Recently, the team led by Liu Han from the College of Polymer Science and Engineering at Sichuan University has overcome the technical barriers to using computational simulations for material prediction applications.

At the same time, they have also addressed the difficulty that arises from the target users' unfamiliarity with computational physics programming languages, which makes it challenging for them to apply predictive tools in actual production or research and development processes.

In their research, they have developed a software called Lang2Sim, which makes it possible to simulate material properties through natural language descriptions.

To achieve this goal, the model is divided into three main modules: LM-Type (simulation type), LM-Sim (simulation performance), and LM-EXE (simulation parameters).

Among them, LM-Type integrates a variety of simulation tools ranging from macroscopic to microscopic, from empirical to theoretical. LM-Sim, on the other hand, selects the simulatable properties based on the chosen simulation tools, and

(Note: The text seems to be cut off at the end, so the translation is also cut off at the corresponding point.)Ultimately, based on the selected LM-Type and LM-Sim, LM-EXE will guide the user to input the parameters required for the simulation, and output the final results according to relevant literature.

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The aforementioned three modules sequentially constitute a complete prediction process. In the future, by integrating more simulation methods, it is expected that the number of generated results will exhibit an exponential growth similar to that of a decision tree.

In addition, the model's self-learning capability is also commendable. Through the reasonable arrangement of storage space, after each simulation, the model will record the simulation conditions.

When encountering similar situations, a secondary simulation can be quickly performed based on the previously recorded conditions, thereby accelerating the calculation process and avoiding the waste of computational resources.

In summary, this research has developed a human-computer interaction intelligent modeling system. By constructing a language intelligent body integrated system with a large language model, it can accurately convert natural language into material calculation models based on the history of human-computer interaction, providing a new tool for conveniently conducting material simulations.The anticipated application scenarios can be divided into two aspects:

1. In academic research, before conducting physical and chemical experiments, simulating the envisioned materials in advance can provide guidance for material development, such as making changes to the structure and composition, etc.;

2. In production applications, the model can predict the performance of current materials under specific conditions, providing a basis for whether the materials can play the required role under actual conditions.

Researchers have stated that this model can be regarded as the academic version of ChatGPT in the field of materials science.

In fact, the operation mode of Lang2Sim is very similar to that of ChatGPT. However, compared with ChatGPT, Lang2Sim has more explicit requirements for the information input by users, but at the same time, it also provides more accurate results than ChatGPT.Recently, a related paper titled "On Languaging a Simulation Engine" was published on arXiv[1], with Liu Han serving as the first author and corresponding author. In addition, graduate student Li Tianyi from the research group participated in this interview.

It is reported that the application of AI in materials science has always been the main research direction of the group, and they are currently committed to building a computational materials platform based on artificial intelligence.

Through this, they hope to achieve the full process simulation and reverse design of materials "preparation - structure - performance - application", thereby accurately predicting and reversely controlling the material preparation process.

Subsequently, they aim to quickly reduce the R&D cycle and cost of target materials, accelerate the development of high-performance new materials, and promote the development of related theoretical methodologies in practical applications.

At present, the team is still carrying out related work, such as the design of language intelligent agent cluster architecture based on large language models, and predictive models that can be used in the LM-Type, etc.Next, the research team plans to enrich each functional module through further research. In addition, they are actively collaborating with external research groups to apply the prediction results to experimental verification or experimental guidance.

Ultimately, the team hopes to promote the virtualization and intelligence of the material design laboratory, including the full automation of the entire process of material development, preparation, and characterization.

At present, the artificial intelligence-based fully automatic material development platform is profoundly transforming the entire scientific and technological field, with many opportunities and challenges.

For example, there is still a large gap in the fully automatic development assisted by artificial intelligence in polymer processing, which is also one of the key directions that academia needs to make up in the future.

In fact, every technological revolution has freed people from repetitive labor.

Please note that the original text provided is in Chinese, and the translation provided is in English as requested.For example:

The invention of the loom not only increased the efficiency of textile production but also improved the quality and output of fabrics. It spurred industrialization in other sectors and propelled the Industrial Revolution.

The invention of the computer revolutionized the scale of human computation, and its profound impact is still felt to this day.

Now, by endowing machines with the ability to think, we make computational simulation more accessible, fundamentally changing the existing paradigm of scientific research and promoting the development and production of materials to be more comprehensively intelligent, automated, and rational.