In the past decade, there has been a significant interest in the use of machine learning approaches in materials science research. Conventional deep learning approaches that rely on complex, nonlinear models have become increasingly important in computational materials science due to their high predictive accuracy. In contrast to these approaches, we have shown in a recent work that a remarkably simple learned heuristic rule -- based on the concept of topogivity -- can classify whether a material is topological using only its chemical composition. In this paper, we go beyond the topology classification scenario by also studying the use of machine learning to develop simple heuristic rules for classifying whether a material is a metal based on chemical composition. Moreover, we present a framework for incorporating chemistry-informed inductive bias based on the structure of the periodic table. For both the topology classification and the metallicity classification tasks, we empirically characterize the performance of simple heuristic rules fit with and without chemistry-informed inductive bias across a wide range of training set sizes. We find evidence that incorporating chemistry-informed inductive bias can reduce the amount of training data required to reach a given level of test accuracy.

Topological materials occupy a frontier in condensed-matter physics thanks to their remarkable electronic and quantum properties, yet their cross-scale design remains bottlenecked by inefficient discovery workflows. Here, we introduce TopoMAS (Topological materials Multi-Agent System), an interactive human-AI framework that seamlessly orchestrates the entire materials-discovery pipeline: from user-defined queries and multi-source data retrieval, through theoretical inference and crystal-structure generation, to first-principles validation. Crucially, TopoMAS closes the loop by autonomously integrating computational outcomes into a dynamic knowledge graph, enabling continuous knowledge refinement. In collaboration with human experts, it has already guided the identification of novel topological phases SrSbO3, confirmed by first-principles calculations. Comprehensive benchmarks demonstrate robust adaptability across base Large Language Model, with the lightweight Qwen2.5-72B model achieving 94.55% accuracy while consuming only 74.3-78.4% of tokens required by Qwen3-235B and 83.0% of DeepSeek-V3's usage--delivering responses twice as fast as Qwen3-235B. This efficiency establishes TopoMAS as an accelerator for computation-driven discovery pipelines. By harmonizing rational agent orchestration with a self-evolving knowledge graph, our framework not only delivers immediate advances in topological materials but also establishes a transferable, extensible paradigm for materials-science domain.

However, such materials, particularly those with a full band gap, remain scarce. Given the limitations of traditional approaches that scan known materials for candidates, we focus on the generation of new topological materials through a generative model. Specifically, we apply reinforcement fine-tuning (ReFT) to a pre-trained generative model, thereby aligning the model's objectives with our material design goals. We demonstrate that ReFT is effective in enhancing the model's ability to generate TIs and TCIs, with minimal compromise on the stability of the generated materials. Using the fine-tuned model, we successfully identify a large number of new topological materials, with Ge 2Bi 2O 6 serving as a representative example--a TI with a full band gap of 0.26 eV, ranking among the largest known in this category. Topological materials, including topological insulators (TIs), topological crystalline insulators (TCIs), and topological semimetals (TSMs), represent a fascinating and expansive class of materials whose electronic properties are fundamentally governed by the topology of their electronic bands [1-16]. In particular, TIs [6] and TCIs [8] that feature a full energy gap at the Fermi energy exhibit insulating bulk states and distinct surface or edge states, which are robust against perturbations such as impurities, defects, and disorder. These materials thus hold substantial promise for next-generation technologies, including quantum computing, spintronics, and energy-efficient electronics [2]. Despite over a decade of intensive research on TIs and TCIs, and the discovery of several material systems exhibiting these phases, the number of TIs and TCIs--particularly those with a full bulk gap--remains markedly limited. Consequently, the discovery and identification of real-world materials exhibiting these topological properties continue to represent a critical and ongoing challenge within the field.

The use of machine learning methods for predicting the properties of crystalline materials encounters significant challenges, primarily related to input encoding, output versatility, and interpretability. Here, we introduce CrystalBERT, an adaptable transformer-based framework with novel structure that integrates space group, elemental, and unit cell information. The method's adaptability lies not only in its ability to seamlessly combine diverse features but also in its capability to accurately predict a wide range of physically important properties, including topological properties, superconducting transition temperatures, dielectric constants, and more. CrystalBERT also provides insightful physical interpretations regarding the features that most significantly influence the target properties. Our findings indicate that space group and elemental information are more important for predicting topological and superconducting properties, in contrast to some properties that primarily depend on the unit cell information. This underscores the intricate nature of topological and superconducting properties. By incorporating all these features, we achieve a high accuracy of 91% in topological classification, surpassing prior studies and identifying previously misclassified topological materials, further demonstrating the effectiveness of our model.

Prediction and discovery of new materials with desired properties are at the forefront of quantum science and technology research. A major bottleneck in this field is the computational resources and time complexity related to finding new materials from ab initio calculations. In this work, an effective and robust deep learning-based model is proposed by incorporating persistent homology and graph neural network which offers an accuracy of 91.4% and an F1 score of 88.5% in classifying topological vs. non-topological materials, outperforming the other state-of-the-art classifier models. The incorporation of the graph neural network encodes the underlying relation between the atoms into the model based on their own crystalline structures and thus proved to be an effective method to represent and process non-euclidean data like molecules with a relatively shallow network. The persistent homology pipeline in the suggested neural network is capable of integrating the atom-specific topological information into the deep learning model, increasing robustness, and gain in performance. It is believed that the presented work will be an efficacious tool for predicting the topological class and therefore enable the high-throughput search for novel materials in this field.

Topological materials present unconventional electronic properties that make them attractive for both basic science and next-generation technological applications. The majority of currently known topological materials have been discovered using methods that involve symmetry-based analysis of the quantum wavefunction. Here we use machine learning to develop a simple-to-use heuristic chemical rule that diagnoses with a high accuracy whether a material is topological using only its chemical formula. This heuristic rule is based on a notion that we term topogivity, a machine-learned numerical value for each element that loosely captures its tendency to form topological materials. We next implement a high-throughput procedure for discovering topological materials based on the heuristic topogivity-rule prediction followed by ab initio validation. This way, we discover new topological materials that are not diagnosable using symmetry indicators, including several that may be promising for experimental observation.

MIT researchers discovered hidden magnetic properties in multi-layered electronic material by analyzing polarized neutrons using neural networks. An MIT team incorporates AI to facilitate the detection of an intriguing materials phenomenon that can lead to electronics without energy dissipation. Superconductors have long been considered the principal approach for realizing electronics without resistivity. In the past decade, a new family of quantum materials, "topological materials," has offered an alternative but promising means for achieving electronics without energy dissipation (or loss). Compared to superconductors, topological materials provide a few advantages, such as robustness against disturbances.

Superconductors have long been considered the principal approach for realizing electronics without resistivity. In the past decade, a new family of quantum materials, "topological materials," has offered an alternative but promising means for achieving electronics without energy dissipation (or loss). Compared to superconductors, topological materials provide a few advantages, such as robustness against disturbances. To attain the dissipationless electronic states, one key route is the so-called "magnetic proximity effect," which occurs when magnetism penetrates slightly into the surface of a topological material. However, observing the proximity effect has been challenging.

Superconductors have long been considered the principal approach for realizing electronics without resistivity. In the past decade, a new family of quantum materials, "topological materials," has offered an alternative but promising means for achieving electronics without energy dissipation (or loss). Compared to superconductors, topological materials provide a few advantages, such as robustness against disturbances. To attain the dissipationless electronic states, one key route is the so-called "magnetic proximity effect," which occurs when magnetism penetrates slightly into the surface of a topological material. However, observing the proximity effect has been challenging.