[논문]Tuplewise Material Representation Based Machine Learning for Accurate Band Gap Prediction

Na, Gyoung S.; Jang, Seunghun; Lee, Yea-Lee; Chang, Hyunju

doi:10.1021/acs.jpca.0c07802

[해외논문] Tuplewise Material Representation Based Machine Learning for Accurate Band Gap Prediction

The journal of physical chemistry. A, Molecules, spectroscopy, kinetics, environment & general theory, v.124 no.50, 2020년, pp.10616 - 10623

Na, Gyoung S. (Chemical Data-Driven Research Center , Korea Research Institute of Chemical Technology (KRICT) , Daejeon 34114 , Korea) , Jang, Seunghun (Chemical Data-Driven Research Center , Korea Research Institute of Chemical Technology (KRICT) , Daejeon 34114 , Korea) , Lee, Yea-Lee , Chang, Hyunju

Abstract ▼ AI-Helper

The open-access material databases allowed us to approach scientific questions from a completely new perspective with machine learning methods. Here, on the basis of open-access databases, we focus on the classical band gap problem for predicting accurately the band gap of a crystalline compound using a machine learning approach with newly developed tuplewise graph neural networks (TGNN), which is devised to automatically generate input representation of crystal structures in tuple types and to exploit crystal-level properties as one of the input features. Our method brings about a highly accurate prediction of the band gaps at hybrid functionals and GW approximation levels for multiple material data sets without heavy computational cost. Furthermore, to demonstrate the applicability of our prediction model, we provide a data set of GW band gaps for 45835 materials predicted by TGNN posing higher accuracy than standard density functional theory calculations.[FIG OMISSION]

참고문헌 (63)

Sham, L. J., Schlüter, M.. Density-Functional Theory of the Energy Gap. Physical review letters, vol.51, no.20, 1888-1891.

상세보기
Sham, L. J., Schlüter, M.. Density-functional theory of the band gap. Physical review. B, Condensed matter, vol.32, no.6, 3883-3889.

상세보기
Electronic Structure: Basic Theory and Practical Methods Martin R. M. 2004 10.1017/CBO9780511805769
Fundamentals of Condensed Matter Physics Cohen M. L. 2016 10.1017/CBO9781139031783
Perdew, John P., Yang, Weitao, Burke, Kieron, Yang, Zenghui, Gross, Eberhard K. U., Scheffler, Matthias, Scuseria, Gustavo E., Henderson, Thomas M., Zhang, Igor Ying, Ruzsinszky, Adrienn, Peng, Haowei, Sun, Jianwei, Trushin, Egor, Gorling, Andreas. Understanding band gaps of solids in generalized Kohn–Sham theory. Proceedings of the National Academy of Sciences of the United States of America, vol.114, no.11, 2801-2806.

상세보기
Physics of Semiconductor Devices Sze S. M. 2006 10.1002/0470068329
Kohn, W., Sham, L. J.. Self-Consistent Equations Including Exchange and Correlation Effects. Physical review, vol.140, no.4, A1133-A1138.

상세보기
Perdew, John P., Burke, Kieron, Ernzerhof, Matthias. Generalized Gradient Approximation Made Simple. Physical review letters, vol.77, no.18, 3865-3868.

상세보기
Perdew, John P., Levy, Mel. Physical Content of the Exact Kohn-Sham Orbital Energies: Band Gaps and Derivative Discontinuities. Physical review letters, vol.51, no.20, 1884-1887.

상세보기
Perdew, John P., Ernzerhof, Matthias, Burke, Kieron. Rationale for mixing exact exchange with density functional approximations. The Journal of chemical physics, vol.105, no.22, 9982-9985.

상세보기
Heyd, Jochen, Scuseria, Gustavo E., Ernzerhof, Matthias. Hybrid functionals based on a screened Coulomb potential. The Journal of chemical physics, vol.118, no.18, 8207-8215.

