The ScholarNet and Artificial Intelligence (AI) Supervisor in Material Science Research.

The past decade has witnessed extensive applications of artificial intelligence (AI) and robotics in chemistry and material science. However, the current focus mainly revolves around idea execution, neglecting the significance of idea generation, which plays a pivotal role in determining research novelty and potential breakthroughs. Concurrently, the exponential growth of scientific publications has resulted in overpublishing, making it challenging for researchers to grasp multiple fields effectively.

Tensor Network-Encrypted Physical Anti-counterfeiting Passport for Digital Twin Authentication.

The trend of digitalization has produced rapidly increasing data interaction and authentication demand in today's internet of things ecosystem. To face the challenge, we demonstrated a micro-scale label by direct laser writing to perform as a passport between the physical and digital worlds. On this label, the user information is encrypted into three-dimensional geometric structures by a tensor network and then authenticated through the decryption system based on computer vision.

Catalyst deep neural networks (Cat-DNNs) in singlet fission property prediction.

Many current deep neural network (DNN) models only focus on straightforward optimization over the given database. However, most numerical fitting procedures depart from physical laws. By introducing the concept of "catalysis" from physical chemistry, we propose that the physical correlations among molecular properties could spontaneously act as a catalyst in the DNNs, which increases the accuracy, and more importantly, guides the DNNs in the right way.

QM-symex, update of the QM-sym database with excited state information for 173 kilo molecules.

In the research field of material science, quantum chemistry database plays an indispensable role in determining the structure and properties of new material molecules and in deep learning in this field. A new quantum chemistry database, the QM-sym, has been set up in our previous work. The QM-sym is an open-access database focusing on transition states, energy, and orbital symmetry.

Symmetrical Graph Neural Network for Quantum Chemistry with Dual Real and Momenta Space.

Most of the current neural network models in quantum chemistry (QC) exclude the molecular symmetry and separate the well-correlated real space ( space) and momenta space ( space) into two individuals, which lack the essential physics in molecular chemistry. In this work, by endorsing the molecular symmetry and elementals of group theory, we propose a comprehendible method to apply symmetry in the graph neural network (SY-GNN), which extends the property-predicting coverage to orbital symmetry for both ground and excited states. SY-GNN is an end-to-end model that can predict multiple properties in both and space within a single model, and it shows excellent performance in predicting both the absolute and relative and space quantities.

AIR-Chem: Authentic Intelligent Robotics for Chemistry.

The new era with prosperous artificial intelligence (AI) and robotics technology is reshaping the materials discovery process in a more radical fashion. Here we present authentic intelligent robotics for chemistry (AIR-Chem), integrated with technological innovations in the AI and robotics fields, functionalized with modules including gradient descent-based optimization frameworks, multiple external field modulations, a real-time computer vision (CV) system, and automated guided vehicle (AGV) parts. AIR-Chem is portable and remotely controllable by cloud computing.