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MDTS: automatic complex materials design using Monte Carlo tree search
Thaer M Dieb1,2, Shenghong Ju3, Kazuki Yoshizoe4
1National Institute for Materials Science, Tsukuba, Japan.
Science and Technology of Advanced Materials
|August 15, 2017
Summary
We introduce Materials Design using Tree Search (MDTS), a novel Python library for autonomous complex materials design. MDTS efficiently optimizes material structures without parameter tuning, outperforming existing methods.
Area of Science:
- Materials Science
- Computational Chemistry
- Optimization Algorithms
Background:
- Complex materials design is a challenging combinatorial optimization problem.
- Existing methods like evolutionary algorithms require manual parameter tuning.
- Bayesian optimization struggles with scalability for large problems.
Purpose of the Study:
- To present a novel, autonomous algorithm for materials design.
- To introduce the Materials Design using Tree Search (MDTS) Python library.
- To demonstrate MDTS's efficiency and scalability compared to existing methods.
Main Methods:
- Utilizing a Monte Carlo tree search (MCTS) algorithm, inspired by its success in game AI.
- Developing the MDTS Python library for autonomous materials design.
- Benchmarking MDTS against Bayesian optimization on complex materials design tasks.
Main Results:
- MDTS demonstrated competitive search efficiency and superior scalability.
- The algorithm successfully designed large Silicon-Germanium (Si-Ge) alloy structures.
- MDTS achieved these results autonomously, without parameter tuning.
Conclusions:
- MDTS offers an autonomous and efficient approach to complex materials design.
- The library provides a scalable solution for problems intractable for other methods.
- MDTS represents a significant advancement in computational materials discovery.
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