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A multi-fidelity machine learning approach to high throughput materials screening

npj Computational Materials 2022年第1期8卷 2453-2461页

作者： Clyde Fare Peter Fenner Matthew Benatan Alessandro Varsi edward o.pyzer-knapp IBM Research-Europe-Daresbury DaresburyUK University of Liverpool LiverpoolUK

The ever-increasing capability of computational methods has resulted in their general acceptance as a key part of the materials design *** 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 *** 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 *** 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 *** 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.

关键词： has approach learning

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Bayesian optimization for Field-Scale Geological Carbon Storage

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Engineering 2022年第11期18卷 96-104页

作者： Xueying Lu Kirk E.Jordan Mary F.Wheeler edward o.pyzer-knapp Matthew Benatan Center for Subsurface Modeling Oden Institute for Computational Engineering and SciencesThe University of Texas at AustinAustinTX 78712USA IBM Research United Kingdom Warrington WA44ADUK

We present a framework that couples a high-fidelity compositional reservoir simulator with Bayesian optimization(Bo)for injection well scheduling optimization in geological carbon *** work represents one of the first at tempts to apply Bo and high-fidelity physics models to geological carbon *** implicit parallel accurate reservoir simulator(IPARS)is utilized to accurately capture the underlying physical processes during Co_(2)*** provides a framework for several flow and mechanics models and thus supports both stand-alone and coupled *** this work,we use the compositional flow module to simulate the geological carbon storage *** 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 ***,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)*** 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 *** 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 *** 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 *** optimized injection schedule achieves 16%more gas storage volume and 56%less water/surfactant usage compared with the *** performance of Bo is compared with that of a genetic algorithm(GA)and a covariance matrix adaptation(CMA)-e

关键词： Compositional flow Bayesian optimization Geological carbon storage CCUS Machine learning AI for science

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Accelerating materials discovery using artificial intelligence, high performance computing and robotics

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npj Computational Materials 2022年第1期8卷 767-775页

作者： edward o.pyzer-knapp Jed W.Pitera Peter W.J.Staar Seiji Takeda Teodoro Laino Daniel P.Sanders James Sexton John R.Smith Alessandro Curioni IBM Research Europe-Daresbury DaresburyUK IBM Almaden Research Centre San JoseCAUSA IBM Research Europe Zurich RüschlikonSwitzerland IBM Research Tokyo TokyoJapan IBM Thomas J.Watson Research Centre Yorktown HeightsNYUSA

New tools enable new ways of working,and materials science is no *** 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 *** this perspective,we describe how these new capabilities enable the acceleration and enrichment of each stage of the discovery *** 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.

关键词： artificial computing enable

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