RESUMO
Large-language models (LLMs) such as GPT-4 caught the interest of many scientists. Recent studies suggested that these models could be useful in chemistry and materials science. To explore these possibilities, we organized a hackathon. This article chronicles the projects built as part of this hackathon. Participants employed LLMs for various applications, including predicting properties of molecules and materials, designing novel interfaces for tools, extracting knowledge from unstructured data, and developing new educational applications. The diverse topics and the fact that working prototypes could be generated in less than two days highlight that LLMs will profoundly impact the future of our fields. The rich collection of ideas and projects also indicates that the applications of LLMs are not limited to materials science and chemistry but offer potential benefits to a wide range of scientific disciplines.
RESUMO
In molecular electronic conduction, exotic lattice morphologies often give rise to exotic behaviors. Among 2D systems, graphene is a notable example. Recently, a stable amorphous version of graphene called monolayer amorphous carbon (MAC) was synthesized. MAC poses a new set of questions regarding the effects of disorder on conduction. In this Letter, we perform an ensemble-level computational analysis of the coherent electronic transmission through MAC nanofragments in search of defining characteristics. Our analysis, relying on a semiempirical Hamiltonian (Pariser-Parr-Pople) and Landauer theory, showed that states near the Fermi energy (EF) in MAC inherit partial characteristics of analogous surface states in graphene nanofragments. Away from EF, current is carried by a set of delocalized states that transition into a subset of insulating interior states at the extreme portions of MAC's energy spectrum. Finally, we also found that quantum interference between frontier orbitals is a common feature among MAC nanofragments.
RESUMO
Monolayer amorphous carbon (MAC) is a recently synthesized disordered 2D carbon material. An ensemble of MAC nanofragments contains diverse manifestations of lattice disorder, and because of disorder the key unifying characteristic of this ensemble is poor electronic conductance. Curiously, our computational analysis of the electronic properties of MAC nanofragments revealed an additional commonality: a robust presence of charge-transfer character for electronic transitions from the occupied to virtual orbitals. This charge-transfer property suggests possible applications in optoelectronics. In this Letter, we demonstrate computationally that a laser pulse induces directional electronic currents in unbiased MAC nanojunctions and discuss the effects of pulse intensity on the magnitude of electron transfer.
RESUMO
Amorphous molecular assemblies appear in a vast array of systems: from living cells to chemical plants and from everyday items to new devices. The absence of long-range order in amorphous materials implies that precise knowledge of their underlying structures throughout is needed to rationalize and control their properties at the mesoscale. Standard computational simulations suffer from exponentially unfavorable scaling of the required compute with system size. We present a method based on deep learning that leverages the finite range of structural correlations for an autoregressive generation of disordered molecular aggregates up to arbitrary size from small-scale computational or experimental samples. We benchmark performance on self-assembled nanoparticle aggregates and proceed to simulate monolayer amorphous carbon with atomistic resolution. This method bridges the gap between the nanoscale and mesoscale simulations of amorphous molecular systems.