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A practical guide to machine learning interatomic potentials – Status and future

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arXiv 2025年

作者： Jacobs, Ryan Morgan, Dane Attarian, Siamak Meng, Jun Shen, Chen Wu, Zhenghao Xie, Clare Yijia Yang, Julia H. Artrith, Nongnuch Blaiszik, Ben Ceder, Gerbrand Choudhary, Kamal Csanyi, Gabor Cubuk, Ekin Dogus Deng, Bowen Drautz, Ralf Fu, Xiang Godwin, Jonathan Honavar, Vasant Isayev, Olexandr Johansson, Anders Kozinsky, Boris Martiniani, Stefano Ong, Shyue Ping Poltavsky, Igor Schmidt, K.J. Takamoto, So Thompson, Aidan Westermayr, Julia Wood, Brandon M. Department of Materials Science and Engineering University of Wisconsin-Madison MadisonWI55705 United States Harvard University Center for the Environment Harvard University CambridgeMA02138 United States John A. Paulson School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States Materials Chemistry and Catalysis Debye Institute for Nanomaterials Science Utrecht University Utrecht3584 CG Netherlands Globus University of Chicago ChicagoIL United States Data Science and Learning Division Argonne National Laboratory LemontIL United States Department of Materials Science and Engineering University of California BerkeleyCA94720 United States Materials Sciences Division Lawrence Berkeley National Laboratory CA94720 United States Material Measurement Laboratory National Institute of Standards and Technology GaithersburgMD20899 United States Department of Engineering University of Cambridge CambridgeCB2 1PZ United Kingdom Google DeepMind Mountain ViewCA United States Ruhr-Universität Bochum Bochum44780 Germany Meta United States Orbital Materials London United Kingdom Department of Computer Science and Engineering The Pennsylvania State University University ParkPA United States College of Information Sciences and Technology The Pennsylvania State University University ParkPA United States Artificial Intelligence Research Laboratory The Pennsylvania State University University ParkPA United States Center for Artificial Intelligence Foundations and Scientific Applications The Pennsylvania State University University ParkPA United States Department of Chemistry Mellon College of Science Carnegie Mellon University PittsburghPA15213 United States Computational Biology Department School of Computer Science Carnegie Mellon University PittsburghPA15213 United States Courant Institute of Mathematical Sciences New York University New YorkNY10003 United States Center for Soft Matter Research Department of P

The rapid development and large body of literature on machine learning interatomic potentials (MLIPs) can make it difficult to know how to proceed for researchers who are not experts but wish to use these tools. The spirit of this review is to help such researchers by serving as a practical, accessible guide to the state-of-the-art in MLIPs. This review paper covers a broad range of topics related to MLIPs, including (i) central aspects of how and why MLIPs are enablers of many exciting advancements in molecular modeling, (ii) the main underpinnings of different types of MLIPs, including their basic structure and formalism, (iii) the potentially transformative impact of universal MLIPs for both organic and inorganic systems, including an overview of the most recent advances, capabilities, downsides, and potential applications of this nascent class of MLIPs, (iv) a practical guide for estimating and understanding the execution speed of MLIPs, including guidance for users based on hardware availability, type of MLIP used, and prospective simulation size and time, (v) a manual for what MLIP a user should choose for a given application by considering hardware resources, speed requirements, energy and force accuracy requirements, as well as guidance for choosing pre-trained potentials or fitting a new potential from scratch, (vi) discussion around MLIP infrastructure, including sources of training data, pre-trained potentials, and hardware resources for training, (vii) summary of some key limitations of present MLIPs and current approaches to mitigate such limitations, including methods of including long-range interactions, handling magnetic systems, and treatment of excited states, and finally (viii) we finish with some more speculative thoughts on what the future holds for the development and application of MLIPs over the next 3-10+ years. © 2025, CC BY.

关键词： Digital storage

Numerical Computation, Symbolic Computation and Result Analysis of Jordan Decomposition of Time-Varying Matrices

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Numerical Computation, Symbolic Computation and Result Analy...

