The Experts below are selected from a list of 195 Experts worldwide ranked by ideXlab platform
Li Zhong - One of the best experts on this subject based on the ideXlab platform.
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The study of the structure-toxicity relationships of nitrobenzene derivatives using artificial neural network
Journal of Molecular Science, 2011Co-Authors: Li ZhongAbstract:The toxicity of nitrobenzene derivatives to the fathe ad minnow and quantitative structure-activity relationships(QSAR) were studied through artificial neural networks based on Simple Descriptor of chemical group.It is found that the artificial neural networks have the significant advantages over the multiple regressiong on nonlinear problems.
Carmen Herrmann - One of the best experts on this subject based on the ideXlab platform.
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Exchange Spin Coupling from Gaussian Process Regression.
Journal of Physical Chemistry A, 2020Co-Authors: Marc Philipp Bahlke, Natnael Mogos, Jonny Proppe, Carmen HerrmannAbstract:Heisenberg exchange spin coupling between metal centers is essential for describing and understanding the electronic structure of many molecular catalysts, metalloenzymes, and molecular magnets for potential application in information technology. We explore the machine-learnability of exchange spin coupling beyond linear regression, which has not been studied yet. We employ Gaussian process regression, since it can potentially deal with small training sets (as likely associated with the rather complex molecular structures required for exploring spin coupling) and since it provides uncertainty estimates ("error bars") along with predicted values. We compare a range of Descriptors and kernels for 257 small dicopper complexes and find that a Simple Descriptor based on chemical intuition, consisting only of copper-bridge angles and copper-copper distances, clearly outperforms several more sophisticated Descriptors when it comes to extrapolating toward larger experimentally relevant complexes. Exchange spin coupling is similarly easy to learn as the polarizability, while learning dipole moments is much harder. The strength of the sophisticated Descriptors lies in their ability to linearize structure-property relationships, to the point that a Simple linear ridge regression performs just as well as the kernel-based machine-learning model for our small dicopper data set. The superior extrapolation performance of the Simple Descriptor is unique to exchange spin coupling, reinforcing the crucial role of choosing a suitable Descriptor and highlighting the interesting question of the role of chemical intuition vs systematic or automated selection of features for machine learning in chemistry and material science.
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Exchange Spin Coupling from Gaussian Process Regression
2020Co-Authors: Marc Philipp Bahlke, Natnael Mogos, Jonny Proppe, Carmen HerrmannAbstract:Heisenberg exchange spin coupling between metal centers is essential for describing and understanding the electronic structure of many molecular catalysts, metalloenzymes, and molecular magnets for potential application in information technology. We explore the machine-learnability of exchange spin coupling, which has not been studied yet. We employ Gaussian process regression since it can potentially deal with small training sets (as likely associated with the rather complex molecular structures required for exploring spin coupling) and since it provides uncertainty estimates (“error bars”) along with predicted values. We compare a range of Descriptors and kernels for 257 small dicopper complexes and find that a Simple Descriptor based on chemical intuition, consisting only of copper-bridge angles and copper-copper distances, clearly outperforms several more sophisticated Descriptors when it comes to extrapolating towards larger experimentally relevant complexes. Exchange spin coupling is similarly easy to learn as the polarizability, while learning dipole moments is much harder. The strength of the sophisticated Descriptors lies in their ability to linearize structure-property relationships, to the point that a Simple linear ridge regression performs just as well as the kernel-based machine-learning model for our small dicopper data set. The superior extrapolation performance of the Simple Descriptor is unique to exchange spin coupling, reinforcing the crucial role of choosing a suitable Descriptor, and highlighting the interesting question of the role of chemical intuition vs. systematic or automated selection of features for machine learning in chemistry and material science.
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Exchange Spin Coupling fromGaussian Progress Regression
2020Co-Authors: Marc Philipp Bahlke, Natnael Mogos, Jonny Proppe, Carmen HerrmannAbstract:Heisenberg exchange spin coupling between metal centers is essential for describing and understanding the electronic structure of many molecular catalysts, metalloenzymes, and molecular magnets for potential application in information technology. We explore the machine-learnability of exchange spin coupling, which has not been studied yet. We employ Gaussian process regression since it can potentially deal with small training sets (as likely associated with the rather complex molecular structures required for exploring spin coupling) and since it provides uncertainty estimates (“error bars”) along with predicted values. We compare a range of Descriptors and kernels for 257 small dicopper complexes and find that a Simple Descriptor based on chemical intuition, consisting only of copper-bridge angles and copper-copper distances, clearly outperforms several more sophisticated Descriptors when it comes to extrapolating towards larger experimentally relevant complexes. Exchange spin coupling is similarly easy to learn as the polarizability, while learning dipole moments is much harder. The strength of the sophisticated Descriptors lies in their ability to linearize structure-property relationships, to the point that a Simple linear ridge regression performs just as well as the kernel-based machine-learning model for our small dicopper data set. The superior extrapolation performance of the Simple Descriptor is unique to exchange spin coupling, reinforcing the crucial role of choosing a suitable Descriptor, and highlighting the interesting question of the role of chemical intuition vs. systematic or automated selection of features for machine learning in chemistry and material science.
