In the dynamic and rapidly advancing battery field, alloy anode materials are a focal point due to their superior electrochemical performance. Traditional screening methods are inefficient and time-consuming. Our research introduces a machine learning-assisted strategy to expedite the discovery and optimization of these materials. We compiled a vast dataset from the MP and AFLOW databases, encompassing tens of thousands of alloy compositions and properties. Utilizing a CGCNN, we accurately predicted the potential and specific capacity of alloy anodes, validated against experimental data. This approach identified approximately 120 low potential and high specific capacity alloy anodes suitable for various battery systems including Li, Na, K, Zn, Mg, Ca, and Al-based. Our method not only streamlines the screening of battery anode materials but also propels the advancement of battery material research and innovation in energy storage technology.
In view of the long-standing controversy over the reversibility of transition metals in Sn-based alloys as anode for Li-ion batteries, an in situ real-time magnetic monitoring method was used to investigate the evolution of Sn-Co intermetallic during the electrochemical cycling. Sn-Co alloy film anodes with different compositions were prepared via magnetron sputtering without using binders and conductive additives. The magnetic responses showed that the Co particles liberated by Li insertion recombine fully with Sn during the delithiation to reform Sn-Co intermetallic into stannum richer phase…
Carnegie Mellon University（卡内基梅隆大学）；Massachusetts Institute of Technology（麻省理工学院）
Next generation batteries based on lithium (Li) metal anodes have been plagued by the dendritic electrodeposition of Li metal on the anode during cycling, resulting in short circuit and capacity loss. Suppression of dendritic growth through the use of solid electrolytes has emerged as one of the most promising strategies for enabling the use of Li metal anodes. We perform a computational screening of over 12,000 inorganic solids based on their ability to suppress dendrite initiation in contact with Li metal anode. Properties for mechanically isotropic and anisotropic interfaces that can be use…
Lithium metal cells are key towards achieving high specific energy and energy density for electrification of transportation and aviation. Anode-free cells are the limiting case of lithium metal cells involving no excess lithium and the highest possible specific energy. In addition, anode-free cells are easier, cheaper and safer as they avoid handling and manufacturing of lithium metal foils. Issues related to dendrite growth and poor cycling are magnified in anode-free cells due to lack of excess lithium. Electrolyte and current collector surface play a crucial role in affecting the cycling pe…
Due to its exceptional lithium storage capacity silicon is considered as a promising candidate for anode material in lithium-ion batteries (LIBs). In the present work we demonstrate that methods of the soft X-ray emission spectroscopy (SXES) can be used as a powerful tool for the comprehensive analysis of the electronic and structural properties of lithium silicides Li$_{x}$Si forming in LIB's anode upon Si lithiation. On the basis of density functional theory (DFT) and molecular dynamics (MD) simulations it is shown that coordination of Si atoms in Li$_{x}$Si decreases with increase in Li con…
Solid-state batteries (SSBs) can offer a paradigm shift in battery safety and energy density. Yet, the promise hinges on the ability to integrate high-performance electrodes with state-of-the-art solid electrolytes. For example, lithium (Li) metal, the most energy-dense anode candidate, suffers from severe interfacial chemomechanical issues that lead to cell failure. Li alloys of In/Sn are attractive alternatives, but their exploration has mostly been limited to the low capacity(low Li content)and In rich Li$_x$In (x$\leq$0.5). Here, the fundamental electro-chemo-mechanical behavior of Li-In a…
Compositional effects in NaK alloy clusters have been studied using bond order length strength notation and density functional theory calculations. The results reveal binding energy shifts of the NaK alloy clusters under different elemental compositions. Atomic arrangements that can be used to predict the structures of stable experimental NaK alloys were also obtained. Our study of these alloy nanoclusters has uncovered a trend correlating atomic position and composition with binding energy. We believe this data will help in the experimental preparation of alloy nanoclusters.
Rate-Dependent Reversibility and Lithium Losses in Hybrid Anode-Collector Metal Electrodes
Departamento de Física de la Materia Condensada, Universidad Autónoma de Madrid（马德里自治大学凝聚态物理系）；Instituto Universitario de Ciencia de Materiales Nicolás Cabrera (INC), Universidad Autónoma de Madrid（马德里自治大学尼古拉斯·卡布雷拉材料科学大学研究所）；Centro de Micro-Análisis de Materiales (CMAM), Universidad Autónoma de Madrid（马德里自治大学材料微分析中心）；Condensed Matter Physics Center (IFIMAC), Universidad Autónoma de Madrid（马德里自治大学凝聚态物理中心）
In this paper, Al-Cu-Fe quasicrystal alloy was used as anode material of lithium-ion batteries. The first specific discharge capacity of quasicrystal was 204mAh/g. Cyclic voltammetry showed that oxidation peak of Al-Cu-Fe quasicrystal was at about 1.4V. The reduction peak was at 0.3V. Al-Cu-Fe quasicrystal have higher Li-ion diffusion impedance and Warburg in the first cycle. X-ray diffraction analysis demonstrate that Li atom enter into quasicrystal structure can not fully leave quasicrytal during first charge/discharge cycle which induce the irreversible capacity.