IEEE International Conference on Mechatronics and Automation

作者： Zhuoheng Zhen Yunong Zhang Xiao Liu Yihong Ling Min Yang School of Data and Computer Science Sun Yat-sen University (SYSU) Guangzhou China Research Institute of Sun Yat-sen University in Shenzhen Shenzhen China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangzhou China School of Electronics and Information Technology Sun Yat-sen University (SYSU) Guangzhou China

ISBN: (数字)9781728164168

ISBN: (纸本)9781728164175

Jordan decomposition is a special form of matrix decomposition. As a method applicable to general square matrix, Jordan decomposition is capable of finding a similar matrix that is diagonal or almost diagonal, called the Jordan normal form. The problem of Jordan decomposition can be solved by computer with certain algorithms. Some of them are classified as numerical methods, which usually perform on constant matrices. Others are symbolic computation methods, which can perform Jordan decomposition on matrices with variables. Both of them have computational errors, which are designated as static errors. Additionally, in time-varying cases, using traditional methods above causes not only static errors, but also lag errors (i.e., dynamic errors). In this paper, numerical experiments conducted on some time-varying matrices are introduced. Then, the results are shown and analyzed to reach a conclusion.

关键词： Matrix decomposition Matlab Signal processing algorithms Signal processing Reliability theory Numerical models Neural networks

Robot Simulation and Reinforcement Learning Training Platform Based on Distributed Architecture 1

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4th International Conference on Cognitive Systems and information Processing, ICCSIP 2018

作者： Jiang, Yun-Liang Huang, Zeng-Qiang Cao, Jun-Jie Liu, Yong Ma, Xinqiang Huang, Yi School of Computer Science Hangzhou Dianzi University Hangzhou310018 China School of Information Engineering Huzhou University Huzhou313000 China Institute of Cyber Systems and Control Zhejiang University Hangzhou310027 China Institute of Intelligent Computing and Visualization Based on Big Data Chongqing University of Arts and Sciences Chongqing402160 China

ISBN: (数字)9789811379833

ISBN: (纸本)9789811379826

In recent years, reinforcement learning, which enables robots to learn previously missing abilities, plays an increasingly important role in robotics, such as learning hard-to-code behaviors or optimizing problems without an accepted closed solution. The main problem of RL in robotics is that it is expensive and takes a long time to learn and operate. Another problem: advanced robot simulators like Gazebo are inefficient and time-consuming. In order to cope with these problems, a hybrid computing platform based on traditional robot simulation architecture and distributed architecture (hereinafter referred to as RDTP) is proposed in this paper, which helps to save cost, shorten time and speed up simulation and training. Additionally, the platform is optimized to a certain extent in terms of ease of use and compatibility. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Reinforcement learning

RETRACTED ARTICLE: SmartPharma: Blockchain Enabled Internet of Things for Smart Pharmaceutical Traceability System

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IETE Journal of Research 2023年第11期69卷 XLIV-LVII页

作者： M. Kavitha Margret E. Golden Julie Y. Harold Robinson D. Vijayanandh S. Vimal Seifidine Kadry Ahmed M. El-Sherbeeny Mohammed A. El-Meligy 1 Department of Computer Science and Engineering Sri Krishna College of Technology Coimbatore India 2 Department of Computer Science and Engineering Anna University Regional Campus Tirunelveli India 3 School of Information Technology and Engineering Vellore Institute of Technology Vellore India 4 Department of Electrical and Electronics Engineering Hindustan College of Engineering and Technology Coimbatore India 5 Department of Artificial Intelligence and Data Science Ramco Institute of Technology Rajapalayam Tamilnadu India 6 Faculty of Applied Computing and Technology Noroff University College Kristiansand Norway 7 Industrial Engineering Department College of Engineering King Saud University Riyadh11421 Saudi Arabia

We, the Editor, Institution and Publisher of the journal IETE Journal of Research, have retracted the following article, which is part of the Special Issue titled “Federated Learning for Blockchain Systems and Industrial Internet of Things”:M. Kavitha Margret, E. Golden Julie, Y. Harold Robinson, D. Vijayanandh, S. Vimal, Seifidine Kadry, Ahmed M. El-Sherbeeny & Mohammed A. El-Meligy (2021) SmartPharma: Blockchain Enabled Internet of Things for Smart Pharmaceutical Traceability System, IETE Journal of Research, DOI: 10.1080/03772063.2021.1994041Following publication, concerns were raised about the peer review and decision-making processes for this special *** an investigation by the Taylor & Francis Publishing Ethics & Integrity team, in full cooperation with the Editor-in-Chief and the Institution, it was confirmed that the articles included in this special issue were not reviewed appropriately, in line with the Journal's peer review standards and *** the stringency of the peer review process is core to the integrity of the publication process, the Editor and Publisher have decided to retract all of the articles within the above-named Special *** journal has not confirmed if the authors were aware of this compromised peer review *** journal is committed to correcting the scientific record and will fully cooperate with any institutional investigations into this matter. The authors have been informed of this *** have been informed in our decision-making by our editorial policies and the COPE *** retracted articles will remain online to maintain the scholarly record, but they will be digitally watermarked on each page as ‘Retracted’.

