We present a framework that couples a high-fidelity compositional reservoir simulator with Bayesian optimization(BO)for injection well scheduling optimization in geological carbon sequestration.This work represents on...We present a framework that couples a high-fidelity compositional reservoir simulator with Bayesian optimization(BO)for injection well scheduling optimization in geological carbon sequestration.This work represents one of the first at tempts to apply BO and high-fidelity physics models to geological carbon storage.The implicit parallel accurate reservoir simulator(IPARS)is utilized to accurately capture the underlying physical processes during CO_(2)sequestration.IPARS provides a framework for several flow and mechanics models and thus supports both stand-alone and coupled simulations.In this work,we use the compositional flow module to simulate the geological carbon storage process.The compositional flow model,which includes a hysteretic three-phase relative permeability model,accounts for three major CO_(2)trapping mechanisms:structural trapping,residual gas trapping,and solubility trapping.Furthermore,IPARS is coupled to the International Business Machines(IBM)Corporation Bayesian Optimization Accelerator(BOA)for parallel optimizations of CO_(2)injection strategies during field-scale CO_(2)sequestration.BO builds a probabilistic surrogate for the objective function using a Bayesian machine learning algorithm-the Gaussian process regression,and then uses an acquisition function that leverages the uncertainty in the surrogate to decide where to sample.The IBM BOA addresses the three weaknesses of standard BO that limits its scalability in that IBM BOA supports parallel(batch)executions,scales better for high-dimensional problems,and is more robust to initializations.We demonstrate these merits by applying the algorithm in the optimization of the CO_(2)injection schedule in the Cranfield site in Mississippi,USA,using field data.The optimized injection schedule achieves 16%more gas storage volume and 56%less water/surfactant usage compared with the baseline.The performance of BO is compared with that of a genetic algorithm(GA)and a covariance matrix adaptation(CMA)-evolution strategy(ES).The results demonstrate the superior performance of BO,in that it achieves a competitive objective function value with over 60%fewer forward model evaluations.展开更多
New tools enable new ways of working,and materials science is no exception.In materials discovery,traditional manual,serial,and human-intensive work is being augmented by automated,parallel,and iterative processes dri...New tools enable new ways of working,and materials science is no exception.In materials discovery,traditional manual,serial,and human-intensive work is being augmented by automated,parallel,and iterative processes driven by Artificial Intelligence (AI),simulation and experimental automation.In this perspective,we describe how these new capabilities enable the acceleration and enrichment of each stage of the discovery cycle.We show,using the example of the development of a novel chemically amplified photoresist,how these technologies’ impacts are amplified when they are used in concert with each other as powerful,heterogeneous workflows.展开更多
The ever-increasing capability of computational methods has resulted in their general acceptance as a key part of the materials design process.Traditionally this has been achieved using a so-called computational funne...The ever-increasing capability of computational methods has resulted in their general acceptance as a key part of the materials design process.Traditionally this has been achieved using a so-called computational funnel,where increasingly accurate-and expensive–methodologies are used to winnow down a large initial library to a size which can be tackled by experiment.In this paper we present an alternative approach,using a multi-output Gaussian process to fuse the information gained from both experimental and computational methods into a single,dynamically evolving design.Common challenges with computational funnels,such as mis-ordering methods,and the inclusion of non-informative steps are avoided by learning the relationships between methods on the fly.We show this approach reduces overall optimisation cost on average by around a factor of three compared to other commonly used approaches,through evaluation on three challenging materials design problems.展开更多
Machine learning interatomic potentials(MLIPs)have achieved remarkable accuracy on standard benchmarks,yet their ability to reproduce molecular kinetics,critical for reaction rate calculations,remains largely unexplor...Machine learning interatomic potentials(MLIPs)have achieved remarkable accuracy on standard benchmarks,yet their ability to reproduce molecular kinetics,critical for reaction rate calculations,remains largely unexplored.We introduce Landscape17,a dataset of complete kinetic transition networks(KTNs)for the six molecules of the rMD17 dataset,computed using hybrid-level density functional theory.Each KTN contains minima,transition states,and approximate steepest-descent paths,along with energies,forces,and Hessian eigenspectra at stationary points.We develop a comprehensive test suite to evaluate theMLIPs’ability to reproduce these reference landscapes and apply it to state-of-the-art architectures.Our results reveal limitations in current MLIPs:all models considered miss over half of the DFT transition state paths and generate stable unphysical structures throughout the potential energy surface.Data augmentation with pathway configurations improves reproduction of DFT potential energy surfaces,resulting in significant improvement in global kinetics.However,these models still produce many spurious stable structures,indicating that current MLIP architectures face underlying challenges in capturing the topology of molecular potential energy surfaces.The Landscape17 benchmark provides a straightforward,lightweight,but demanding test of MLIPs for kinetic applications,and we propose this test for validation of next-generation MLIPs targeting reaction discovery and rate prediction.展开更多
Large language models,commonly known as LLMs,are showing promise in tacking some of the most complex tasks in AI.In this perspective,we review the wider field of foundation models-of which LLMs are a component-and the...Large language models,commonly known as LLMs,are showing promise in tacking some of the most complex tasks in AI.In this perspective,we review the wider field of foundation models-of which LLMs are a component-and their application to the field of materials discovery.In addition to the current state of the art-including applications to property prediction,synthesis planning and molecular generation-we also take a look to the future,and posit how new methods of data capture,and indeed modalities of data,will influence the direction of this emerging field.展开更多
