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Abstract Predictive materials synthesis is the primary bottleneck in realizing functional and quantum materials. Strategies for synthesis of promising materials are currently identified by time-consuming trial and error and there are no known predictive schemes to design synthesis parameters for materials. We use offline reinforcement learning (RL) to predict optimal synthesis schedules, i.e., a time-sequence of reaction conditions like temperatures and concentrations, for the synthesis of semiconducting monolayer MoS2 using chemical vapor deposition. The RL agent, trained on 10,000 computational synthesis simulations, learned threshold temperatures and chemical potentials for onset of chemical reactions and predicted previously unknown synthesis schedules that produce well-sulfidized crystalline, phase-pure MoS2. The model can be extended to multi-task objectives such as predicting profiles for synthesis of complex structures including multi-phase heterostructures and can predict long-time behavior of reacting systems, far beyond the domain of molecular dynamics simulations, making these predictions directly relevant to experimental synthesis.

Abstract Mechanical behavior of 2D materials such as MoS2 can be tuned by the ancient art of kirigami. Experiments and atomistic simulations show that 2D materials can be stretched more than 50% by strategic insertion of cuts. However, designing kirigami structures with desired mechanical properties is highly sensitive to the pattern and location of kirigami cuts. We use reinforcement learning (RL) to generate a wide range of highly stretchable MoS2 kirigami structures. The RL agent is trained by a small fraction (1.45%) of molecular dynamics simulation data, randomly sampled from a search space of over 4 million candidates for MoS2 kirigami structures with 6 cuts. After training, the RL agent not only proposes 6-cut kirigami structures that have stretchability above 45%, but also gains mechanistic insight to propose highly stretchable (above 40%) kirigami structures consisting of 8 and 10 cuts from a search space of billion candidates as zero-shot predictions.

RXMD is a parallel reactive molecular dynamics (RMD) simulator designed to perform large-scale RMD simulations on commodity laptop computers to supercomputer platforms. With extensive Time-to-Solution (TtoS) optimization techniques and scalable algorithms implemented, RXMD enables researchers to explorer materials design space that requires vast spatial-extent and long-time atomistic events with highly accurate chemistry. Extensible and modular software architecture allows to implement innovative optimization techniques. RXMD is one of core simulation engines at Material Genome Innovation for Computational Software (MAGICS) center, and is freely distributed as an open-source software from MAGICS website and Github. RXMD is also used as computational materials courseware to provide basic training of parallel RMD simulations for computational researchers and community.

QXMD is a scalable, parallel program for Quantum Molecular Dynamics simulations with various eXtensions. Its simulation engine is based on (time-dependent) density functional theory using pseudopotentials and a plane-wave basis set, while extensions include nonadiabatic electron–nuclei dynamics and multiscale shock technique. QXMD serves as a community-development platform for new methods and algorithms, a research platform on high-end parallel supercomputers, and an educational platform for hands-on training. Keywords: Nonadiabatic quantum molecular dynamics, Parallel computing, Hands-on training

Quantification of lattice thermal conductivity of two-dimensional semiconductors like MoS2 is necessary for the design of electronic and thermoelectric devices, but direct experimental measurements on free-standing samples is challenging. Molecular dynamics simulations, with appropriate corrections, can provide a reference value for thermal conductivity for these material systems. Here, we construct a new empirical forcefield of the Stillinger-Weber form, parameterized to phonon dispersion relations, lattice constants and elastic moduli and we use it to compute a material-intrinsic thermal conductivity of 38.1 W/m-K at room temperature and estimate a maximum thermal conductivity of 85.4 W/m-K at T = 200 K. We also identify that phonon scattering by the large isotopic mass distribution of Mo and S contributes a significant correction (>45%) to the thermal conductivity at low temperatures.