상세보기
Krukau, Aliaksandr V., Vydrov, Oleg A., Izmaylov, Artur F., Scuseria, Gustavo E.. Influence of the exchange screening parameter on the performance of screened hybrid functionals. The Journal of chemical physics, vol.125, no.22, 224106-.

상세보기
10.1016/S0081-1947(08)60615-3
Hybertsen, Mark S., Louie, Steven G.. Electron correlation in semiconductors and insulators: Band gaps and quasiparticle energies. Physical review. B, Condensed matter, vol.34, no.8, 5390-5413.

상세보기
Shirley, Eric L.. Self-consistentGWand higher-order calculations of electron states in metals. Physical review. B, Condensed matter, vol.54, no.11, 7758-7764.

상세보기
van Schilfgaarde, M., Kotani, Takao, Faleev, S.. Quasiparticle Self-Consistent $ GW$ Theory. Physical review letters, vol.96, no.22, 226402-.

상세보기
Kotani, Takao, van Schilfgaarde, Mark, Faleev, Sergey V.. Quasiparticle self-consistentGWmethod: A basis for the independent-particle approximation. Physical review. B, Condensed matter and materials physics, vol.76, no.16, 165106-.

상세보기
Tran, Fabien, Blaha, Peter. Accurate Band Gaps of Semiconductors and Insulators with a Semilocal Exchange-Correlation Potential. Physical review letters, vol.102, no.22, 226401-.

상세보기
Chan, M. K. Y., Ceder, G.. Efficient Band Gap Prediction for Solids. Physical review letters, vol.105, no.19, 196403-.

상세보기
Sun, Jianwei, Remsing, Richard C., Zhang, Yubo, Sun, Zhaoru, Ruzsinszky, Adrienn, Peng, Haowei, Yang, Zenghui, Paul, Arpita, Waghmare, Umesh, Wu, Xifan, Klein, Michael L., Perdew, John P.. Accurate first-principles structures and energies of diversely bonded systems from an efficient density functional. Nature chemistry, vol.8, no.9, 831-836.

상세보기
Gritsenko, Oleg, van Leeuwen, Robert, van Lenthe, Erik, Baerends, Evert Jan. Self-consistent approximation to the Kohn-Sham exchange potential. Physical review. A. Atomic, molecular, and optical physics, vol.51, no.3, 1944-1954.

상세보기
Crowley, Jason M., Tahir-Kheli, Jamil, Goddard, William A.. Resolution of the Band Gap Prediction Problem for Materials Design. The journal of physical chemistry letters, vol.7, no.7, 1198-1203.

상세보기
Xiao, Hai, Tahir-Kheli, Jamil, Goddard, William A.. Accurate Band Gaps for Semiconductors from Density Functional Theory. The journal of physical chemistry letters, vol.2, no.3, 212-217.

상세보기
Jain, Anubhav, Ong, Shyue Ping, Hautier, Geoffroy, Chen, Wei, Richards, William Davidson, Dacek, Stephen, Cholia, Shreyas, Gunter, Dan, Skinner, David, Ceder, Gerbrand, Persson, Kristin A.. Commentary: The Materials Project: A materials genome approach to accelerating materials innovation. APL materials, vol.1, no.1, 011002-.

상세보기
Curtarolo, Stefano, Setyawan, Wahyu, Wang, Shidong, Xue, Junkai, Yang, Kesong, Taylor, Richard H., Nelson, Lance J., Hart, Gus L.W., Sanvito, Stefano, Buongiorno-Nardelli, Marco, Mingo, Natalio, Levy, Ohad. AFLOWLIB.ORG: A distributed materials properties repository from high-throughput ab initio calculations. Computational materials science, vol.58, 227-235.

상세보기
Saal, James E., Kirklin, Scott, Aykol, Muratahan, Meredig, Bryce, Wolverton, C.. Materials Design and Discovery with High-Throughput Density Functional Theory: The Open Quantum Materials Database (OQMD). JOM : the journal of the Minerals, Metals & Materials Society, vol.65, no.11, 1501-1509.

