This article introduces two innovative deep reinforcement learning (RL) methodologies for inverse inorganic materials design, focusing on generating novel oxide compositions that satisfy both materials property (e.g., band gap, bulk modulus) and synthesis (e.g., sintering and calcination temperatures) objectives. These RL models, namely a policy gradient network (PGN) and a deep Q-network (DQN), leverage a feedback system combining material generation and property prediction. By learning chemical principles such as charge neutrality and formation energy stability, the models generate valid and diverse compositions. The study highlights the superiority of these RL approaches over traditional machine learning methods in balancing property optimization and chemical validity, offering a promising pathway for high-throughput materials discovery. Multi-objective optimization further allows fine-tuning of trade-offs between synthesis and property goals, accelerating the identification of compositions with desirable characteristics and plausible crystal structures for experimental validation.
For more details, please continue reading the full article under the following link:
https://www.nature.com/articles/s41524-024-01474-5
In general, if you enjoy reading this kind of scientific news articles, I am always keen to connect with fellow researchers in materials science, including the possibility to discuss about any potential interest in the Materials Square cloud-based online platform ( www.matsq.com ), designed for streamlining the execution of materials and molecular modelling simulations!
The Materials Square platform in fact provides extensive web-based functionalities for computational chemistry/materials research and training/education purposes, as detailed also in the following PDF brochure: https://www.materialssquare.com/wp-content/uploads/Materials_Square_Brochure_2022-compressed_1674181754.pdf
Many thanks for your interest and consideration,
Dr. Gabriele Mogni
Technical Consultant and EU Representative of Virtual Lab Inc., the parent company of the Materials Square platform
Website: Home | Virtual Lab Inc.
Email: gabriele@simulation.re.kr
#materials #materialsscience #materialsengineering #computationalchemistry #modelling #chemistry #researchanddevelopment #research #MaterialsSquare #ComputationalChemistry #Tutorial #DFT #simulationsoftware #simulation