Mechanical behavior of 2D materials such as MoS_(2) 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...Mechanical behavior of 2D materials such as MoS_(2) 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 MoS_(2) 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 MoS_(2)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.展开更多
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 the...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.展开更多
基金This work was supported by National Science Foundation,Future Manufacturing Program,Award 2036359This research was partly supported by Aurora Early Science programs and used resources of the Argonne Leadership Computing Facility,which is a DOE Office of Science User Facility supported under Contract DE-AC02-06CH11357Computations were performed at the Argonne Leadership Computing Facility under the DOE INCITE and Aurora Early Science programs and at the Center for Advanced Research Computing of the University of Southern California.We would like to thank one of the reviewers for asking us to examine if RL can be used to propose a highstretchability kirigami structure with 10 cuts,which led to zero-shot predictions for 8-and 10-cut structures that have stretchability above 40%.
文摘Mechanical behavior of 2D materials such as MoS_(2) 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 MoS_(2) 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 MoS_(2)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.
基金This work was supported as part of the Computational Materials Sciences Program funded by the U.S.Department of Energy,Office of Science,Basic Energy Sciences,under Award Number DE-SC0014607This research was partly supported by Aurora Early Science programs and used resources of the Argonne Leadership Computing Facility,which is a DOE Office of Science User Facility supported under Contract DEAC02-06CH11357Computations were performed at the Argonne Leadership Computing Facility under the DOE INCITE and Aurora Early Science programs and at the Center for Advanced Research Computing of the University of Southern California.
文摘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.
