Marián Rynik

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All published works

Action	Title	Year	Authors
+ PDF Chat	DeePMD-kit v2: A software package for deep potential models	2023	Jinzhe Zeng Duo Zhang Denghui Lu Pinghui Mo Zeyu Li Yixiao Chen Marián Rynik Liang Huang Ziyao Li Shaochen Shi
+	DeePMD-kit v2: A software package for Deep Potential models	2023	Jinzhe Zeng Duo Zhang Denghui Lu Pinghui Mo Zeyu Li Yixiao Chen Marián Rynik Li'ang Huang Ziyao Li Shaochen Shi

Common Coauthors

Coauthor	Papers Together
Duo Zhang	2
Denghui Lu	2
Ping Tuo	2
Roberto Car	2
Sigbjørn Løland Bore	2
Rhys E. A. Goodall	2
Jiequn Han	2
Renata M. Wentzcovitch	2
Koki Muraoka	2
Chenxing Luo	2
Chun Cai	2
Yin‐Nian Lin	2
Ziyao Li	2
Yingze Wang	2
Sikai Yao	2
Yixiao Chen	2
Haotian Ye	2
Fengbo Yuan	2
Jingchao Zhang	2
Jinzhe Zeng	2
Jiaxin Zhu	2
Pinghui Mo	2
Jiabin Yang	2
Shaochen Shi	2
Jiayan Xu	2
Davide Tisi	2
E Weinan	2
Darrin M. York	2
Wenshuo Liang	2
Qiyu Zeng	2
Yibo Wang	2
Yu Xia	2
Yifan Li	2
Han Wang	2
Jie Liu	2
Zeyu Li	2
Junhan Chang	2
Ye Ding	2
Yuzhi Zhang	2
Han Bao	2
Linfeng Zhang	2
Jiameng Huang	2
Anurag Kumar Singh	2
Weile Jia	2
Li'ang Huang	1
Liang Huang	1
Bo Wang	1
Bo Wang	1

