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您的检索式：作者名="Subra Suresh"
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| 1 | 建立全球创新文化显示文摘今天,新概念、新想法不断涌现,要把这些好的想法变成科研成果和产品,需要更合理的科技政策。 | 苏布拉·苏雷什(Subra Suresh) | 2013 | 环球科学2013，，11： | 1 |
| 2 | Biomechanics and biophysics of cancer cells显示文摘 | Subra Suresh | 2007 | Acta Materialia2007，55，12： | 1 |
| 3 | Biomechanics and biophysics of cancer cells显示文摘 | Subra Suresh | 2007 | Acta Biomaterialia2007，，4： | 1 |
| 4 | 纳米级金刚石的超大弹性形变显示文摘金刚石具有很强的硬度和耐磨性,但使金刚石形变通常会导致脆性断裂。本文研究展示了纳米级(~300纳米)单晶和多晶金刚石针的超大、完全可逆的弹性形变。对于单晶金刚石,最大拉伸应变(高达9%)接近理论弹性极限,并且相应的最大拉伸应力达到约89至98千兆帕斯卡。 | Amit Banerjee Yang Lu Subra Suresh 刘斐莹 | 2018 | 家电科技2018，0，5： | 0 |
| 5 | Machine learning for deep elastic strain engineering of semiconductor electronic band structure and effective mass显示文摘The controlled introduction of elastic strains is an appealing strategy for modulating the physical properties of semiconductor materials.With the recent discovery of large elastic deformation in nanoscale specimens as diverse as silicon and diamond,employing this strategy to improve device performance necessitates first-principles computations of the fundamental electronic band structure and target figures-of-merit,through the design of an optimal straining pathway.Such simulations,however,call for approaches that combine deep learning algorithms and physics of deformation with band structure calculations to custom-design electronic and optical properties.Motivated by this challenge,we present here details of a machine learning framework involving convolutional neural networks to represent the topology and curvature of band structures in k-space.These calculations enable us to identify ways in which the physical properties can be altered through“deep”elastic strain engineering up to a large fraction of the ideal strain.Algorithms capable of active learning and informed by the underlying physics were presented here for predicting the bandgap and the band structure.By training a surrogate model with ab initio computational data,our method can identify the most efficient strain energy pathway to realize physical property changes.The power of this method is further demonstrated with results from the prediction of strain states that influence the effective electron mass.We illustrate the applications of the method with specific results for diamonds,although the general deep learning technique presented here is potentially useful for optimizing the physical properties of a wide variety of semiconductor materials。 | Evgenii Tsymbalov Zhe Shi Ming Dao Subra Suresh Ju Li Alexander Shapeev | 2021 | npj Computational Materials2021，，1： | 0 |
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