상세보기
Landis, D. D., Hummelshøj, J. S., Nestorov, S., Greeley, J., Dułak, M., Bligaard, T., Nørskov, J. K., Jacobsen, K. W.. The Computational Materials Repository. Computing in science & engineering, vol.14, no.6, 51-57.

상세보기
Morales-García, Ángel, Valero, Rosendo, Illas, Francesc. An Empirical, yet Practical Way To Predict the Band Gap in Solids by Using Density Functional Band Structure Calculations. The journal of physical chemistry. C, Nanomaterials and Interfaces, vol.121, no.34, 18862-18866.

상세보기
Setyawan, Wahyu, Gaume, Romain M., Lam, Stephanie, Feigelson, Robert S., Curtarolo, Stefano. High-Throughput Combinatorial Database of Electronic Band Structures for Inorganic Scintillator Materials. ACS combinatorial science, vol.13, no.4, 382-390.

상세보기
Ramakrishnan, Raghunathan, Dral, Pavlo O., Rupp, Matthias, von Lilienfeld, O. Anatole. Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach. Journal of chemical theory and computation, vol.11, no.5, 2087-2096.

상세보기
Lee, Joohwi, Seko, Atsuto, Shitara, Kazuki, Nakayama, Keita, Tanaka, Isao. Prediction model of band gap for inorganic compounds by combination of density functional theory calculations and machine learning techniques. Physical review. B, vol.93, no.11, 115104-.

상세보기
Gu, Tianhong, Lu, Wencong, Bao, Xinhua, Chen, Nianyi. Using support vector regression for the prediction of the band gap and melting point of binary and ternary compound semiconductors. Solid state sciences, vol.8, no.2, 129-136.

상세보기
Montavon, Grégoire, Rupp, Matthias, Gobre, Vivekanand, Vazquez-Mayagoitia, Alvaro, Hansen, Katja, Tkatchenko, Alexandre, Müller, Klaus-Robert, Anatole von Lilienfeld, O. Machine learning of molecular electronic properties in chemical compound space. New journal of physics, vol.15, no.9, 095003-.

상세보기
Pilania, G., Gubernatis, J.E., Lookman, T.. Multi-fidelity machine learning models for accurate bandgap predictions of solids. Computational materials science, vol.129, 156-163.

상세보기
Rajan, Arunkumar Chitteth, Mishra, Avanish, Satsangi, Swanti, Vaish, Rishabh, Mizuseki, Hiroshi, Lee, Kwang-Ryeol, Singh, Abhishek K.. Machine-Learning-Assisted Accurate Band Gap Predictions of Functionalized MXene. Chemistry of materials : a publication of the American Chemical Society, vol.30, no.12, 4031-4038.

상세보기
Dong, Yuan, Wu, Chuhan, Zhang, Chi, Liu, Yingda, Cheng, Jianlin, Lin, Jian. Bandgap prediction by deep learning in configurationally hybridized graphene and boron nitride. Npj Computational materials, vol.5, no.1, 26-.

상세보기
Zhuo, Ya, Mansouri Tehrani, Aria, Brgoch, Jakoah. Predicting the Band Gaps of Inorganic Solids by Machine Learning. The journal of physical chemistry letters, vol.9, no.7, 1668-1673.

상세보기
Xie, Tian, Grossman, Jeffrey C.. Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties. Physical review letters, vol.120, no.14, 145301-.

상세보기
10.1109/IJCNN.2000.857823 Caruana, R.; Lawrence, S.; Giles, C. L. Overfitting in Neural Nets: Backpropagation, Conjugate Gradient, and Early Stopping , 2001; pp 402-408.
10.1109/CVPR.2016.90 He, K.; Zhang, X.; Ren, S.; Sun, J. Deep Residual Learning for Image Recognition. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2016.
Isayev, Olexandr, Oses, Corey, Toher, Cormac, Gossett, Eric, Curtarolo, Stefano, Tropsha, Alexander. Universal fragment descriptors for predicting properties of inorganic crystals. Nature communications, vol.8, 15679-.