Commonly Cited References

Action	Title	Year	Authors	# of times referenced
+ PDF Chat	Effective Approaches to Attention-based Neural Machine Translation	2015	Thang Luong Hieu Pham Christopher D. Manning	1
+ PDF Chat	Projector augmented-wave method	1994	Peter E. Blöchl	1
+ PDF Chat	Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons	2010	Albert P. Bartók M. C. Payne Risi Kondor Gábor Cśanyi	1
+ PDF Chat	Quantum-chemical insights from deep tensor neural networks	2017	Kristof T. Schütt Farhad Arbabzadah Stefan Chmiela K. Müller Alexandre Tkatchenko	1
+ PDF Chat	ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost	2017	Justin S. Smith Olexandr Isayev Adrián E. Roitberg	1
+ PDF Chat	Machine learning of accurate energy-conserving molecular force fields	2017	Stefan Chmiela Alexandre Tkatchenko Huziel E. Sauceda Igor Poltavsky Kristof T. Schütt Klaus‐Robert Müller	1
+ PDF Chat	Spectral neighbor analysis method for automated generation of quantum-accurate interatomic potentials	2014	Aidan P. Thompson Laura Swiler Christian Robert Trott Stephen M. Foiles Garritt J. Tucker	1
+ PDF Chat	Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics	2018	Linfeng Zhang Jiequn Han Han Wang Roberto Car E Weinan	1
+	DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics	2018	Han Wang Linfeng Zhang Jiequn Han E Weinan	1
+ PDF Chat	wACSF—Weighted atom-centered symmetry functions as descriptors in machine learning potentials	2018	Michael Gastegger Ludwig Schwiedrzik Marius R. Bittermann Florian Berzsenyi Philipp Marquetand	1
+ PDF Chat	SchNet – A deep learning architecture for molecules and materials	2018	Kristof T. Schütt Huziel E. Sauceda Pieter-Jan Kindermans Alexandre Tkatchenko K. Müller	1
+	i-PI 2.0: A universal force engine for advanced molecular simulations	2018	Venkat Kapil Mariana Rossi Ondřej Maršálek Riccardo Petraglia Yair Litman Thomas Spura Bingqing Cheng Alice Cuzzocrea Robert H. Meißner David M. Wilkins	1
+ PDF Chat	SchNetPack: A Deep Learning Toolbox For Atomistic Systems	2018	Kristof T. Schütt Pan Kessel Michael Gastegger Kim A. Nicoli Alexandre Tkatchenko K. Müller	1
+ PDF Chat	Silicon Liquid Structure and Crystal Nucleation from <i>Ab Initio</i> Deep Metadynamics	2018	Luigi Bonati Michele Parrinello	1
+	sGDML: Constructing accurate and data efficient molecular force fields using machine learning	2019	Stefan Chmiela Huziel E. Sauceda Igor Poltavsky Klaus‐Robert Müller Alexandre Tkatchenko	1
+ PDF Chat	PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments, and Partial Charges	2019	Oliver T. Unke Markus Meuwly	1
+ PDF Chat	Large-scale ab initio simulations based on systematically improvable atomic basis	2015	Pengfei Li Xiaohui Liu Mohan Chen Peize Lin Xinguo Ren Lin Lin Chao Yang Lixin He	1
+ PDF Chat	Embedded Atom Neural Network Potentials: Efficient and Accurate Machine Learning with a Physically Inspired Representation	2019	Yaolong Zhang Ce Hu Bin Jiang	1
+ PDF Chat	Isotope effects in liquid water via deep potential molecular dynamics	2019	Hsin-Yu Ko Linfeng Zhang Biswajit Santra Han Wang E Weinan Robert A. DiStasio Roberto Car	1
+ PDF Chat	86 PFLOPS Deep Potential Molecular Dynamics simulation of 100 million atoms with ab initio accuracy	2020	Denghui Lu Han Wang Mohan Chen Lin Lin Roberto Car E Weinan Weile Jia Linfeng Zhang	1
+	A unified deep neural network potential capable of predicting thermal conductivity of silicon in different phases	2020	Ruiyang Li Eungkyu Lee Tengfei Luo	1
+ PDF Chat	Deep neural network for the dielectric response of insulators	2020	Linfeng Zhang Mohan Chen Xifan Wu Han Wang E Weinan Roberto Car	1
+ PDF Chat	DP-GEN: A concurrent learning platform for the generation of reliable deep learning based potential energy models	2020	Yuzhi Zhang Haidi Wang Weijie Chen Jinzhe Zeng Linfeng Zhang Han Wang E Weinan	1
+ PDF Chat	Isotope effects in x-ray absorption spectra of liquid water	2020	Chunyi Zhang Linfeng Zhang Jianhang Xu Fujie Tang Biswajit Santra Xifan Wu	1
+ PDF Chat	Open Catalyst 2020 (OC20) Dataset and Community Challenges	2021	Lowik Chanussot Abhishek Das Siddharth Goyal Thibaut Lavril Muhammed Shuaibi Morgane Rivière Kevin Tran Javier Heras‐Domingo Caleb Ho Weihua Hu	1
+ PDF Chat	Machine Learning for Molecular Simulation	2020	Frank Noé Alexandre Tkatchenko Klaus‐Robert Müller Cecilia Clementi	1