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Roadmap on data-Centric Materials science

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arXiv 2024年

作者： Bauer, Stefan Benner, Peter Bereau, Tristan Blum, Volker Boley, Mario Carbogno, Christian Catlow, C.A. Richard Dehm, Gerhard Eibl, Sebastian Ernstorfer, Ralph Fekete, Ádám Foppa, Lucas Fratzl, Peter Freysoldt, Christoph Gault, Baptiste Ghiringhelli, Luca M. Giri, Sajal K. Gladyshev, Anton Goyal, Pawan Hattrick-Simpers, Jason Kabalan, Lara Karpov, Petr Khorrami, Mohammad S. Koch, Christoph Kokott, Sebastian Kosch, Thomas Kowalec, Igor Kremer, Kurt Leitherer, Andreas Li, Yue Liebscher, Christian H. Logsdail, Andrew J. Lu, Zhongwei Luong, Felix Marek, Andreas Merz, Florian Mianroodi, Jaber R. Neugebauer, Jörg Pei, Zongrui Purcell, Thomas A.R. Raabe, Dierk Rampp, Markus Rossi, Mariana Rost, Jan-Michael Saal, James Saalmann, Ulf Sasidhar, Kasturi Narasimha Saxena, Alaukik Sbailò, Luigi Scheidgen, Markus Schloz, Marcel Schmidt, Daniel F. Teshuva, Simon Trunschke, Annette Wei, Ye Weikum, Gerhard Xian, R. Patrick Yao, Yi Yin, Junqi Zhao, Meng Scheffler, Matthias School of Computation Information and Technology Technical University of Munich & Helmholtz AI Munich Germany Max Planck Institute for Dynamics of Complex Technical Systems Magdeburg Germany Institute for Theoretical Physics Heidelberg University Heidelberg Germany Thomas Lord Department of Mechanical Engineering and Materials Science Duke University DurhamNC United States Monash University Department of Data Science and AI Melbourne Australia The NOMAD Laboratory The FHI of the Max-Planck-Gesellschaft IRIS-Adlershof of the Humboldt-Universität zu Berlin Berlin Germany School of Chemistry Cardiff University Cardiff United Kingdom Max-Planck-Institut für Eisenforschung GmbH Düsseldorf Germany Max Planck Computing and Data Facility Garching Germany Institute for Optics and Atomic Physics Technical University of Berlin Berlin Germany Physics Department and IRIS-Adlershof Humboldt Universität zu Berlin Berlin Germany Max Planck Institute of Colloids and Interfaces Potsdam Germany Department of Materials Imperial College London United Kingdom Department of Materials Science and Engineering Friedrich-Alexander Universität Erlangen-Nürnberg Germany Department of Chemistry Northwestern University Illinois United States Humboldt-Universität zu Berlin Department of Physics Berlin Germany Department of Materials Science and Engineering University of Toronto Toronto Canada STFC Hartree Centre Daresbury Laboratory Daresbury Warrington United Kingdom Molecular Simulations from First Principles e.V. Berlin Germany Humboldt-Universität zu Berlin Department of Computer Science Berlin Germany Max Planck Institute for Polymer Research Mainz Germany ICFO-Institut de Ciencies Fotoniques The Barcelona Institute of Science and Technology Castelldefels Barcelona Spain Lenovo HPC Innovation Center Stuttgart Germany New York University New York United States University of Arizona Biochemistry Department Arizona United States Max Planck Institute for the Structure an

science is and always has been based on data, but the terms ‘data-centric’ and the ‘4th paradigm’ of materials research indicate a radical change in how information is retrieved, handled and research is performed. It signifies a transformative shift towards managing vast data collections, digital repositories, and innovative data analytics methods. The integration of Artificial Intelligence (AI) and its subset Machine Learning (ML), has become pivotal in addressing all these challenges. This Roadmap on data-Centric Materials science explores fundamental concepts and methodologies, illustrating diverse applications in electronic-structure theory, soft matter theory, microstructure research, and experimental techniques like photoemission, atom probe tomography, and electron microscopy. While the roadmap delves into specific areas within the broad interdisciplinary field of materials science, the provided examples elucidate key concepts applicable to a wider range of topics. The discussed instances offer insights into addressing the multifaceted challenges encountered in contemporary materials research. © 2024, CC BY.