The design of corrosion-resistant high entropy alloys (CR-HEAs) is challenging due to the alloys' virtually astrological composition space. To facilitate this, efficient and reliable high-throughput exploratory approaches are needed. Toward this end, the current work reports a first-principles-based approach exploiting the correlations between work function, surface energy, and corrosion resistance (i.e., work function and surface energy are, by definitions, proportional and inversely proportional to an alloy's inherent corrosion resistance, respectively). Two Bayesian CALPHAD models (or datab…
Body-centered cubic (bcc) refractory multicomponent alloys are of great interest due to their remarkable strength at high temperatures. Meanwhile, further optimizing the chemical compositions of these alloys to achieve a combination of high strength and room-temperature ductility remains challenging, which would require systematic predictions of the correlated alloy properties across a vast compositional space. In the present work, we performed first-principles calculations with the special quasi-random structure (SQS) method to predict the unstable stacking fault energy ($γ_{usf}$) of the $(1…
Downsizing well-established materials to the nanoscale is a key route to novel functionalities, in particular if different functionalities are merged in hybrid nanomaterials. Hybrid carbon-based hierarchical nanostructures are particularly promising for electrochemical energy storage since they combine benefits of nanosize effects, enhanced electrical conductivity and integrity of bulk materials. We show that endohedral multiwalled carbon nanotubes (CNT) encapsulating high-capacity (here: conversion and alloying) electrode materials have a high potential for use in anode materials for lithium-…
An exciting development over the past few decades has been the use of high-throughput computational screening as a means of identifying promising candidate materials for a variety of structural or functional properties. Experimentally, it is often found that the highest-performing materials contain substantial atomic site disorder. These are frequently overlooked in high-throughput computational searches however, due to difficulties in dealing with materials that do not possess simple, well-defined crystallographic unit cells. Here we demonstrate that the screening of magnetocaloric materials …
We have developed a machine learning-based interatomic potential (MLIP) for the quaternary MoNbTaW (R4) and quinary MoNbTaTiW (R5) high entropy alloys (HEAs). MLIPs enabled accurate high throughput calculations of elastic and mechanical properties of various non-equimolar R4 and R5 alloys, which are otherwise very time consuming calculations when performed using density functional theory (DFT). We demonstrate that the MLIP predicted properties compare well with the DFT results on various test cases and are consistent with the available experimental data. The MLIPs are also utilized for high th…
We investigate the Li2CuSb full-Heusler alloy using the first-principles electronic structure calculations and propose the electrochemical lithiation in this alloy. Band structure calculations suggest the presence of metallic nature in this alloy contrary to half-metallic nature as predicted for most of the members of the full-Heusler alloy family. This alloy is found to be a promising anode material for high-capacity rechargeable batteries based on lithium-ion. We found a removal voltage of 2.48 V for lithium ions in the Li2CuSb/Cu cell, which is in good agreement with the experimentally obta…
Hydrogen permeability prediction in palladium alloys and virtual screening of B2-phase stabilized Pd(100-x-y)CuxMy ternary alloys using machine learning
Lowering the Exponential Wall: Accelerating High-Entropy Alloy Catalysts Screening using Local Surface Energy Descriptors from Neural Network Potentials
Accurate Screening of Functional Materials with Machine-Learning Potential and Transfer-Learned Regressions: Heusler Alloy Benchmark
Research Center for Magnetic and Spintronic Materials, National Institute for Materials Science（磁性及自旋材料研究中心，物质材料研究机构）；Digital Transformation Initiative Center for Magnetic Materials (DXMag), National Institute for Materials Science（磁性材料数字化转型倡议中心（DXMag），物质材料研究机构）
In computational materials science, a common means for predicting macroscopic (e.g., mechanical) properties of an alloy is to define a model using combinations of descriptors that depend on some material properties (elastic constants, misfit volumes, etc.), representative for the macroscopic behavior. The material properties are usually computed using special quasi-random structures (SQSs), in tandem with density functional theory (DFT). However, DFT scales cubically with the number of atoms and is thus impractical for a screening over many alloy compositions. Here, we present a novel methodol…
We present an automated procedure for computing stacking fault energies in random alloys from large-scale simulations using moment tensor potentials (MTPs) with the accuracy of density functional theory (DFT). To that end, we develop an algorithm for training MTPs on random alloys. In the first step, our algorithm constructs a set of ~10000 or more training candidate configurations with 50-100 atoms that are representative for the atomic neighborhoods occurring in the large-scale simulation. In the second step, we use active learning to reduce this set to ~100 most distinct configurations - fo…
