From computational screening to the synthesis of a promising OER catalyst.

The search for new materials can be laborious and expensive. Given the challenges that mankind faces today concerning the climate change crisis, the need to accelerate materials discovery for applications like water-splitting could be very relevant for a renewable economy. In this work, we introduce a computational framework to predict the activity of oxygen evolution reaction (OER) catalysts, in order to accelerate the discovery of materials that can facilitate water splitting. We use this framework to screen 6155 ternary-phase spinel oxides and have isolated 33 candidates which are predicted to have potentially high OER activity. We have also trained a machine learning model to predict the binding energies of the *O, *OH and *OOH intermediates calculated within this workflow to gain a deeper understanding of the relationship between electronic structure descriptors and OER activity. Out of the 33 candidates predicted to have high OER activity, we have synthesized three compounds and characterized them using linear sweep voltammetry to gauge their performance in OER. From these three catalyst materials, we have identified a new material, Co2.5Ga0.5O4, that is competitive with benchmark OER catalysts in the literature with a low overpotential of 220 mV at 10 mA cm-2 and a Tafel slope at 56.0 mV dec-1. Given the vast size of chemical space as well as the success of this technique to date, we believe that further application of this computational framework based on the high-throughput virtual screening of materials can lead to the discovery of additional novel, high-performing OER catalysts.

Reconstruction of High Entropy Alloys on a Metal-Organic Framework Approaching Active Oxygen Reduction Electrocatalysts.

High-entropy alloys (HEAs) have garnered considerable attention as promising nanocatalysts for effectively utilizing Pt in catalysis toward oxygen reduction reactions due to their unique properties. Nonetheless, there is a relative dearth of attention regarding the structural evolution of HEAs in response to electrochemical conditions. In this work, we propose a thermal reduction method to synthesize high entropy nanoparticles by leveraging the confinement effect and abundant nitrogen-anchored sites provided by pyrolyzed metal-organic frameworks (MOFs). Notably, the prepared catalysts exhibit enhanced activity accompanied by structural reconstruction during electrochemical activation, approaching 1 order of magnitude higher mass activity compared to Pt/C in oxygen reduction. Atomic-scale structural characterization reveals that abundant defects and single atoms are formed during the activation process, contributing to a significant boost in the catalytic performance for oxygen reduction reactions. This study provides deep insights into surface reconstruction engineering during electrochemical operations, with practical implications for fuel cell applications.

Chemical short-range disorder in lithium oxide cathodes.

Ordered layered structures serve as essential components in lithium (Li)-ion cathodes1-3. However, on charging, the inherently delicate Li-deficient frameworks become vulnerable to lattice strain and structural and/or chemo-mechanical degradation, resulting in rapid capacity deterioration and thus short battery life2,4. Here we report an approach that addresses these issues using the integration of chemical short-range disorder (CSRD) into oxide cathodes, which involves the localized distribution of elements in a crystalline lattice over spatial dimensions, spanning a few nearest-neighbour spacings. This is guided by fundamental principles of structural chemistry and achieved through an improved ceramic synthesis process. To demonstrate its viability, we showcase how the introduction of CSRD substantially affects the crystal structure of layered Li cobalt oxide cathodes. This is manifested in the transition metal environment and its interactions with oxygen, effectively preventing detrimental sliding of crystal slabs and structural deterioration during Li removal. Meanwhile, it affects the electronic structure, leading to improved electronic conductivity. These attributes are highly beneficial for Li-ion storage capabilities, markedly improving cycle life and rate capability. Moreover, we find that CSRD can be introduced in additional layered oxide materials through improved chemical co-doping, further illustrating its potential to enhance structural and electrochemical stability. These findings open up new avenues for the design of oxide cathodes, offering insights into the effects of CSRD on the crystal and electronic structure of advanced functional materials.

Designing lithium halide solid electrolytes.

All-solid-state lithium batteries have attracted widespread attention for next-generation energy storage, potentially providing enhanced safety and cycling stability. The performance of such batteries relies on solid electrolyte materials; hence many structures/phases are being investigated with increasing compositional complexity. Among the various solid electrolytes, lithium halides show promising ionic conductivity and cathode compatibility, however, there are no effective guidelines when moving toward complex compositions that go beyond ab-initio modeling. Here, we show that ionic potential, the ratio of charge number and ion radius, can effectively capture the key interactions within halide materials, making it possible to guide the design of the representative crystal structures. This is demonstrated by the preparation of a family of complex layered halides that combine an enhanced conductivity with a favorable isometric morphology, induced by the high configurational entropy. This work provides insights into the characteristics of complex halide phases and presents a methodology for designing solid materials.

Resolving the Origins of Superior Cycling Performance of Antimony Anode in Sodium-ion Batteries: A Comparison with Lithium-ion Batteries.

Alloying-type antimony (Sb) with high theoretical capacity is a promising anode candidate for both lithium-ion batteries (LIBs) and sodium-ion batteries (SIBs). Given the larger radius of Na+ (1.02 Å) than Li+ (0.76 Å), it was generally believed that the Sb anode would experience even worse capacity degradation in SIBs due to more substantial volumetric variations during cycling when compared to LIBs. However, the Sb anode in SIBs unexpectedly exhibited both better electrochemical and structural stability than in LIBs, and the mechanistic reasons that underlie this performance discrepancy remain undiscovered. Here, using substantial in situ transmission electron microscopy, X-ray diffraction, and Raman techniques complemented by theoretical simulations, we explicitly reveal that compared to the lithiation/delithiation process, sodiation/desodiation process of Sb anode displays a previously unexplored two-stage alloying/dealloying mechanism with polycrystalline and amorphous phases as the intermediates featuring improved resilience to mechanical damage, contributing to superior cycling stability in SIBs. Additionally, the better mechanical properties and weaker atomic interaction of Na-Sb alloys than Li-Sb alloys favor enabling mitigated mechanical stress, accounting for enhanced structural stability as unveiled by theoretical simulations. Our finding delineates the mechanistic origins of enhanced cycling stability of Sb anode in SIBs with potential implications for other large-volume-change electrode materials.