Wan-jian Yin - One of the best experts on this subject based on the ideXlab platform.
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Simple Descriptor derived from symbolic regression accelerating the discovery of new perovskite catalysts.
Nature Communications, 2020Co-Authors: Baicheng Weng, Zhilong Song, Rilong Zhu, Qingyu Yan, Qingde Sun, Corey G. Grice, Yanfa Yan, Wan-jian YinAbstract:Symbolic regression (SR) is an approach of interpretable machine learning for building mathematical formulas that best fit certain datasets. In this work, SR is used to guide the design of new oxide perovskite catalysts with improved oxygen evolution reaction (OER) activities. A Simple Descriptor, μ/t, where μ and t are the octahedral and tolerance factors, respectively, is identified, which accelerates the discovery of a series of new oxide perovskite catalysts with improved OER activity. We successfully synthesise five new oxide perovskites and characterise their OER activities. Remarkably, four of them, Cs0.4La0.6Mn0.25Co0.75O3, Cs0.3La0.7NiO3, SrNi0.75Co0.25O3, and Sr0.25Ba0.75NiO3, are among the oxide perovskite catalysts with the highest intrinsic activities. Our results demonstrate the potential of SR for accelerating the data-driven design and discovery of new materials with improved properties.
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Symbolic Regression Discovery of New Perovskite Catalysts with High Oxygen Evolution Reaction Activity
arXiv: Materials Science, 2019Co-Authors: Baicheng Weng, Zhilong Song, Rilong Zhu, Qingyu Yan, Qingde Sun, Corey G. Grice, Yanfa Yan, Wan-jian YinAbstract:Symbolic regression (SR) is an emerging method for building analytical formulas to find models that best fit data sets. Here, SR was used to guide the design of new oxide perovskite catalysts with improved oxygen evolution reaction (OER) activities. An unprecedentedly Simple Descriptor, {\mu}/t, where {\mu} and t are the octahedral and tolerance factors, respectively, was identified, which accelerated the discovery of a series of new oxide perovskite catalysts with improved OER activity. We successfully synthesized five new oxide perovskites and characterized their OER activities. Remarkably, four of them, Cs0.4La0.6Mn0.25Co0.75O3, Cs0.3La0.7NiO3, SrNi0.75Co0.25O3, and Sr0.25Ba0.75NiO3, outperform the current state-of-the-art oxide perovskite catalyst, Ba0.5Sr0.5Co0.8Fe0.2O3 (BSCF). Our results demonstrate the potential of SR for accelerating data-driven design and discovery of new materials with improved properties.
Cheng-wen Wu - One of the best experts on this subject based on the ideXlab platform.
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Scalable Security Processor Design and Its Implementation
2005 IEEE Asian Solid-State Circuits Conference, 2005Co-Authors: Chen-hsing Wang, Chih-tsun Huang, Cheng-wen WuAbstract:This paper presents a security processor to accelerate cryptographic processing in modern security applications. Our security processor is capable of popular cryptographic functions such as RSA, AES, hashing and random number generation, etc. With proposed crypto-DMA controller, data gathering and scattering become flexible for security processing, using a Simple Descriptor-based programming model. The architecture of the security processor with its core-based platform is scalable and configurable for security variations in performance, cost and power consumption. Different number of data channels and crypto-engines can be used to meet the specifications. In addition, a DFT (design for test) platform is also implemented for the design-test integration. The security processor has been fabricated (using UMC 0.18mum CMOS technology) and measured. The core area is 3.899mm times 2.296mm (525K gates approximately) and the operating clock rate is 66MHz
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ASP-DAC - Design and test of a scalable security processor
Proceedings of the 2005 conference on Asia South Pacific design automation - ASP-DAC '05, 2005Co-Authors: Chih-pin Su, Chen-hsing Wang, Chih-tsun Huang, Kuo-liang Cheng, Cheng-wen WuAbstract:This paper presents a security processor to accelerate cryptographic processing in modern security applications. Our security processor is capable of popular cryptographic functions such as RSA, AES, hashing and random number generation, etc. With proposed crypto-DMA controller, data gathering and scattering become flexible for security processing, using a Simple Descriptor-based programming model. The architecture of the security processor with its core-based platform is scalable and configurable for security variations in performance, cost and power consumption. Different number of data channels and crypto-engines can be used to meet the specifications. In addition, a DFT platform is also implemented for the design-test integration. The security processor has been fabricated with 0.18/spl mu/m CMOS technology. The core area is 3.899mm /spl times/ 2.296mm (525K gates approximately) and the operating clock rate is 83MHz.
