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. As most opportunities for innovation lie in interdisciplinary realms, there is a risk of missing out on the development of new ideas. To address these challenges, we present a deep learning-based AI supervisor trained on correlation-based ScholarNet data of publications. Primarily tailored for material science, this AI supervisor excels in recommending research ideas, analyzing their novelty, and providing comprehensive guidance to researchers. By offering invaluable support in idea generation and novelty assessment, our AI supervisor has emerged as a promising digital infrastructure for future material science research.

The Blockchain Integrated Automatic Experiment Platform (BiaeP).

Given that robots are being utilized extensively in chemical synthesis research, the potential applications of robots remain to be explored. Along with the remarkable progress of experimental science, circumstances have occurred in which publications were castigated because of irreproducibility, either because of rigorous experimental conditions or because of initial data forgery. Some credit-assignment issues and plagiarism cases also attracted intense attention throughout the community. As a possible solution to authenticity and originality problems, we herein propose a blockchain integrated automatic experiment platform, BiaeP, which attempts to provide solutions for those kinds of problems. As a result of the integration with blockchain, its data irreversibility secures the authenticity and the timestamp helps prove the originality. Two trial experiments are included as examples. We believe the architecture of BiaeP could be widely applicable for future development of scientific research in experimental subjects, such as chemistry, materials science, biology, and so forth.

QM-sym, a symmetrized quantum chemistry database of 135 kilo molecules.

Applying deep learning methods in materials science research is an important way of solving the time-consuming problems of typical ab initio quantum chemistry methodology, but due to the size of large molecules, large and uncharted fields still exist. Implementing symmetry information can significantly reduce the calculation complexity of structures, as they can be simplified to the minimum symmetric units. Because there are few quantum chemistry databases that include symmetry information, we constructed a new one, named QM-sym, by designing an algorithm to generate 135k organic molecules with the Cnh symmetry composite. Those generated molecules were optimized to a stable state using Gaussian 09. The geometric, electronic, energetic, and thermodynamic properties of the molecules were calculated, including their orbital degeneracy states and orbital symmetry around the HOMO-LUMO. The basic symmetric units were also included. This database p rovides consistent and comprehensive quantum chemical properties for structures with Cnh symmetries. QM-sym can be used as a benchmark for machine learning models in quantum chemistry or as a dataset for training new symmetry-based models.

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. AIR-Chem can learn the parametric procedures for given targets and carry on laboratory operations in standalone mode, with high reproducibility, precision, and availability for knowledge regeneration. Moreover, an improved nucleation theory of size focusing on inorganic perovskite quantum dots (IPQDs) is theoretically proposed and experimentally testified to by AIR-Chem. This work aims to boost the process of an unmanned chemistry laboratory from the synthesis of chemical materials to the analysis of physical chemical properties, and it provides a vivid demonstration for future chemistry reshaped by AI and robotics technology.

fvs-Si48: a direct bandgap silicon allotrope.

A structurally stable silicon allotrope is predicted by means of first principles calculations. This new structure is composed of a six-membered ring, a five-membered ring and a three-membered ring with the space group PA3[combining macron] and fvs topology, which is named fvs-Si48. The calculations of geometrical, vibrational, and electronic and optical properties reveal that fvs-Si48 has good mechanical stability with a mass density of 1.86 g cm-3. More importantly, it is a semiconductor with a direct band gap of 2.15 eV. From the analysis of its optical properties, there is the possibility of its synthesis in theory. This fvs-Si48 could have a wide range of applications in photo catalysts, optoelectronics, hydrogen storage and aerospace engineering.

TE-C36 carbon: a new semiconducting phase with an all-sp3 bonding network.

A new carbon allotrope is investigated by first principles calculations. The allotrope consists of 36 atoms in a tetragonal cell and displays P42/nmc symmetry (termed TE-C36 carbon) with a mass density of 3.18 g cm-3. The new carbon phase has an all-sp3 network, possessing squares, rhombuses, pentagons and hexagons formed by near-by atoms. The dynamic and mechanical stabilities are demonstrated by phonon dispersion and elastic constants, respectively. Its bulk modulus is 353 GPa. The analysis of its electronic band structure shows that it is a semiconductor possessing a direct band gap of 2.25 eV. X-ray diffraction patterns and Raman spectra are also simulated for future experimental characterization. Due to the direct band gap and a comparatively large bulk modulus, this new semiconducting carbon allotrope may possess not only potential electronic and optical applications but also mechanical application.