+	Active learning of uniformly accurate interatomic potentials for materials simulation	2019	Linfeng Zhang De-Ye Lin Han Wang Roberto Car E Weinan	1
+	PyXtal_FF: a python library for automated force field generation	2020	Howard Yanxon David Zagaceta Binh Tang David S. Matteson Qiang Zhu	1
+ PDF Chat	Array programming with NumPy	2020	C. R. Harris K. Jarrod Millman Stéfan van der Walt Ralf Gommers Pauli Virtanen David Cournapeau Eric Wieser Julian Taylor Sebastian Berg Nathaniel J. Smith	1
+ PDF Chat	The MLIP package: moment tensor potentials with MPI and active learning	2020	Ivan S. Novikov Konstantin Gubaev Evgeny V. Podryabinkin Alexander V. Shapeev	1
+ PDF Chat	Raman spectrum and polarizability of liquid water from deep neural networks	2020	Grace M. Sommers Marcos F. Calegari Andrade Linfeng Zhang Han Wang Roberto Car	1
+ PDF Chat	Complex reaction processes in combustion unraveled by neural network-based molecular dynamics simulation	2020	Jinzhe Zeng Liqun Cao Mingyuan Xu Tong Zhu John Z. H. Zhang	1
+ PDF Chat	Warm dense matter simulation via electron temperature dependent deep potential molecular dynamics	2020	Yuzhi Zhang Chang Gao Qianrui Liu Linfeng Zhang Han Wang Mohan Chen	1
+	Isotope effects in molecular structures and electronic properties of liquid water via deep potential molecular dynamics based on the SCAN functional	2020	Jianhang Xu Chunyi Zhang Linfeng Zhang Mohan Chen Biswajit Santra Xifan Wu	1
+ PDF Chat	DeePKS-kit: A package for developing machine learning-based chemically accurate energy and density functional models	2022	Yixiao Chen Linfeng Zhang Han Wang E Weinan	1
+	Phase Diagram of a Deep Potential Water Model	2021	Linfeng Zhang Han Wang Roberto Car E Weinan	1
+	Machine Learning Force Fields	2021	Oliver T. Unke Stefan Chmiela Huziel E. Sauceda Michael Gastegger Igor Poltavsky Kristof T. Schütt Alexandre Tkatchenko Klaus‐Robert Müller	1
+ PDF Chat	Using metadynamics to build neural network potentials for reactive events: the case of urea decomposition in water	2021	Manyi Yang Luigi Bonati Daniela Polino Michele Parrinello	1
+ PDF Chat	NewtonNet: a Newtonian message passing network for deep learning of interatomic potentials and forces	2022	Mojtaba Haghighatlari Jie Li Xingyi Guan Oufan Zhang Akshaya Kumar Das Christopher J. Stein Farnaz Heidar‐Zadeh Meili Liu Martin Head‐Gordon Luke W. Bertels	1
+	Heat transport in liquid water from first-principles and deep neural network simulations	2021	Davide Tisi Linfeng Zhang Riccardo Bertossa Han Wang Roberto Car Stefano Baroni	1
+ PDF Chat	Learning intermolecular forces at liquid–vapor interfaces	2021	Samuel P. Niblett Mirza Galib David T. Limmer	1
+ PDF Chat	Dissolving salt is not equivalent to applying a pressure on water	2022	Chunyi Zhang Shuwen Yue Athanassios Z. Panagiotopoulos Michael L. Klein Xifan Wu	1
+ PDF Chat	REANN: A PyTorch-based end-to-end multi-functional deep neural network package for molecular, reactive, and periodic systems	2022	Yaolong Zhang Junfan Xia Bin Jiang	1
+ PDF Chat	A deep potential model with long-range electrostatic interactions	2022	Linfeng Zhang Han Wang Maria Carolina Muniz Athanassios Z. Panagiotopoulos Roberto Car E Weinan	1
+ PDF Chat	E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials	2022	Simon Batzner Albert Musaelian Lixin Sun Mario Geiger Jonathan P. Mailoa Mordechai Kornbluth Nicola Molinari Tess Smidt Boris Kozinsky	1
+ PDF Chat	A tungsten deep neural-network potential for simulating mechanical property degradation under fusion service environment	2022	Xiaoyang Wang Yinan Wang Linfeng Zhang Fu‐Zhi Dai Han Wang	1
+ PDF Chat	Deep potentials for materials science	2022	Tongqi Wen Linfeng Zhang Han Wang E Weinan David J. Srolovitz	1
+ PDF Chat	GPUMD: A package for constructing accurate machine-learned potentials and performing highly efficient atomistic simulations	2022	Zheyong Fan Yanzhou Wang Penghua Ying Keke Song Junjie Wang Yong Wang Zezhu Zeng Ke Xu Eric Lindgren J. Magnus Rahm	1
+ PDF Chat	Towards universal neural network potential for material discovery applicable to arbitrary combination of 45 elements	2022	So Takamoto Chikashi Shinagawa D. Motoki Kosuke Nakago Wenwen Li Iori Kurata Taku Watanabe Yoshihiro Yayama Hiroki Iriguchi Yusuke Asano	1
+	Deep-learning density functional theory Hamiltonian for efficient ab initio electronic-structure calculation	2022	He Li Zun Wang Nianlong Zou Meng Ye Runzhang Xu Xiaoxun Gong Wenhui Duan Yong Xu	1