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Autonomous reinforcement learning agent for stretchable kirigami design of 2D materials

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npj Computational Materials 2021年第1期7卷 934-941页

作者： Pankaj Rajak Beibei Wang Ken-ichi Nomura Ye Luo aiichiro nakano Rajiv Kalia Priya Vashishta Argonne Leadership Computing Facility Argonne National LaboratoryArgonneILUSA Collaboratory for Advanced Computing and Simulations University of Southern CaliforniaLos AngelesCAUSA Department of Physics and Astronomy University of Southern CaliforniaLos AngelesCAUSA Department of Chemical Engineering and Materials Science University of Southern CaliforniaLos AngelesCAUSA Department of Computer Science University of Southern CaliforniaLos AngelesCAUSA

Mechanical behavior of 2D materials such as MoS_(2) can be tuned by the ancient art of *** and atomistic simulations show that 2D materials can be stretched more than 50%by strategic insertion of ***,designing kirigami structures with desired mechanical properties is highly sensitive to the pattern and location of kirigami *** use reinforcement learning(RL)to generate a wide range of highly stretchable MoS_(2) kirigami *** 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 *** 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.

关键词： agent learning stretch

Multiobjective genetic training and uncertainty quantification of reactive force fields

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npj Computational Materials 2018年第1期4卷 305-311页

作者： Ankit Mishra Sungwook Hong Pankaj Rajak Chunyang Sheng Ken-ichi Nomura Rajiv K.Kalia aiichiro nakano Priya Vashishta Collaboratory for Advanced Computing and Simulations University of Southern CaliforniaLos AngelesCA 90089-0242USA Department of Chemical Engineering&Materials Science University of Southern CaliforniaLos AngelesCA 90089-1211USA Department of Physics&Astronomy University of Southern CaliforniaLos AngelesCA 90089-0484USA Department of Computer Science University of Southern CaliforniaLos AngelesCA 90089-0781USA Department of Biological Sciences University of Southern CaliforniaLos AngelesCA 90089-0371USA

The ReaxFF reactive force-field approach has significantly extended the applicability of reactive molecular dynamics simulations to a wide range of material properties and *** parameters are commonly trained to fit a predefined set of quantummechanical data,but it remains uncertain how accurately the quantities of interest are described when applied to complex chemical ***,we present a dynamic approach based on multiobjective genetic algorithm for the training of ReaxFF parameters and uncertainty quantification of simulated quantities of *** parameters are trained by directly fitting reactive molecular dynamics trajectories against quantum molecular dynamics trajectories on the fly,where the Pareto optimal front for the multiple quantities of interest provides an ensemble of ReaxFF models for uncertainty *** in situ multiobjective genetic algorithm workflow achieves scalability by eliminating the file I/O bottleneck using interprocess *** in situ multiobjective genetic algorithm workflow has been applied to high-temperature sulfidation of MoO_(3) by H_(2)S precursor,which is an essential reaction step for chemical vapor deposition synthesis of MoS_(2) *** work suggests a new reactive molecular dynamics simulation approach for far-from-equilibrium chemical processes,which quantitatively reproduces quantum molecular dynamics simulations while providing error bars.

关键词： quantities multiobjective uncertainty

Active learning for accelerated design of layered materials

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npj Computational Materials 2018年第1期4卷 54-62页

作者： Lindsay Bassman Pankaj Rajak Rajiv K.Kalia aiichiro nakano Fei Sha Jifeng Sun David J.Singh Muratahan Aykol Patrick Huck Kristin Persson Priya Vashishta Collaboratory for Advanced Computing and Simulations University of Southern CaliforniaLos AngelesCA 90089USA Department of Physics and Astronomy University of Southern CaliforniaLos AngelesCA 90089USA Department of Chemical Engineering and Material Science University of Southern CaliforniaLos AngelesCA 90089USA Department of Computer Science University of Southern CaliforniaLos AngelesCA 90089USA Department of Biological Sciences University of Southern CaliforniaLos AngelesCA 90089USA Department of Physics and Astronomy University of MissouriColumbiaMO 65211-7010USA Lawrence Berkeley National Laboratory 1 Cyclotron RoadBerkeleyCA 94720USA

Hetero-structures made from vertically stacked monolayers of transition metal dichalcogenides hold great potential for optoelectronic and thermoelectric *** of the optimal layered material for specific applications necessitates the estimation of key material properties,such as electronic band structure and thermal transport ***,screening of material properties via brute force ab initio calculations of the entire material structure space exceeds the limits of current computing ***,the functional dependence of material properties on the structures is often complicated,making simplistic statistical procedures for prediction difficult to employ without large amounts of data ***,we present a Gaussian process regression model,which predicts material properties of an input hetero-structure,as well as an active learning model based on Bayesian optimization,which can efficiently discover the optimal hetero-structure using a minimal number of ab initio *** electronic band gap,conduction/valence band dispersions,and thermoelectric performance are used as representative material properties for prediction and *** Materials Project platform is used for electronic structure computation,while the BoltzTraP code is used to compute thermoelectric *** optimization is shown to significantly reduce the computational cost of discovering the optimal structure when compared with finding an optimal structure by building a regression model to predict material *** models can be used for predictions with respect to any material property and our software,including data preparation code based on the Python Materials Genomics(PyMatGen)library as well as python-based machine learning code,is available open source.

