Generalized Mechanism for the Solid Phase Transition of M_(2)O_(3)(M=Al,Ga)Featuring Single Cation Migration and Martensitic Lattice Transformation

Al_(2)O_(3)and Ga_(2)O_(3)exhibit numerous crystal phases with distinct stabilities and materialproperties.However,the phase transitions among thosematerialsare typicallyundesirable in industrial applications,making it imperative to elucidate the transition mechanisms between these phases.The configurational similarities between Al_(2)O_(3)and Ga_(2)O_(3)allow for the replication of phase transition pathways between these materials.In this study,we investigate the potential phase transition pathway of alumina from the 0-phase to the α-phase using stochastic surface walking global optimization based on global neural network potentials,while extending an existing Ga_(2)O_(3)phase transition path.Through this exploration,we identify a novel single-atom migration pseudomartensitic mechanism,which combines martensitic transformation with single-atom diffusion.This discovery offers valuable insights for experimental endeavors aimed at stabilizing alumina in transitional phases.

Recursion-transform method and potential formulae of the m×n cobweb and fan networks

In this paper, we made a new breakthrough, which proposes a new recursion–transform（RT） method with potential parameters to evaluate the nodal potential in arbitrary resistor networks. For the first time, we found the exact potential formulae of arbitrary m × n cobweb and fan networks by the RT method, and the potential formulae of infinite and semi-infinite networks are derived. As applications, a series of interesting corollaries of potential formulae are given by using the general formula, the equivalent resistance formula is deduced by using the potential formula, and we find a new trigonometric identity by comparing two equivalence results with different forms.

Recent Implementations in LASP 3.0:Global Neural Network Potential with Multiple Elements and Better Long-Range Description

LASP(large-scale atomistic simulation with neural network potential)software developed by our group since 2018 is a powerful platform(www.lasphub.com)for performing atomic simulation of complex materials.The software integrates the neural network(NN)potential technique with the global potential energy surface exploration method,and thus can be utilized widely for structure prediction and reaction mechanism exploration.Here we introduce our recent update on the LASP program version 3.0,focusing on the new functionalities including the advanced neuralnetwork training based on the multi-network framework,the newly-introduced S^(7) and S^(8) power type structure descriptor(PTSD).These new functionalities are designed to further improve the accuracy of potentials and accelerate the neural network training for multipleelement systems.Taking Cu-C-H-O neural network potential and a heterogeneous catalytic model as the example,we show that these new functionalities can accelerate the training of multi-element neural network potential by using the existing single-network potential as the input.The obtained double-network potential Cu CHO is robust in simulation and the introduction of S^(7) and S^(8) PTSDs can reduce the root-mean-square errors of energy by a factor of two.

A procedure for design of hydrogen networks with multiple contaminants

It is necessary to reduce hydrogen consumption to meet increasingly strict environmental and product-quality regulations for refinery plants. In this paper, the concentration potential concepts proposed for design of water-using networks are extended to synthesis of hydrogen networks with multiple contaminants. In the design procedure, the precedence of processes is determined by the values of concentration potential of demands.The usage of complementary source pair(s) to reduce utility consumption is investigated. Three case studies are presented to illustrate the effectiveness of the method. It is shown that the design procedure has clear engineering meaning.

Tour Planning Design for Mobile Robots Using Pruned Adaptive Resonance Theory Networks

The development of intelligent algorithms for controlling autonomous mobile robots in real-time activities has increased dramatically in recent years.However,conventional intelligent algorithms currently fail to accurately predict unexpected obstacles involved in tour paths and thereby suffer from inefficient tour trajectories.The present study addresses these issues by proposing a potential field integrated pruned adaptive resonance theory(PPART)neural network for effectively managing the touring process of autonomous mobile robots in real-time.The proposed system is implemented using the AlphaBot platform,and the performance of the system is evaluated according to the obstacle prediction accuracy,path detection accuracy,time-lapse,tour length,and the overall accuracy of the system.The proposed system provide a very high obstacle prediction accuracy of 99.61%.Accordingly,the proposed tour planning design effectively predicts unexpected obstacles in the environment and thereby increases the overall efficiency of tour navigation.

