Recent implementations in LASP 3.0: Global neural network potential with multiple elements and better long-range description

A human-machine interface for automatic exploration of chemical reaction networks

Autonomous reaction network exploration algorithms offer a systematic approach to explore mechanisms of complex chemical processes. However, the resulting reaction networks are so vast that an exploration of all potentially accessible intermediates is computationally too demanding. This renders brute-force explorations unfeasible, while explorations with completely pre-defined intermediates or hard-wired chemical constraints, such as element-specific coordination numbers, are not flexible enough for complex chemical systems. Here, we introduce a STEERING WHEEL to guide an otherwise unbiased automated exploration. The STEERING WHEEL algorithm is intuitive, generally applicable, and enables one to focus on specific regions of an emerging network. It also allows for guiding automated data generation in the context of mechanism exploration, catalyst design, and other chemical optimization challenges. The algorithm is demonstrated for reaction mechanism elucidation of transition metal catalysts. We highlight how to explore catalytic cycles in a systematic and reproducible way. The exploration objectives are fully adjustable, allowing one to harness the STEERING WHEEL for both structure-specific (accurate) calculations as well as for broad high-throughput screening of possible reaction intermediates.

Construction of High Accuracy Machine Learning Interatomic Potential for Surface/Interface of Nanomaterials-A Review

The inherent discontinuity and unique dimensional attributes of nanomaterial surfaces and interfaces bestow them with various exceptional properties. These properties, however, also introduce difficulties for both experimental and computational studies. The advent of machine learning interatomic potential (MLIP) addresses some of the limitations associated with empirical force fields, presenting a valuable avenue for accurate simulations of these surfaces/interfaces of nanomaterials. Central to this approach is the idea of capturing the relationship between system configuration and potential energy, leveraging the proficiency of machine learning (ML) to precisely approximate high-dimensional functions. This review offers an in-depth examination of MLIP principles and their execution and elaborates on their applications in the realm of nanomaterial surface and interface systems. The prevailing challenges faced by this potent methodology are also discussed.

Amorphous Chloride Solid Electrolytes with High Li-Ion Conductivity for Stable Cycling of All-Solid-State High-Nickel Cathodes

Solid electrolytes (SEs) are central components that enable high-performance, all-solid-state lithium batteries (ASSLBs). Amorphous SEs hold great potential for ASSLBs because their grain-boundary-free characteristics facilitate intact solid-solid contact and uniform Li-ion conduction for high-performance cathodes. However, amorphous oxide SEs with limited ionic conductivities and glassy sulfide SEs with narrow electrochemical windows cannot sustain high-nickel cathodes. Herein, we report a class of amorphous Li-Ta-Cl-based chloride SEs possessing high Li-ion conductivity (up to 7.16 mS cm-1) and low Young's modulus (approximately 3 GPa) to enable excellent Li-ion conduction and intact physical contact among rigid components in ASSLBs. We reveal that the amorphous Li-Ta-Cl matrix is composed of LiCl43-, LiCl54-, LiCl65- polyhedra, and TaCl6- octahedra via machine-learning simulation, solid-state 7Li nuclear magnetic resonance, and X-ray absorption analysis. Attractively, our amorphous chloride SEs exhibit excellent compatibility with high-nickel cathodes. We demonstrate that ASSLBs comprising amorphous chloride SEs and high-nickel single-crystal cathodes (LiNi0.88Co0.07Mn0.05O2) exhibit ∼99% capacity retention after 800 cycles at ∼3 C under 1 mA h cm-2 and ∼80% capacity retention after 75 cycles at 0.2 C under a high areal capacity of 5 mA h cm-2. Most importantly, a stable operation of up to 9800 cycles with a capacity retention of ∼77% at a high rate of 3.4 C can be achieved in a freezing environment of -10 °C. Our amorphous chloride SEs will pave the way to realize high-performance high-nickel cathodes for high-energy-density ASSLBs.

Global Neural Network Potential with Explicit Many-Body Functions for Improved Descriptions of Complex Potential Energy Surface

The high dimensional machine learning potential (MLP) that has developed rapidly in the past decade represents a giant step forward in large-scale atomic simulation for complex systems. The long-range interaction and the poor description of chemical reactions are typical problems of high dimensional MLP, which are mainly caused by the poor structure discrimination of the atom-centered ML model. Herein, we propose a low-cost neural-network-based MLP architecture for fitting global potential energy surface data, namely, G-MBNN, that can offer improved energy and force resolution on a complex potential energy surface. In G-MBNN, a set of many-body energy terms based on the local atomic environment are explicitly included in computing the total energy─the total energy of the system is written as the sum of atomic energy and many-body energy contributions. These extra many-body energy terms are computationally low-cost and, importantly, can provide easy access to delicate energy terms in complex systems such as very short repulsion, long-range attractions, and sensitive angular-dependent covalent interactions. We implement G-MBNN in the LASP code and demonstrate the improved accuracy of the new framework in representative systems, including ternary-element energy materials LiCoOx, TiO2 with defects, and a series of organic reactions.

Regulating reconstruction of oxide-derived Cu for electrochemical CO2 reduction toward n-propanol

Oxide-derived copper (OD-Cu) is the most efficient and likely practical electrocatalyst for CO2 reduction toward multicarbon products. However, the inevitable but poorly understood reconstruction from the pristine state to the working state of OD-Cu under strong reduction conditions largely hinders the rational construction of catalysts toward multicarbon products, especially C3 products like n-propanol. Here, we simulate the reconstruction of CuO and Cu2O into their derived Cu by molecular dynamics, revealing that CuO-derived Cu (CuOD-Cu) intrinsically has a richer population of undercoordinated Cu sites and higher surficial Cu atom density than the counterpart Cu2O-derived Cu (Cu2OD-Cu) because of the vigorous oxygen removal. In situ spectroscopes disclose that the coordination number of CuOD-Cu is considerably lower than that of Cu2OD-Cu, enabling the fast kinetics of CO2 reaction and strengthened binding of *C2 intermediate(s). Benefiting from the rich undercoordinated Cu sites, CuOD-Cu achieves remarkable n-propanol faradaic efficiency up to ~17.9%, whereas the Cu2OD-Cu dominantly generates formate.

Machine learning potential era of zeolite simulation

Zeolites, owing to their great variety and complexity in structure and wide applications in chemistry, have long been the hot topic in chemical research. This perspective first presents a short retrospect of theoretical investigations on zeolites using the tools from classical force fields to quantum mechanics calculations and to the latest machine learning (ML) potential simulations. ML potentials as the next-generation technique for atomic simulation open new avenues to simulate and interpret zeolite systems and thus hold great promise for finally predicting the structure-functionality relation of zeolites. Recent advances using ML potentials are then summarized from two main aspects: the origin of zeolite stability and the mechanism of zeolite-related catalytic reactions. We also discussed the possible scenarios of ML potential application aiming to provide instantaneous and easy access of zeolite properties. These advanced applications could now be accomplished by combining cloud-computing-based techniques with ML potential-based atomic simulations. The future development of ML potentials for zeolites in the respects of improving the calculation accuracy, expanding the application scope and constructing the zeolite-related datasets is finally outlooked.