关键词： Active Python python

Author Correction:Active learning for accelerated design of layered materials

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npj Computational Materials 2022年第1期8卷 2075-2075页

作者： Lindsay Bassman Oftelie Pankaj Rajak Rajiv KKalia aiichiro nakano Fei Sha Jifeng Sun David JSingh Muratahan Aykol Patrick Huck Kristin Persson Priya Vashishta Collaboratory for Advanced Computing and Simulations University of Southern California Los Angeles CA 90089 USA Department of Physics and Astronomy University of Southern California Los Angeles CA 90089 USA Department of Chemical Engineering and Material Science University of Southern California Los Angeles CA 90089 USA Department of Computer Science University of Southern California Los Angeles CA 90089 USA Department of Biological Sciences University of Southern California Los Angeles CA 90089 USA Department of Physics and Astronomy University of Missouri Columbia MO 65211-7010 USA Lawrence Berkeley National Laboratory 1 Cyclotron Road Berkeley CA 94720 USA

Since the publication of this work,Lindsay Bassman Oftelie has changed their name from Lindsay *** has now been amended.

关键词： Active learning layered

Rare combination of familial adenomatous polyposis and gallbladder polyps

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World Journal of Gastroenterology 2014年第46期20卷 17661-17665页

作者： Yasuhisa Mori Norihiro Sato Nobutaka Matayoshi Toshihisa Tamura Noritaka Minagawa Kazunori Shibao aiichiro Higure Mitsuhiro Nakamoto Masashi Taguchi Koji Yamaguchi Department of Surgery School of MedicineUniversity of Occupational and Environmental HealthKitakyushuFukuoka 807-8555Japan Department of Pathology and Cell Biology School of MedicineUniversity of Occupational and Environmental HealthKitakyushuFukuoka 807-8555Japan Department of Internal Medicine School of MedicineUniversity of Occupational and Environmental HealthKitakyushuFukuoka 807-8555Japan

Familial adenomatous polyposis is associated with a high incidence of malignancies in the upper gastrointestinal tract (particularly ampullary adenocarcinomas). However, few reports have described a correlation between familial adenomatous polyposis and gallbladder neoplasms. We present a case of a 60-year-old woman with familial adenomatous polyposis who presented with an elevated mass in the neck of the gallbladder (measuring 16 mm × 8 mm in diameter) and multiple small cholecystic polyps. She had undergone a total colectomy for ascending colon cancer associated with familial adenomatous polyposis 22 years previously. The patient underwent laparoscopic cholecystectomy under a preoperative diagnosis of multifocal gallbladder polyps. Pathologic examination of the resected gallbladder revealed more than 70 adenomatous lesions, a feature consistent with adenoma of the gallbladder. This case suggests a requirement for long-term surveillance of the biliary system in addition to the gastrointestinal tract in patients with familial adenomatous polyposis.

关键词： Adenoma Adenomatous polyposis coli Biliary system Familial adenomatous polyposis Gallbladder polyp

Stapled gastro/duodenojejunostomy shortens reconstruction time during pylorus-preserving pancreaticoduodenectomy

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World Journal of Gastroenterology 2013年第48期19卷 9399-9404页

作者： Norihiro Sato Kei Yabuki Shiro Kohi Yasuhisa Mori Noritaka Minagawa Toshihisa Tamura aiichiro Higure Koji Yamaguchi Department of Surgery 1 School of Medicine University of Occupational and Environmental Health Kitakyushu 807-8555 Japan

AIM:To investigate whether a stapled technique is superior to the conventional hand-sewn technique for gastro/duodenojejunostomy during pylorus-preserving pancreaticoduodenectomy(PpPD).METHODS:In October 2010,we introduced a mechanical anastomotic technique of gastro-or duodenojejunostomy using staplers during *** compared clinical outcomes between 19 patients who underwent PpPD with a stapled gastro/duodenojejunostomy(stapled anastomosis group)and 19 patients who underwent PpPD with a conventional hand-sewn duodenojejunostomy(hand-sewn anastomosis group).RESULTS:The time required for reconstruction was significantly shorter in the stapled anastomosis group than in the hand-sewn anastomosis group(186.0±29.4 min vs 219.7±50.0 min,P=0.02).In addition,intraoperative blood loss was significantly less(391.0±212.0 mL vs 647.1±482.1 mL,P=0.03)and the time to oral intake was significantly shorter(5.4±1.7d vs 11.3±7.9 d,P=0.002)in the stapled anastomosis group than in the hand-sewn anastomosis *** were no differences in the incidences of delayed gastric emptying and other postoperative complications between the ***:These results suggest that stapled gastro/duodenojejunostomy shortens reconstruction time during PpPD without affecting the incidence of delayed gastric emptying.

关键词： Pylorus-preserving pancreaticoduodenectomy Stapled anastomosis Gastrojejunostomy Duodenojejunostomy Delayed gastric emptying