CrysXPP: An explainable property predictor for crystalline materials

Visualizations for an Explainable Planning Agent

In this paper, we report on the visualization capabilities of an Explainable AI Planning (XAIP) agent that can support human in the loop decision making. Imposing transparency and explainability requirements on such agents is especially important in order to establish trust and common ground with the end-to-end automated planning system. Visualizing the agent’s internal decision making processe...

Predictive analytics for crystalline materials: bulk modulus

The bulk modulus is one of the important parameters for designing advanced high-performance and thermoelectric materials. The current work is the first attempt to develop a generalized model for forecasting bulk moduli of various types of crystalline materials, based on ensemble predictive learning using a unique set of attributes. The attributes used are a combination of experimentally measure...

Phase transitions in crystalline materials

We reviewed structural phase transitions in crystalline materials, especially recent simulations and experiments on the shock-induced transitions in iron. They both confirmed a phase transition wave in the picosecond to nanosecond time scale, inferred previously from wave profile analyses. The nonequilibrium molecular dynamics simulation suggests modification of pair potentials from temperature...

Diffusion in porous crystalline materials.

The design and development of many separation and catalytic process technologies require a proper quantitative description of diffusion of mixtures of guest molecules within porous crystalline materials. This tutorial review presents a unified, phenomenological description of diffusion inside meso- and micro-porous structures. In meso-porous materials, with pore sizes 2 nm < d(p) < 50 nm, there...

Electronic Structure of Crystalline Materials