Marc Philipp Bahlke - One of the best experts on this subject based on the ideXlab platform.
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Exchange Spin Coupling from Gaussian Process Regression.
Journal of Physical Chemistry A, 2020Co-Authors: Marc Philipp Bahlke, Natnael Mogos, Jonny Proppe, Carmen HerrmannAbstract:Heisenberg exchange spin coupling between metal centers is essential for describing and understanding the electronic structure of many molecular catalysts, metalloenzymes, and molecular magnets for potential application in information technology. We explore the machine-learnability of exchange spin coupling beyond linear regression, which has not been studied yet. We employ Gaussian process regression, since it can potentially deal with small training sets (as likely associated with the rather complex molecular structures required for exploring spin coupling) and since it provides uncertainty estimates ("error bars") along with predicted values. We compare a range of Descriptors and kernels for 257 small dicopper complexes and find that a Simple Descriptor based on chemical intuition, consisting only of copper-bridge angles and copper-copper distances, clearly outperforms several more sophisticated Descriptors when it comes to extrapolating toward larger experimentally relevant complexes. Exchange spin coupling is similarly easy to learn as the polarizability, while learning dipole moments is much harder. The strength of the sophisticated Descriptors lies in their ability to linearize structure-property relationships, to the point that a Simple linear ridge regression performs just as well as the kernel-based machine-learning model for our small dicopper data set. The superior extrapolation performance of the Simple Descriptor is unique to exchange spin coupling, reinforcing the crucial role of choosing a suitable Descriptor and highlighting the interesting question of the role of chemical intuition vs systematic or automated selection of features for machine learning in chemistry and material science.
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Exchange Spin Coupling from Gaussian Process Regression
2020Co-Authors: Marc Philipp Bahlke, Natnael Mogos, Jonny Proppe, Carmen HerrmannAbstract:Heisenberg exchange spin coupling between metal centers is essential for describing and understanding the electronic structure of many molecular catalysts, metalloenzymes, and molecular magnets for potential application in information technology. We explore the machine-learnability of exchange spin coupling, which has not been studied yet. We employ Gaussian process regression since it can potentially deal with small training sets (as likely associated with the rather complex molecular structures required for exploring spin coupling) and since it provides uncertainty estimates (“error bars”) along with predicted values. We compare a range of Descriptors and kernels for 257 small dicopper complexes and find that a Simple Descriptor based on chemical intuition, consisting only of copper-bridge angles and copper-copper distances, clearly outperforms several more sophisticated Descriptors when it comes to extrapolating towards larger experimentally relevant complexes. Exchange spin coupling is similarly easy to learn as the polarizability, while learning dipole moments is much harder. The strength of the sophisticated Descriptors lies in their ability to linearize structure-property relationships, to the point that a Simple linear ridge regression performs just as well as the kernel-based machine-learning model for our small dicopper data set. The superior extrapolation performance of the Simple Descriptor is unique to exchange spin coupling, reinforcing the crucial role of choosing a suitable Descriptor, and highlighting the interesting question of the role of chemical intuition vs. systematic or automated selection of features for machine learning in chemistry and material science.
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Exchange Spin Coupling fromGaussian Progress Regression
2020Co-Authors: Marc Philipp Bahlke, Natnael Mogos, Jonny Proppe, Carmen HerrmannAbstract:Heisenberg exchange spin coupling between metal centers is essential for describing and understanding the electronic structure of many molecular catalysts, metalloenzymes, and molecular magnets for potential application in information technology. We explore the machine-learnability of exchange spin coupling, which has not been studied yet. We employ Gaussian process regression since it can potentially deal with small training sets (as likely associated with the rather complex molecular structures required for exploring spin coupling) and since it provides uncertainty estimates (“error bars”) along with predicted values. We compare a range of Descriptors and kernels for 257 small dicopper complexes and find that a Simple Descriptor based on chemical intuition, consisting only of copper-bridge angles and copper-copper distances, clearly outperforms several more sophisticated Descriptors when it comes to extrapolating towards larger experimentally relevant complexes. Exchange spin coupling is similarly easy to learn as the polarizability, while learning dipole moments is much harder. The strength of the sophisticated Descriptors lies in their ability to linearize structure-property relationships, to the point that a Simple linear ridge regression performs just as well as the kernel-based machine-learning model for our small dicopper data set. The superior extrapolation performance of the Simple Descriptor is unique to exchange spin coupling, reinforcing the crucial role of choosing a suitable Descriptor, and highlighting the interesting question of the role of chemical intuition vs. systematic or automated selection of features for machine learning in chemistry and material science.