Machine Learning for Chemistry:Basics and Applications

The past decade has seen a sharp increase in machine learning(ML)applications in scientific research.This review introduces the basic constituents of ML,including databases,features,and algorithms,and highlights a few important achievements in chemistry that have been aided by ML techniques.The described databases include some of the most popular chemical databases for molecules and materials obtained from either experiments or computational calculations.Important two-dimensional(2D)and three-dimensional(3D)features representing the chemical environment of molecules and solids are briefly introduced.Decision tree and deep learning neural network algorithms are overviewed to emphasize their frameworks and typical application scenarios.Three important fields of ML in chemistry are discussed:(1)retrosynthesis,in which ML predicts the likely routes of organic synthesis;(2)atomic simulations,which utilize the ML potential to accelerate potential energy surface sampling;and(3)heterogeneous catalysis,in which ML assists in various aspects of catalytic design,ranging from synthetic condition optimization to reaction mechanism exploration.Finally,a prospect on future ML applications is provided.

Atomistic insights into early stage corrosion of bcc Fe surfaces in oxygen dissolved liquid lead-bismuth eutectic(LBE-O)

Classical molecular dynamics simulations with global neural network machine learning potential are used to study early stage oxidation and dissolution behaviors of bcc Fe surfaces contacting with stagnant oxygen dissolved liquid leadbismuth eutectic(LBE-O).Both static and dynamic simulation results indicate that the early stage oxidation and dissolution behaviors of bcc Fe show strong orientation dependence under the liquid LBE environments,which may explain the experimental observations of uneven interface between iron-based materials and liquid LBE.Our investigations show that it is the delicate balance between the oxide growth and metal dissolution that leads to the observed corrosion anisotropy for bcc Fe contacting with liquid LBE-O.

Selectivity control in alkyne semihydrogenation:Recent experimental and theoretical progress

Researchers have been attempting to characterize heterogeneous catalysts in situ in addition to correlating their structures with their activity and selectivity in spite of many challenges.Here,we review recent experimental and theoretical advances regarding alkyne selective hydrogenation by Pd‐based catalysts,which are an important petrochemical reaction.The catalytic selectivity for the reaction of alkynes to alkenes is influenced by the composition and structure of the catalysts.Recent progress achieved through experimental studies and atomic simulations has provided useful insights into the origins of the selectivity.The important role of the subsurface species(H and C)was revealed by monitoring the catalyst surface and the related catalytic performance.The atomic structures of the Pd catalytic centers and their relationship with selectivity were established through atomic simulations.The combined knowledge gained from experimental and theoretical studies provides a fundamental understanding of catalytic mechanisms and reveals a path toward improved catalyst design.

A Convolutional Neural Network Model for Classifying Cardiac Membrane Potential Patterns

Investigation of the electrophysiological mechanisms that induce arrhythmias is one of the most important issues in scientific research.Since computational cardiology allows the systematic dissection of causal mechanisms of observed effects,simulations based on the ionic channel mathematical models have become one of the most widely used methods.To reduce themanual classification of different types of membrane potential patterns produced during simulations,a convolutional neural network is developed in this paper.The model includes 4convolution layers,4 pooling layers and a fully connected layer.An activation function of Re LU is used.Before machine learning,all the pattems are calibrated,cut,and normalized to a uniform format with a size of 256×256.The contour boundary of each pattern is extracted using the maximum between-class variance method.In the examination,the proposed learning algorithm shows a recognition accuracy of 97%on test data set after training.

Toward accurate and efficient dynamic computational strategy for heterogeneous catalysis:Temperature-dependent thermodynamics and kinetics for the chemisorbed on-surface CO

Computational tools on top of first principle calculations have played an indispensable role in revealing the molecular details,thermodynamics,and kinetics in catalytic reactions.Here we proposed a highly efficient dynamic strategy for the calculation of thermodynamic and kinetic properties in heterogeneous catalysis on the basis of efficient potential energy surface(PES)and MD simulations.Taking CO adsorbate on Ru(0001)surface as the illustrative model system,we demonstrated the PES-based MD can efficiently generate reliable two-dimensional potential-of-mean-force(PMF)surfaces in a wide range of temperatures,and thus temperature-dependent thermodynamic properties can be obtained in a comprehensive investigation on the whole PMF surface.Moreover,MD offers an effective way to describe the surface kinetics such as adsorbate on-surface movement,which goes beyond the most popular static approach based on free energy barrier and transition state theory(TST).We further revealed that the dynamic strategy significantly improves the predictions of both thermodynamic and kinetic properties as compared to the popular ideal statistic mechanics approaches such as harmonic analysis and TST.It is expected that this accurate yet efficient dynamic strategy can be powerful in understanding mechanisms and reactivity of a catalytic surface system,and further guides the rational design of heterogeneous catalysts.