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
Upgrades to advanced scientific user facilities such as next-generation x-ray light sources,nanoscience centers,and neutron facilities are revolutionizing our understanding of materials across the spectrum of the phys...Upgrades to advanced scientific user facilities such as next-generation x-ray light sources,nanoscience centers,and neutron facilities are revolutionizing our understanding of materials across the spectrum of the physical sciences,from life sciences to microelectronics.However,these facility and instrument upgrades come with a significant increase in complexity.Driven by more exacting scientific needs,instruments and experiments become more intricate each year.This increased operational complexity makes it ever more challenging for domain scientists to design experiments that effectively leverage the capabilities of and operate on these advanced instruments.Large language models(LLMs)can perform complex information retrieval,assist in knowledge-intensive tasks across applications,and provide guidance on tool usage.Using x-ray light sources,leadership computing,and nanoscience centers as representative examples,we describe preliminary experiments with a Context-Aware Language Model for Science(CALMS)to assist scientists with instrument operations and complex experimentation.With the ability to retrieve relevant information from facility documentation,CALMS can answer simple questions on scientific capabilities and other operational procedures.With the ability to interface with software tools and experimental hardware,CALMS can conversationally operate scientific instruments.By making information more accessible and acting on user needs,LLMs could expand and diversify scientific facilities’users and accelerate scientific output.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
A data-based reduced-order model(ROM)is developed to accelerate the time integration of stiff chemically reacting systems by effectively removing the stiffness arising from a wide spectrum of chemical time scales.Spec...A data-based reduced-order model(ROM)is developed to accelerate the time integration of stiff chemically reacting systems by effectively removing the stiffness arising from a wide spectrum of chemical time scales.Specifically,the objective of this work is to develop a ROM that acts as a non-stiff surrogate model for the time evolution of the thermochemical state vector(temperature and species mass fractions)during an otherwise highly stiff and nonlinear ignition process.The model follows an encode-forecast-decode strategy that combines a nonlinear autoencoder(AE)for dimensionality reduction(encode and decode steps)with a neural ordinary differential equation(NODE)for modeling the dynamical system in the AE-provided latent space(forecasting step).By means of detailed timescale analysis by leveraging the dynamical system Jacobians,this work shows how data-based projection operators provided by autoencoders can inherently construct the latent spaces by removing unnecessary fast timescales,even more effectively than physics-based counterparts based on an eigenvalue analysis.A key finding is that the most significant degree of stiffness reduction is achieved through an end-to-end training strategy,where both AE and neural ODE parameters are optimized simultaneously,allowing the discovered latent space to be dynamics-informed.In addition to end-to-end training,this work highlights the vital contribution of AE nonlinearity in the stiffness reduction task.For the prediction of homogeneous ignition phenomena for H2-air and C2H4-air mixtures,the proposed ROM achieves several ordersof-magnitude increase in the integration time step size when compared to(a)a baseline CVODE solver for the full-chemical system,(b)statistical technique–principal component analysis(PCA),and(c)computational singular perturbation(CSP),a vetted physics-based stiffness-reducing modeling framework.展开更多
Modern diagnostic tools in turbulent combustion allow for highly-resolved measurements of reacting flows;however,they tend to generate massive data-sets,rendering conventional analysis intractable and inefficient.To a...Modern diagnostic tools in turbulent combustion allow for highly-resolved measurements of reacting flows;however,they tend to generate massive data-sets,rendering conventional analysis intractable and inefficient.To alleviate this problem,machine learning tools may be used to,for example,discover features from the data for downstream modeling and prediction tasks.To this end,this work applies generative adversarial networks(GANs)to generate realistic flame images based on a time-resolved data set of hydroxide concentration snapshots obtained from planar laser induced fluorescence measurements of a model combustor.The generative model is able to generate flames in attached,lifted,and intermediate configurations dictated by the user.Using𝑙-means clustering and proper orthogonal decomposition,the synthetic image set produced by the GAN is shown to be visually similar to the real image set,with recirculation zones and burned/unburned regions clearly present,indicating good GAN performance in capturing the experimental data statistical structure.Combined with techniques for controlling the configuration of generated flames,this work opens new avenues towards tractable statistical analysis and modeling of flame behavior,as well as rapid and inexpensive flame data generation.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supported by the U.S.DOE,Office of Basic Energy Sciences,under Contract No.DE-AC02-06CH11357supported by the U.S.DOE Office of Science-Advanced Scientific Computing Research Program,under Contract No.DE-AC02-06CH11357.