상세보기
Ghiringhelli, Luca M., Vybiral, Jan, Levchenko, Sergey V., Draxl, Claudia, Scheffler, Matthias. Big Data of Materials Science: Critical Role of the Descriptor. Physical review letters, vol.114, no.10, 105503-.

상세보기
Wu, Zhenqin, Ramsundar, Bharath, Feinberg, Evan N., Gomes, Joseph, Geniesse, Caleb, Pappu, Aneesh S., Leswing, Karl, Pande, Vijay. MoleculeNet: a benchmark for molecular machine learning. Chemical science, vol.9, no.2, 513-530.

상세보기
Kipf, T. N.; Welling, M. Semi-Supervised Classification with Graph Convolutional Networks. International Conference on Learning Representations (ICLR) , 2017.
Na, Gyoung S., Kim, Hyun Woo, Chang, Hyunju. Costless Performance Improvement in Machine Learning for Graph-Based Molecular Analysis. Journal of chemical information and modeling, vol.60, no.3, 1137-1145.

상세보기
Chen, Chi, Ye, Weike, Zuo, Yunxing, Zheng, Chen, Ong, Shyue Ping. Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals. Chemistry of materials : a publication of the American Chemical Society, vol.31, no.9, 3564-3572.

상세보기
Back, Seoin, Yoon, Junwoong, Tian, Nianhan, Zhong, Wen, Tran, Kevin, Ulissi, Zachary W.. Convolutional Neural Network of Atomic Surface Structures To Predict Binding Energies for High-Throughput Screening of Catalysts. The journal of physical chemistry letters, vol.10, 4401-4408.

상세보기
10.1109/ITW.2016.7606789 Xu, A.; Raginsky, M. Information-theoretic analysis of generalization capability of learning algorithms , 2017.
Conference on Neural Information Processing Systems (NIPS) Krogh A. 950 1992
Cortes, C.; Mohri, M.; Rostamizadeh, A. L2 Regularization for Learning Kernels. Association for Uncertainty in Artificial Intelligence (UAI) , 2009.
Kim, Chiho, Huan, Tran Doan, Krishnan, Sridevi, Ramprasad, Rampi. A hybrid organic-inorganic perovskite dataset. Scientific data, vol.4, 170057-.

상세보기
Haastrup, Sten, Strange, Mikkel, Pandey, Mohnish, Deilmann, Thorsten, Schmidt, Per S, Hinsche, Nicki F, Gjerding, Morten N, Torelli, Daniele, Larsen, Peter M, Riis-Jensen, Anders C, Gath, Jakob, Jacobsen, Karsten W, Jørgen Mortensen, Jens, Olsen, Thomas, Thygesen, Kristian S. The Computational 2D Materials Database: high-throughput modeling and discovery of atomically thin crystals. 2d materials, vol.5, no.4, 042002-.

상세보기
Castelli, Ivano E., Hüser, Falco, Pandey, Mohnish, Li, Hong, Thygesen, Kristian S., Seger, Brian, Jain, Anubhav, Persson, Kristin A., Ceder, Gerbrand, Jacobsen, Karsten W.. New Light‐Harvesting Materials Using Accurate and Efficient Bandgap Calculations. Advanced energy materials, vol.5, no.2, 1400915-.

상세보기
Kuisma, M., Ojanen, J., Enkovaara, J., Rantala, T. T.. Kohn-Sham potential with discontinuity for band gap materials. Physical review. B, Condensed matter and materials physics, vol.82, no.11, 115106-.