基金supported under the Center for Subsurface Modeling Affiliates Program,United States of America and the National Science Foundation,United States of America(1911320,Collaborative Research:High-Fidelity Modeling of Poromechanics with Strong Discontinuities)。
文摘We present a framework that couples a high-fidelity compositional reservoir simulator with Bayesian optimization(BO)for injection well scheduling optimization in geological carbon sequestration.This work represents one of the first at tempts to apply BO and high-fidelity physics models to geological carbon storage.The implicit parallel accurate reservoir simulator(IPARS)is utilized to accurately capture the underlying physical processes during CO_(2)sequestration.IPARS provides a framework for several flow and mechanics models and thus supports both stand-alone and coupled simulations.In this work,we use the compositional flow module to simulate the geological carbon storage process.The compositional flow model,which includes a hysteretic three-phase relative permeability model,accounts for three major CO_(2)trapping mechanisms:structural trapping,residual gas trapping,and solubility trapping.Furthermore,IPARS is coupled to the International Business Machines(IBM)Corporation Bayesian Optimization Accelerator(BOA)for parallel optimizations of CO_(2)injection strategies during field-scale CO_(2)sequestration.BO builds a probabilistic surrogate for the objective function using a Bayesian machine learning algorithm-the Gaussian process regression,and then uses an acquisition function that leverages the uncertainty in the surrogate to decide where to sample.The IBM BOA addresses the three weaknesses of standard BO that limits its scalability in that IBM BOA supports parallel(batch)executions,scales better for high-dimensional problems,and is more robust to initializations.We demonstrate these merits by applying the algorithm in the optimization of the CO_(2)injection schedule in the Cranfield site in Mississippi,USA,using field data.The optimized injection schedule achieves 16%more gas storage volume and 56%less water/surfactant usage compared with the baseline.The performance of BO is compared with that of a genetic algorithm(GA)and a covariance matrix adaptation(CMA)-evolution strategy(ES).The results demonstrate the superior performance of BO,in that it achieves a competitive objective function value with over 60%fewer forward model evaluations.
文摘New tools enable new ways of working,and materials science is no exception.In materials discovery,traditional manual,serial,and human-intensive work is being augmented by automated,parallel,and iterative processes driven by Artificial Intelligence (AI),simulation and experimental automation.In this perspective,we describe how these new capabilities enable the acceleration and enrichment of each stage of the discovery cycle.We show,using the example of the development of a novel chemically amplified photoresist,how these technologies’ impacts are amplified when they are used in concert with each other as powerful,heterogeneous workflows.
基金This work was supported by the Hartree National Centre for Digital Innovation,a collaboration between Science and Technology Facilities Council and IBM.
文摘The ever-increasing capability of computational methods has resulted in their general acceptance as a key part of the materials design process.Traditionally this has been achieved using a so-called computational funnel,where increasingly accurate-and expensive–methodologies are used to winnow down a large initial library to a size which can be tackled by experiment.In this paper we present an alternative approach,using a multi-output Gaussian process to fuse the information gained from both experimental and computational methods into a single,dynamically evolving design.Common challenges with computational funnels,such as mis-ordering methods,and the inclusion of non-informative steps are avoided by learning the relationships between methods on the fly.We show this approach reduces overall optimisation cost on average by around a factor of three compared to other commonly used approaches,through evaluation on three challenging materials design problems.
基金performed using resources provided by the Cambridge Service for Data Driven Discovery (www.csd3.cam.ac.uk), provided by Dell EMC and Intel using Tier-2 funding from the Engineering and Physical Sciences Research Council(capital grant EP/T022159/1), and DiRAC funding from the Science and Technology Facilities Council (www.dirac.ac.uk). V.C. acknowledges the computational resources obtained through the University of Cambridge EPSRC Core Equipment Award (EP/X034712/1) and EPSRC IAA award number G116766. The authors thank Gábor Csányi for useful discussions.
文摘Machine learning interatomic potentials(MLIPs)have achieved remarkable accuracy on standard benchmarks,yet their ability to reproduce molecular kinetics,critical for reaction rate calculations,remains largely unexplored.We introduce Landscape17,a dataset of complete kinetic transition networks(KTNs)for the six molecules of the rMD17 dataset,computed using hybrid-level density functional theory.Each KTN contains minima,transition states,and approximate steepest-descent paths,along with energies,forces,and Hessian eigenspectra at stationary points.We develop a comprehensive test suite to evaluate theMLIPs’ability to reproduce these reference landscapes and apply it to state-of-the-art architectures.Our results reveal limitations in current MLIPs:all models considered miss over half of the DFT transition state paths and generate stable unphysical structures throughout the potential energy surface.Data augmentation with pathway configurations improves reproduction of DFT potential energy surfaces,resulting in significant improvement in global kinetics.However,these models still produce many spurious stable structures,indicating that current MLIP architectures face underlying challenges in capturing the topology of molecular potential energy surfaces.The Landscape17 benchmark provides a straightforward,lightweight,but demanding test of MLIPs for kinetic applications,and we propose this test for validation of next-generation MLIPs targeting reaction discovery and rate prediction.
文摘Large language models,commonly known as LLMs,are showing promise in tacking some of the most complex tasks in AI.In this perspective,we review the wider field of foundation models-of which LLMs are a component-and their application to the field of materials discovery.In addition to the current state of the art-including applications to property prediction,synthesis planning and molecular generation-we also take a look to the future,and posit how new methods of data capture,and indeed modalities of data,will influence the direction of this emerging field.
