Addressing the Accuracy-Cost Tradeoff in Material Property Prediction: A Teacher-Student Strategy

Deep learning has revolutionized the process of new material discovery, with state-of-the-art models now able to predict material properties based solely on chemical compositions, thus eliminating the necessity for material structures. However, this cost-effective method has led to a trade-off in model accuracy. Specifically, the accuracy of Chemical Composition-based Property Prediction Models (CPMs) significantly lags behind that of Structure-based Property Prediction Models (SPMs). To tackle this challenge, we propose an innovative Teacher-Student (T-S) strategy, where a pre-trained SPM serves as the 'teacher' to enhance the accuracy of the CPM. Leveraging the T-S strategy, T-S CrabNet has risen to become the most accurate model among current CPMs. Initially, we demonstrated the universality of this strategy. On the Materials Project (MP) and Jarvis datasets, we validated the effectiveness of the T-S strategy in boosting the accuracy of CPMs with two distinct network structures, namely CrabNet and Roost. This led to CrabNet, under the guidance of the T-S strategy, emerging as the most accurate model among the current CPMs. Moreover, this strategy shows remarkable efficacy in small datasets. When predicting the formation energy on a small MP dataset comprising merely 5% of the samples, the T-S strategy boosted CrabNet's accuracy by 37.1%, exceeding the enhancement effect of the T-S strategy on the whole dataset

Performance of Silicon Nanowire Solar Cells with Phosphorus-Diffused Emitters

Vertical silicon nanowire (Si NW) arrays on a Si (100) substrate have been prepared by using a low-cost and facile Ag-assisted chemical etching technique. The reflectance of Si NW arrays is very low (<1%) in the spectral range from 400 to 1000 nm. By phosphorus diffusion into Si NW arrays to fabricate solar cells, the power conversion efficiency of 8.84% has been achieved. This power conversion efficiency is much higher than that of the planar cell with the similar celling technology. It is found that the efficiency of Si NW solar cells is intimately associated with their excellent antireflection property. The surface recombination of Si NWs is the main obstacle for the improvement of solar cell efficiency. The current results are helpful to the advancement of the application of Si NWs in photovoltaics

Transesterification Kinetics of Jatropha Methyl Ester and Trimethylol propane for Biolubricant Synthesis Using Paphiaundulata Shell Waste

Indium tin oxide (ITO) thin films of 150 nm thickness were deposited on quartz glass substrates by RF sputtering technique, followed by thermal annealing treatment. In this technique, the samples have been annealed at different temperature, 300ᴼC, 400ᴼC, 500ᴼC respectively in Argon gas flow. Structural and surface morphological properties were analyzed by X-ray diffraction (XRD) and Atomic Force Microscopy (AFM) after annealing. The XRD showed a polycrystalline structure of ITO film with maximum peak intensity at 2θ= 30.54, orientation without any change in the cubic structure. Continuous and homogeneous films obtained by the AFM after annealing treatment. The visible spectrum from the spectrophotometer showed high transparency between 81% and 95% in the. Increasing the annealing temperature yields evenly distributed pyramidal peaks shaped particles with low roughness. Resistance of ITO thin film was significantly improved from 8.75 kΩ to 1.96 kΩ after 10 minute from 300ᴼC to 500ᴼC annealing temperatures respectively under Argon gas flow. ITO films physical properties would be well improved by this method which is highly suitable for cost effective photonic devices

Analysis of Pollution in Dianchi Lake and Consideration of Its Application in Crop Planting

AbstractAfter investigating the distribution and composition of N-cycle-related bacteria of different sites and different depth of Dianchi sediment, we analyzed the longitudinal distribution, lateral distribution of N, its transportation and transformation in Dianchi sediment, as well as the involvement of these bacteria in nitrogen cycle. Conclusion was drawn as follows, (1) Azotobateria could be effectively used as indicative strains to track the changes of Dianchi pollution because the distribution of Azotobateria can not only indicate N contamination but also P enrichment, (2) ammoniate and nitrite is mainly existed in top sediment of Dianchi Lake while other forms of nitrogen mainly in deeper sediment, (3) due to the fact that Dianchi is rich in P, together with the mutual promotion between N pollution and P pollution, the pollution of south part will worsen rapidly, (4) if the south part is also polluted badly, the pollution distribution will appear as peaking at both ends (north and south), and the pollution will definitely extend toward the middle, and finally Dianchi Lake will totally be seriously polluted. Combining with the fact that 40% of Dianchi pollution was caused by abusive use of chemical fertilizer, we put forward the idea of “changing pollutants into things of value”, which could be specified as “using the sediment as agricultural fertilizer”. Such method can solve the problem of internal pollution, and what's more, it can develop agriculture, while cut down the use of chemical fertilizer and thus reduce relative pollution source

Towards Accelerating Particle-Resolved Direct Numerical Simulation with Neural Operators

We present our ongoing work aimed at accelerating a particle-resolved direct numerical simulation model designed to study aerosol-cloud-turbulence interactions. The dynamical model consists of two main components - a set of fluid dynamics equations for air velocity, temperature, and humidity, coupled with a set of equations for particle (i.e., cloud droplet) tracing. Rather than attempting to replace the original numerical solution method in its entirety with a machine learning (ML) method, we consider developing a hybrid approach. We exploit the potential of neural operator learning to yield fast and accurate surrogate models and, in this study, develop such surrogates for the velocity and vorticity fields. We discuss results from numerical experiments designed to assess the performance of ML architectures under consideration as well as their suitability for capturing the behavior of relevant dynamical systems