상세보기
https://xgboost.readthedocs.io.
Rothschild, Daphna, Weissbrod, Omer, Barkan, Elad, Kurilshikov, Alexander, Korem, Tal, Zeevi, David, Costea, Paul I., Godneva, Anastasia, Kalka, Iris N., Bar, Noam, Shilo, Smadar, Lador, Dar, Vila, Arnau Vich, Zmora, Niv, Pevsner-Fischer, Meirav, Israeli, David, Kosower, Noa, Malka, Gal, Wolf, Bat Chen, Avnit-Sagi, Tali, Lotan-Pompan, Maya, Weinberger, Adina, Halpern, Zamir, Carmi, Shai, Fu, Jingyuan, Wijmenga, Cisca, Zhernakova, Alexandra, Elinav, Eran, Segal, Eran. Environment dominates over host genetics in shaping human gut microbiota. Nature, vol.555, no.7695, 210-215.

상세보기
Chen, Xing, Huang, Li, Xie, Di, Zhao, Qi. EGBMMDA: Extreme Gradient Boosting Machine for MiRNA-Disease Association prediction. Cell death & disease, vol.9, no.1, 3-.

상세보기
Zhang, Dahai, Qian, Liyang, Mao, Baijin, Huang, Can, Huang, Bin, Si, Yulin. A Data-Driven Design for Fault Detection of Wind Turbines Using Random Forests and XGboost. IEEE access : practical research, open solutions, vol.6, 21020-21031.

상세보기
https://scikit-learn.org.
https://pytorch.org.
Murray, Éamonn D., Lee, Kyuho, Langreth, David C.. Investigation of Exchange Energy Density Functional Accuracy for Interacting Molecules. Journal of chemical theory and computation, vol.5, no.10, 2754-2762.

상세보기
Tran, Fabien, Ehsan, Sohaib, Blaha, Peter. Assessment of the GLLB-SC potential for solid-state properties and attempts for improvement. Physical review materials, vol.2, no.2, 023802-.

상세보기
Neurips Allen-Zhu Z. 6158 2019

LOADING...

활용도 분석정보

상세보기

다운로드

내보내기

활용도 Top5 논문

해당 논문의 주제분야에서 활용도가 높은 상위 5개 콘텐츠를 보여줍니다.
더보기 버튼을 클릭하시면 더 많은 관련자료를 살펴볼 수 있습니다.

표제어: PCR

동의어: Packet Collision Rate

용어 설명 출처 목록 (6)

용어 설명: PCR은 세균 특이성이 있는 primer를 이용하여 적은 수의 세균이 있을지라도 쉽게 검출할 수 있는 유용한 방법이며, 이를 이용하여 구강 내 치면세균막이나 타액에서 직접 세균을 검출할 수 있게 되었다[8].

내보내기 구분	파일저장 인쇄 메일전송
구성항목	기본정보 상세정보 관리번호, 논문명, 저널/프로시딩명, 저자 , 발행년, 권, 호, 시작페이지, 끝페이지, 발행기관 관리번호, 논문명, 대등논문명, 저자 , 저널/프로시딩명, 발행기관, 발행년, 발행언어, 권, 호, 시작페이지, 끝페이지, ISBN, ISSN, 주제분야, 키워드, 초록(한글), 초록(영문), 저자(소속기관)
저장형식	Text(ASCII format) Excel format RefWorks Direct Export RIS format (for Reference Manager, ProCite, EndNote), Scholar's Aids, Mendeley
메일정보	받는사람 (필수) @ 보내는사람 (선택) @ 제목 내용 KISTI 검색결과 이메일 서비스
안내	총 건의 자료가 검색되었습니다. 다운받으실 자료의 인덱스를 입력하세요. (1-10,000) 검색결과의 순서대로 최대 10,000건 까지 다운로드가 가능합니다. 데이타가 많을 경우 속도가 느려질 수 있습니다.(최대 2~3분 소요) 다운로드 파일은 UTF-8 형태로 저장됩니다. 파일의 내용이 제대로 보이지 않을실 때는 웹브라우저 상단의 보기 -> 인코딩 -> 자동선택 여부를 확인하십시오. ~ Text(ASCII format) Excel format

연합인증