What indeed can GPT models do in chemistry? A comprehensive benchmark on eight tasks

Large Language Models (LLMs) with strong abilities in natural language processing tasks have emerged and have been rapidly applied in various kinds of areas such as science, finance and software engineering. However, the capability of LLMs to advance the field of chemistry remains unclear. In this paper,we establish a comprehensive benchmark containing 8 practical chemistry tasks, including 1) name prediction, 2) property prediction, 3) yield prediction, 4) reaction prediction, 5) retrosynthesis (prediction of reactants from products), 6)text-based molecule design, 7) molecule captioning, and 8) reagent selection. Our analysis draws on widely recognized datasets including BBBP, Tox21, PubChem, USPTO, and ChEBI, facilitating a broad exploration of the capacities of LLMs within the context of practical chemistry. Three GPT models (GPT-4, GPT-3.5,and Davinci-003) are evaluated for each chemistry task in zero-shot and few-shot in-context learning settings with carefully selected demonstration examples and specially crafted prompts. The key results of our investigation are 1) GPT-4 outperforms the other two models among the three evaluated; 2) GPT models exhibit less competitive performance in tasks demanding precise understanding of molecular SMILES representation, such as reaction prediction and retrosynthesis;3) GPT models demonstrate strong capabilities in text-related explanation tasks such as molecule captioning; and 4) GPT models exhibit comparable or better performance to classical machine learning models when applied to chemical problems that can be transformed into classification or ranking tasks, such as property prediction, and yield prediction

Graph-based Molecular Representation Learning

Molecular representation learning (MRL) is a key step to build the connection between machine learning and chemical science. In particular, it encodes molecules as numerical vectors preserving the molecular structures and features, on top of which the downstream tasks (e.g., property prediction) can be performed. Recently, MRL has achieved considerable progress, especially in methods based on deep molecular graph learning. In this survey, we systematically review these graph-based molecular representation techniques, especially the methods incorporating chemical domain knowledge. Specifically, we first introduce the features of 2D and 3D molecular graphs. Then we summarize and categorize MRL methods into three groups based on their input. Furthermore, we discuss some typical chemical applications supported by MRL. To facilitate studies in this fast-developing area, we also list the benchmarks and commonly used datasets in the paper. Finally, we share our thoughts on future research directions

The Variability and Evaluation Method of Recycled Concrete Aggregate Properties

With the same sources and regeneration techniques, given RA’s properties may display large variations. The same single property index of different sets maybe has a large difference of the whole property. How shall we accurately evaluate the whole property of RA? 8 groups of RAs from pavement and building were used to research the method of evaluating the holistic characteristics of RA. After testing and investigating, the parameters of aggregates were analyzed. The data of physical and mechanical properties show a distinct dispersion and instability; thus, it has been difficult to express the whole characteristics in any single property parameter. The Euclidean distance can express the similarity of samples. The closer the distance, the more similar the property. The standard variance of the whole property Euclidean distances for two types of RA is Sk=7.341 and Sk=2.208, respectively, which shows that the property of building RA has great fluctuation, while pavement RA is more stable. There are certain correlations among the apparent density, water absorption, and crushed value of RAs, and the Mahalanobis distance method can directly evaluate the whole property by using its parameters: mean, variance, and covariance, and it can provide a grade evaluation model for RAs

Research on a Conflict Early Warning System Based on the Active Safety Concept

In order to reduce traffic conflicts on cross-intensive roads, this paper proposes a new early warning system based on the active safety concept. The system collects real-time vehicle data using roadside sensors and transmits the results to drivers on the major road in a timely manner via roadside warning lights. In this research, the principles of the warning system are discussed in detail, including how the vehicle dynamics data are collected and how potential collisions are identified and avoided. Through a driving simulation experiment, the speed prediction model after implementation of the warning system was examined. Results indicated that it can accurately identify the vehicle operating status, accurately guide driving behavior, and effectively reduce traffic conflict. To verify the reliability of the proposed warning logic and algorithm, numerical simulations were carried out via CarSim/Simulink cosimulation. The simulation results indicate that the proposed system enables drivers to perceive conflicting vehicles in advance, avoid the sudden braking phenomenon, and ensure safe driving