Conditional spinodal decomposition in Li-Mg anodes for lithium metal batteries
Max Planck Institute for Sustainable Materials（马克斯·普朗克可持续材料研究所）；Aeronautics Institute of Technology（航空技术学院）；Université de Picardie Jules Verne（亚眠儒勒·凡尔纳大学）；University Grenoble Alpes, CNRS, Grenoble INP, SIMAP（格勒诺布尔阿尔卑斯大学，法国国家科学研究中心，格勒诺布尔理工学院，SIMAP）；Thermo-Calc Software AB（Thermo-Calc软件有限公司）；Federal University of São Carlos（圣卡洛斯联邦大学）；Korea University（高丽大学）；Univ Rouen Normandie, CNRS, INSA Rouen Normandie, Groupe de Physique des Matériaux, UMR 6634（鲁昂诺曼底大学，法国国家科学研究中心，鲁昂诺曼底国立应用科学学院，材料物理研究组）
Bismuth has recently attracted interest in connection with Na-ion battery anodes due to its high volumetric capacity. It reacts with Na to form Na$_3$Bi which is a prototypical Dirac semimetal with a nontrivial electronic structure. Density-functional-theory based first-principles calculations are playing a key role in understanding the fascinating electronic structure of Na$_3$Bi and other topological materials. In particular, the strongly-constrained-and-appropriately-normed (SCAN) meta-generalized-gradient-approximation (meta-GGA) has shown significant improvement over the widely used gener…
How surface chemistry influences reactions occurring thereupon has been a long-standing question of broad scientific and technological interest for centuries. Recently, it has re-emerged as a critical question in a subdiscipline of chemistry - electrochemistry at heterointerphases, where the answers have implications for both how, and in what forms, humanity stores the rising quantities of renewable electric power generated from solar and wind installations world-wide. Here we consider the relation between the surface chemistry at such interphases and the reversibility of electrochemical trans…
Transition States Energies from Machine Learning: An Application to Reverse Water-Gas Shift on Single-Atom Alloys
Center for Interstellar Catalysis, Department of Physics and Astronomy, Aarhus University（星际催化中心，物理天文系，奥胡斯大学）；Aarhus Institute of Advanced Studies, Aarhus University（奥胡斯先进研究所，奥胡斯大学）
Magnesium (Mg) alloys have shown great prospects as both structural and biomedical materials, while poor corrosion resistance limits their further application. In this work, to avoid the time-consuming and laborious experiment trial, a high-throughput computational strategy based on first-principles calculations is designed for screening corrosion-resistant binary Mg alloy with intermetallics, from both the thermodynamic and kinetic perspectives. The stable binary Mg intermetallics with low equilibrium potential difference with respect to the Mg matrix are firstly identified. Then, the hydroge…
Palladium satisfies most of the requirements for an effective hydrogen storage material with two major drawbacks: it has a relatively low gravimetric hydrogen density and is prohibitively expensive for large-scale applications. Pd-based alloys should be considered as possible alternatives to a pure Pd. The question is how much one can dilute the Pd concentration in a variety of candidate materials while preserving hydrogen absorption capability. We demonstrate that the resistivity measurements of thin-film alloy samples can be used for a qualitative high-throughput screening and study of the h…
OptiMat Alloys: a FAIR, living database of multi-principal element alloys enabled by a conversational agent
Empa - Swiss Federal Laboratories for Materials Science and Technology（瑞士联邦材料科技研究所）；National Centre for Computational Design and Discovery of Novel Materials MARVEL, Empa（MARVEL新型材料计算设计与发现国家中心）
Inverse Materials Design by Large Language Model-Assisted Generative Framework
School of Software Engineering, South China University of Technology（华南理工大学软件工程学院）；The University of Hong Kong（香港大学）；City University of Hong Kong（香港城市大学）；DP Technology（DP科技）；Institute of Physics, Chinese Academy of Sciences（中国科学院物理研究所）；AI for Science Institute, Beijing（北京科学智能研究院）
The design of new High Entropy Alloys that can achieve exceptional mechanical properties is presently of great interest to the materials science community. However, due to the difficulty of designing these alloys using traditional methods, machine learning has recently emerged as an essential tool. Particularly, the screening of candidate alloy compositions using surrogate models has become a mainstay of materials design in recent years. Many of these models use the atomic fractions of the alloying elements as inputs. However, there are many possible representation schemes for encoding alloy c…
Alloys present the great potential in catalysis because of their adjustable compositions, structures and element distributions, which unfortunately also limit the fast screening of the potential alloy catalysts. Machine learning methods are able to tackle the multi-variable issues but still cannot yet predict the complex alloy catalysts from the properties of pure metals due to the lack of universal descriptors. Herein we propose a transferable machine-learning model based on the intrinsic properties of substrates and adsorbates, which can predict the adsorption energies of single-atom alloys …