文摘Upgrades to advanced scientific user facilities such as next-generation x-ray light sources,nanoscience centers,and neutron facilities are revolutionizing our understanding of materials across the spectrum of the physical sciences,from life sciences to microelectronics.However,these facility and instrument upgrades come with a significant increase in complexity.Driven by more exacting scientific needs,instruments and experiments become more intricate each year.This increased operational complexity makes it ever more challenging for domain scientists to design experiments that effectively leverage the capabilities of and operate on these advanced instruments.Large language models(LLMs)can perform complex information retrieval,assist in knowledge-intensive tasks across applications,and provide guidance on tool usage.Using x-ray light sources,leadership computing,and nanoscience centers as representative examples,we describe preliminary experiments with a Context-Aware Language Model for Science(CALMS)to assist scientists with instrument operations and complex experimentation.With the ability to retrieve relevant information from facility documentation,CALMS can answer simple questions on scientific capabilities and other operational procedures.With the ability to interface with software tools and experimental hardware,CALMS can conversationally operate scientific instruments.By making information more accessible and acting on user needs,LLMs could expand and diversify scientific facilities’users and accelerate scientific output.
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.
基金support from the Argonne Leadership Computing Facility,which is a U.S.Department of Energy Office of Science User Facility operated under contract DE-AC02-06CH11357support of ONR,United States Grant No.N00014-21-1-2475 with Dr.Eric Marineau as Program Manager.
文摘A data-based reduced-order model(ROM)is developed to accelerate the time integration of stiff chemically reacting systems by effectively removing the stiffness arising from a wide spectrum of chemical time scales.Specifically,the objective of this work is to develop a ROM that acts as a non-stiff surrogate model for the time evolution of the thermochemical state vector(temperature and species mass fractions)during an otherwise highly stiff and nonlinear ignition process.The model follows an encode-forecast-decode strategy that combines a nonlinear autoencoder(AE)for dimensionality reduction(encode and decode steps)with a neural ordinary differential equation(NODE)for modeling the dynamical system in the AE-provided latent space(forecasting step).By means of detailed timescale analysis by leveraging the dynamical system Jacobians,this work shows how data-based projection operators provided by autoencoders can inherently construct the latent spaces by removing unnecessary fast timescales,even more effectively than physics-based counterparts based on an eigenvalue analysis.A key finding is that the most significant degree of stiffness reduction is achieved through an end-to-end training strategy,where both AE and neural ODE parameters are optimized simultaneously,allowing the discovered latent space to be dynamics-informed.In addition to end-to-end training,this work highlights the vital contribution of AE nonlinearity in the stiffness reduction task.For the prediction of homogeneous ignition phenomena for H2-air and C2H4-air mixtures,the proposed ROM achieves several ordersof-magnitude increase in the integration time step size when compared to(a)a baseline CVODE solver for the full-chemical system,(b)statistical technique–principal component analysis(PCA),and(c)computational singular perturbation(CSP),a vetted physics-based stiffness-reducing modeling framework.
文摘Modern diagnostic tools in turbulent combustion allow for highly-resolved measurements of reacting flows;however,they tend to generate massive data-sets,rendering conventional analysis intractable and inefficient.To alleviate this problem,machine learning tools may be used to,for example,discover features from the data for downstream modeling and prediction tasks.To this end,this work applies generative adversarial networks(GANs)to generate realistic flame images based on a time-resolved data set of hydroxide concentration snapshots obtained from planar laser induced fluorescence measurements of a model combustor.The generative model is able to generate flames in attached,lifted,and intermediate configurations dictated by the user.Using𝑙-means clustering and proper orthogonal decomposition,the synthetic image set produced by the GAN is shown to be visually similar to the real image set,with recirculation zones and burned/unburned regions clearly present,indicating good GAN performance in capturing the experimental data statistical structure.Combined with techniques for controlling the configuration of generated flames,this work opens new avenues towards tractable statistical analysis and modeling of flame behavior,as well as rapid and inexpensive flame data generation.
