is driven by redox reactions in the interfacial region between the top (Ti) electrode and the GO layer.

Recent first-principles calculations by Raty et al., [25] Gabardi et al., [26] and Zipoli et al. [22] on the prototypical phase-change material GeTe provide significant insights into the microscopic Phase-change memory devices are expected to play a key role in future computing systems as both memory and computing elements. A key challenge in this respect is the temporal evolution of the resistance levels commonly referred to as "resistance drift." In this paper, a comprehensive description of resistance drift as a result of spontaneous structural relaxation of the amorphous phase-change material toward an energetically more favorable ideal glass state is presented. Molecular dynamics simulations provide insights into the microscopic origin of the structural relaxation. Based on those insights, a collective relaxation model is proposed to capture the kinetics of structural relaxation. By linking the physical material parameters governing electrical transport to such a description of structural relaxation, an integrated drift model that is able to predict the current-voltage characteristics at any instance in time even during nontrivial temperature treatments is obtained. Accurate quantitative matching with experimental drift measurements over a wide range of time (10 decades) and temperature (160-420 K) is demonstrated.

The possibility of using the active site, the [FeFe](H) cluster, of the bacterial di-iron hydrogenases as a catalyst for hydrogen production from water by electro- or photocatalysis is of current scientific and technological interest. We present here a theoretical study of hydrogen production by a modified [FeFe](H) cluster stably linked to a pyrite electrode immersed in acidified water. We employed state-of-the-art electronic-structure and first-principles molecular-dynamics methods. We found that a stable sulfur link of the cluster to the surface analogous to that linking the cluster to its enzyme environment cannot be made. However, we have discovered a modification of the cluster which does form a stable, tridentate link to the surface. The pyrite electrode readily produces hydrogen from acidified water when functionalized with the modified cluster, which remains stable throughout the hydrogen production cycle.

We explored the reactivity of the active center of the [FeFe]-hydrogenases detached from the enzyme and immersed in acidified water by first-principles Car-Parrinello molecular-dynamics simulations. We focused on the identification of the structures that are stable and metastable in acidified water and on their activity for hydrogen production. Our calculations revealed that the naked active center could be an efficient catalyst provided that electrons are transferred to the cluster. We found that both bridging and terminal isomers are present at equilibrium and that the bridging configuration is essential for efficient hydrogen production. The formation of the hydrogen molecule occurs via sequential protonations of the distal iron and of the N-atom of the S-CH(2)-NH-CH(2)-S chelating group. H(2) desorption does not involve a significant energy barrier, making the process very efficient at room temperature. We established that the bottleneck in the reaction is the direct proton transfer from water to the vacant site of the distal iron. Moreover, we found that even if the terminal isomer is present at the equilibrium, its strong local hydrophobicity prevents poisoning of the cluster.

<div> <div> <div> <p>Experimental procedures for chemical synthesis are commonly reported in prose in patents or in the scientific literature. The automatic extraction of the details necessary to reproduce and validate a synthesis in a chemical laboratory is quite often a tedious task, requiring extensive human intervention. We present a method to convert unstructured experimental procedures written in English to structured synthetic steps (action sequences) reflecting all the operations needed to successfully conduct the corresponding chemical reactions. To achieve this, we design a set of synthesis actions with predefined properties and a deep-learning sequence to sequence model based on the transformer architecture to convert experimental procedures to action sequences. The model is pretrained on vast amounts of data generated automatically with a custom rule-based natural language processing approach and refined on a smaller set of manually annotated samples. Predictions on our test set resulted in a perfect (100%) match of the action sequence for 60.8% of sentences, a 90% match for 71.3% of sentences, and a 75% match for 82.4% of sentences. </p> </div> </div> </div>