关键词： data Analytics

Computational Approaches to Explainable Artificial Intelligence: Advances Intheory, Applications and Trends

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SSRN

SSRN 2023年

作者： Górriz, Juan Manuel Álvarez-Illán, I. Álvarez-Marquina, A. Arco, J.E. Atzmueller, M. Ballarini, F. Barakova, E. Bologna, G. Bonomini, P. Castellanos-Dominguez, G. Castillo-Barnes, D. Cho, S.B. Contreras, R. Cuadra, J.M. Domínguez, E. Domínguez-Mateos, F. Duro, R.J. Elizondo, D. Fernández-Caballero, A. Fernandez-Jover, E. Formoso, M.A. Gallego-Molina, N.J. Gamazo, J. García González, J. Garcia-Rodriguez, J. Garre, C. Garrigós, J. Gómez-Rodellar, A. Gómez-Vilda, P. Graña, M. Guerrero-Rodriguez, B. Hendrikse, S.C.F. Jimenez-Mesa, C. Jodra-Chuan, M. Julian, V. Kotz, G. Kutt, K. Leming, M. de Lope, J. Macas, B. Marrero-Aguiar, V. Martinez, J.J. Martinez-Murcia, F.J. Martínez-Tomás, R. Mekyska, J. Nalepa, G.J. Novais, P. Orellana, D. Ortiz, A. Palacios-Alonso, D. Palma, J. Pereira, A. Pinacho-Davidson, P. Pinninghoff, M.A. Ponticorvo, M. Psarrou, A. Ramírez, J. Rincón, M. Rodellar-Biarge, V. Rodríguez-Rodríguez, I. Roelofsma, P.H.M.P. Santos, J. Salas-Gonzalez, D. Salcedo-Lagos, P. Segovia, F. Shoeibi, A. Silva, M. Simic, D. Suckling, J. Treur, J. Tsanas, A. Varela, R. Wang, S.H. Wang, W. Zhang, Y.D. Zhu, H. Zhu, Z. Ferrández-Vicente, J.M. SiPBA at Data Science and Computational Intelligence Institute University of Granada Granada Spain University of Granada Granada Spain Dept. of Psychiatry University of Cambridge Cambridge United Kingdom Dept. of Communications Engineering University of Malaga Malaga Spain School of Computing and Mathematical Sciences University of Leicester Leicester United Kingdom Semantic Information Systems Group Osnabrück University Osnabrück Germany Osnabrück Germany Dept. of Electronics Computer Technology and Projects Universidad Politécnica de Cartagena Cartagena Spain Universitat Politècnica de València València Spain Institute of Applied Computer Science Jagiellonian University Kraków Poland Dept. of Information and Communication Engineering University of Murcia Murcia Spain Dept. of Computer Science and Information Technologies University of A Coruña A Coruña Spain Dept. of Computer Science University of Oviedo Oviedo Spain Buenos Aires Argentina Buenos Aires Argentina Eindhoven University of Technology Eindhoven5612 AZ Netherlands Dept. of Computer Science University of Malaga Malaga Spain De Montfort University Leicester United Kingdom Dept. of Computer Science Faculty of Engineering Universidad de Concepción Concepción Chile Neurocognition and Emotion Research Unit Albacete Research Institute of Informatics University of Castilla-La Mancha Albacete Spain Dept. of Educational Informatics Universidad de Concepción Concepción Chile Madrid Spain Dept. of Humanistic Studies University of Naples Federico II Napoli Italy Escuela Técnica Superior de Ingeniería Informática Universidad Rey Juan Carlos Campus de Móstoles Móstoles Madrid28933 Spain Neuromorphic Speech Processing Lab Center for Biomedical Technology Universidad Politécnica de Madrid Campus de Montegancedo Pozuelo de Alarcón Madrid28223 Spain Biomedical Research Networking Center in Mental Health Instituto de Salud Carlos III Madrid28016 Spain Usher Institute Faculty o