Modulating the tumor microenvironment with new therapeutic nanoparticles: a promising paradigm for tumor treatment

To better make nanomedicine entering the clinic, developing new rationally designed nanotherapeutics with a deeper understanding of tumor biology is required. The tumor microenvironment is similar to the inflammatory response in a healing wound, the milieu of which promotes tumor cell invasion and metastasis. Successful targeting of the microenvironmental components with effective nanotherapeutics to modulate the tumor microvessels or restore the homeostatic mechanisms in the tumor stroma will offer new hope for cancer treatment. We here highlight the progress in constructing nanotherapeutics to target or modulate the tumor microenvironment. We discuss the factors necessary for nanomedicines to become a new paradigm in cancer therapy, including the selection of drugs and therapeutic targets, controllable synthesis, and tempo-spatial drug release

Recovery facilitated by interphase boundary motion circumvents recrystallization in superalloy single crystals

Dislocation recovery lowering the driving force for recrystallization would be able to suppress the latter in Ni-based superalloy single crystals, but was believed unlikely due to their low stacking-fault energy. Defying this traditional wisdom, here we show that efficient recovery can be realized once the γ′-precipitates start to dissolve. Our microscopy evidence tracking the distribution/configuration of dislocations reveals that the shifting γ/γ′ interphase boundaries release the dislocations trapped there, facilitating their annihilation and rearrangement into low-energy network configurations. Our finding explains the success of a recent recovery protocol that kept superalloys as single crystals after supersolvus homogenization heat treatment

Effect of local chemical order on the irradiation-induced defect evolution in CrCoNi medium-entropy alloy.

High- (and medium-) entropy alloys have emerged as potentially suitable structural materials for nuclear applications, particularly as they appear to show promising irradiation resistance. Recent studies have provided evidence of the presence of local chemical order (LCO) as a salient feature of these complex concentrated solid-solution alloys. However, the influence of such LCO on their irradiation response has remained uncertain thus far. In this work, we combine ion irradiation experiments with large-scale atomistic simulations to reveal that the presence of chemical short-range order, developed as an early stage of LCO, slows down the formation and evolution of point defects in the equiatomic medium-entropy alloy CrCoNi during irradiation. In particular, the irradiation-induced vacancies and interstitials exhibit a smaller difference in their mobility, arising from a stronger effect of LCO in localizing interstitial diffusion. This effect promotes their recombination as the LCO serves to tune the migration energy barriers of these point defects, thereby delaying the initiation of damage. These findings imply that local chemical ordering may provide a variable in the design space to enhance the resistance of multi-principal element alloys to irradiation damage

Chemical inhomogeneity–induced profuse nanotwinning and phase transformation in AuCu nanowires

Abstract Nanosized metals usually exhibit ultrahigh strength but suffer from low homogeneous plasticity. The origin of a strength–ductility trade-off has been well studied for pure metals, but not for random solid solution (RSS) alloys. How RSS alloys accommodate plasticity and whether they can achieve synergy between high strength and superplasticity has remained unresolved. Here, we show that face-centered cubic (FCC) RSS AuCu alloy nanowires (NWs) exhibit superplasticity of ~260% and ultrahigh strength of ~6 GPa, overcoming the trade-off between strength and ductility. These excellent properties originate from profuse hexagonal close-packed (HCP) phase generation (2H and 4H phases), recurrence of reversible FCC-HCP phase transition, and zigzag-like nanotwin generation, which has rarely been reported before. Such a mechanism stems from the inherent chemical inhomogeneity, which leads to widely distributed and overlapping energy barriers for the concurrent activation of multiple plasticity mechanisms. This naturally implies a similar deformation behavior for other highly concentrated solid-solution alloys with multiple principal elements, such as high/medium-entropy alloys. Our findings shed light on the effect of chemical inhomogeneity on the plastic deformation mechanism of solid-solution alloys