Bingqing Cheng
University of Cambridge
54 Papers
171 Citations
Bingqing Cheng is an academic researcher from University of Cambridge. The author has contributed to research in topics: Nucleation & Chemistry. The author has an hindex of 18, co-authored 42 publications. Previous affiliations of Bingqing Cheng include École Polytechnique Fédérale de Lausanne & University of Hong Kong.
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Papers
i-PI 2.0: A universal force engine for advanced molecular simulations
Venkat Kapil,Mariana Rossi,Ondrej Marsalek,Ondrej Marsalek,Riccardo Petraglia,Yair Litman,Thomas Spura,Bingqing Cheng,Alice Cuzzocrea,Robert H. Meißner,David M. Wilkins,Benjamin A. Helfrecht,Przemysław Juda,Sébastien P. Bienvenue,Wei Fang,Jan Kessler,Igor Poltavsky,Steven Vandenbrande,Jelle Wieme,Clémence Corminboeuf,Thomas D. Kühne,David E. Manolopoulos,Thomas E. Markland,Jeremy O. Richardson,Alexandre Tkatchenko,Gareth A. Tribello,Veronique Van Speybroeck,Michele Ceriotti +27 more
TL;DR: This second release of i-PI not only includes several new advanced path integral methods, but also offers other classes of algorithms that are moving towards becoming a universal force engine that is both modular and tightly coupled to the driver codes that evaluate the potential energy surface and its derivatives.
365
Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems.
John A. Keith,Valentin Vassilev-Galindo,Bingqing Cheng,Stefan Chmiela,Michael Gastegger,Klaus-Robert Müller,Alexandre Tkatchenko +6 more
TL;DR: A critical review of noteworthy applications that demonstrate how computational chemistry and machine learning can be used together to provide insightful predictions in molecular and materials modeling, retrosyntheses, catalysis, and drug design are reviewed.
334
Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems.
John A. Keith,Valentin Vassilev-Galindo,Bingqing Cheng,Stefan Chmiela,Michael Gastegger,Klaus-Robert Müller,Alexandre Tkatchenko +6 more
TL;DR: In this paper, the authors provide a review of the applications of computational chemistry and machine learning in molecular and materials modeling, retrosyntheses, catalysis, and drug design.
308
Ab initio thermodynamics of liquid and solid water
TL;DR: In this paper, the authors combine advanced free-energy methods and state-of-the-art machine learning techniques to predict thermodynamic properties of liquid water as well as hexagonal (Ih) and cubic (Ic) ice.
295
Evidence for supercritical behaviour of high-pressure liquid hydrogen
TL;DR: In this paper, a theoretical study of the phase diagram of dense hydrogen that uses machine learning to learn potential energy surfaces and interatomic forces from reference calculations and then predict them at low computational cost, overcoming length and timescale limitations.
136