Groundbreaking advances in theoretical and applied Artificial Intelligence (AI). DL algorithms are groundedin non-linear and complex artificial neural systems that progressively extract higher-level features from *** is frequently compared with human-level performance in real-world tasks, such as clinical diagnostics. Itis also a turning point in solving otherwise intractable problems in the design of virtual agents or robotics;orin providing novel insight into disease progress in genomics and Neuroimaging (NI), understanding humanbrain function, the automation of industrial processes based on computer vision, and so on. In this paper, themost relevant advances from the last few years in AI and several applications to neuroscience, neuroimaging,computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-ofthe-art in AI methods, models and applications within a collection of works presented at the 9th InternationalConference on the Interplay between Natural and Artificial Computation (IWINAC). The works presentedin this paper are excellent examples of new scientific discoveries made in laboratories that are successfullytransferred to real-life applications. © 2023, The Authors. All rights reserved.

关键词： Neuroimaging

Learning compact target-oriented feature representations for visual tracking

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arXiv 2019年

作者： Li, Chenglong Huang, Yan Wang, Liang Tang, Jin Lin, Liang Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Institute of Physical Science and Information Technology Anhui University Center for Research on Intelligent Perception and Computing NLPR CASIA School of Data and Computer Science Sun Yat-Sen University

Many state-of-the-art trackers usually resort to the pre-trained convolutional neural network (CNN) model for correlation filtering, in which deep features could usually be redundant, noisy and less discriminative for some certain instances, and the tracking performance might thus be affected. To handle this problem, we propose a novel approach, which takes both advantages of good generalization of generative models and excellent discrimination of discriminative models, for visual tracking. In particular, we learn compact, discriminative and target-oriented feature representations using the Laplacian coding algorithm that exploits the dependence among the input local features in a discriminative correlation filter framework. The feature representations and the correlation filter are jointly learnt to enhance to each other via a fast solver which only has very slight computational burden on the tracking speed. Extensive experiments on three benchmark datasets demonstrate that this proposed framework clearly outperforms baseline trackers with a modest impact on the frame rate, and performs comparably against the state-of-the-art methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Target tracking

The importance of environmental factors in forecasting Australian power demand

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arXiv 2019年

作者： Eshragh, Ali Ganim, Benjamin Perkins, Terry Bandara, Kasun School of Information and Physical Sciences University of Newcastle NSW Australia International Computer Science Institute BerkeleyCA United States School of Computing and Information Systems Melbourne Centre for Data Science University of Melbourne VIC Australia

We develop a time series model to forecast weekly peak power demand for three main states of Australia for a yearly time-scale, and show the crucial role of environmental factors in improving the forecasts. More precisely, we construct a seasonal autoregressive integrated moving average (SARIMA) model and reinforce it by employing the exogenous environmental variables including, maximum temperature, minimum temperature, and solar exposure. The estimated hybrid SARIMA-regression model exhibits an excellent mean absolute percentage error (MAPE) of 3.41%. Moreover, our analysis demonstrates the importance of the environmental factors by showing a remarkable improvement of 46.3% in MAPE for the hybrid model over the crude SARIMA model which merely includes the power demand variables. In order to illustrate the efficacy of our model, we compare our outcome with the state-of-the-art machine learning methods in forecasting. The results reveal that our model outperforms the latter *** Codes 62M10, 97K80 Copyright © 2019, The Authors. All rights reserved.

关键词： Electric power utilization

Correction: Sign language mobile apps: a systematic review of current app evaluation progress and solution framework

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Evolving Systems 2024年第5期15卷 1989-1989页

作者： David, Dianes Alamoodi, A. H. Albahri, O. S. Garfan, Salem Albahri, A. S. Zaidan, B. B. Chen, Juliana Faculty of Computing and Meta-Technology (FKMT) Universiti Pendidikan Sultan Idris (UPSI) Perak Malaysia MEU Research Unit Middle East University Amman Jordan Applied Science Research Center Applied Science Private University Amman Jordan Department of Computer Science and Information Technology La Trobe University Melbourne Australia Computer Techniques Engineering Department Mazaya University College Nasiriyah Iraq Iraqi Commission for Computers and Informatics (ICCI) Baghdad Iraq Technical College Imam Ja’afar Al-Sadiq University Baghdad Iraq Future Technology Research Center National Yunlin University of Science and Technology Douliou Taiwan ROC Discipline of Nutrition and Dietetics Susan Wakil School of Nursing and Midwifery Faculty of Medicine and Health The University of Sydney Camperdown Australia Charles Perkins Centre The University of Sydney Camperdown Australia