Nanoscale Design for High Entropy Alloy Electrocatalysts.

Due to their distinctive physical and chemical characteristics, high entropy alloys (HEAs), a class of alloys comprising multiple elements, have garnered a lot of attention. It is demonstrated recently that HEA electrocatalysts increase the activity and stability of several processes. In this paper, the most recent developments in HEA electrocatalysts research are reviewed, and the performance of HEAs in catalyzing key reactions in water electrolysis and fuel cells is summarized. In addition, the design strategies for HEA electrocatalysts optimization is introduced, which include component selection, size optimization, morphology control, structural engineering, crystal phase regulation, and theoretical prediction, which can guide component selection and structural design of HEA electrocatalysts.

Machine Learning-Assisted Low-Dimensional Electrocatalysts Design for Hydrogen Evolution Reaction.

Efficient electrocatalysts are crucial for hydrogen generation from electrolyzing water. Nevertheless, the conventional "trial and error" method for producing advanced electrocatalysts is not only cost-ineffective but also time-consuming and labor-intensive. Fortunately, the advancement of machine learning brings new opportunities for electrocatalysts discovery and design. By analyzing experimental and theoretical data, machine learning can effectively predict their hydrogen evolution reaction (HER) performance. This review summarizes recent developments in machine learning for low-dimensional electrocatalysts, including zero-dimension nanoparticles and nanoclusters, one-dimensional nanotubes and nanowires, two-dimensional nanosheets, as well as other electrocatalysts. In particular, the effects of descriptors and algorithms on screening low-dimensional electrocatalysts and investigating their HER performance are highlighted. Finally, the future directions and perspectives for machine learning in electrocatalysis are discussed, emphasizing the potential for machine learning to accelerate electrocatalyst discovery, optimize their performance, and provide new insights into electrocatalytic mechanisms. Overall, this work offers an in-depth understanding of the current state of machine learning in electrocatalysis and its potential for future research.

High entropy liquid electrolytes for lithium batteries.

High-entropy alloys/compounds have large configurational entropy by introducing multiple components, showing improved functional properties that exceed those of conventional materials. However, how increasing entropy impacts the thermodynamic/kinetic properties in liquids that are ambiguous. Here we show this strategy in liquid electrolytes for rechargeable lithium batteries, demonstrating the substantial impact of raising the entropy of electrolytes by introducing multiple salts. Unlike all liquid electrolytes so far reported, the participation of several anionic groups in this electrolyte induces a larger diversity in solvation structures, unexpectedly decreasing solvation strengths between lithium ions and solvents/anions, facilitating lithium-ion diffusivity and the formation of stable interphase passivation layers. In comparison to the single-salt electrolytes, a low-concentration dimethyl ether electrolyte with four salts shows an enhanced cycling stability and rate capability. These findings, rationalized by the fundamental relationship between entropy-dominated solvation structures and ion transport, bring forward high-entropy electrolytes as a composition-rich and unexplored space for lithium batteries and beyond.

Entropy-Driven Liquid Electrolytes for Lithium Batteries.

Developing liquid electrolytes with higher kinetics and enhanced interphase stability is one of the key challenges for lithium batteries. However, the poor solubility of lithium salts in solvents sets constraints that compromises the electrolyte properties. Here, it is shown that introducing multiple salts to form a high-entropy solution, alters the solvation structure, which can be used to raise the solubility of specific salts and stabilize electrode-electrolyte interphases. The prepared high-entropy electrolytes significantly enhance the cycling and rate performance of lithium batteries. For lithium-metal anodes the reversibility exceeds 99%, which extends the cycle life of batteries even under aggressive cycling conditions. For commercial batteries, combining a graphite anode with a LiNi0.8 Co0.1 Mn0.1 O2 cathode, more than 1000 charge-discharge cycles are achieved while maintaining a capacity retention of more than 90%. These performance improvements with respect to regular electrolytes are rationalized by the unique features of the solvation structure in high-entropy electrolytes. The weaker solvation interaction induced by the higher disorder results in improved lithium-ion kinetics, and the altered solvation composition leads to stabilized interphases. Finally, the high-entropy, induced by the presence of multiple salts, enables a decrease in melting temperature of the electrolytes and thus enables lower battery operation temperatures without changing the solvents.

Regulating Surface Oxygen Activity by Perovskite-Coating-Stabilized Ultrahigh-Nickel Layered Oxide Cathodes.

Ultrahigh-Ni layered oxides are proposed as promising cathodes to fulfill the range demand of electric vehicles; yet, they are still haunted by compromised cyclability and thermal robustness. State-of-the-art surface coating has been applied to solve the instability via blocking the physical contact between the electrolyte and the highly active Ni4+ ions on the cathode surface, but it falls short in handling the chemo-physical mobility of the oxidized lattice oxygen ions in the cathode. Herein, a direct regulation strategy is proposed to accommodate the highly active anionic redox within the solid phase. By leveraging the stable oxygen vacancies/interstitials in a lithium and oxygen dual-ion conductor (layered perovskite La4 NiLiO8 ) coating layer, the reactivity of the surface lattice oxygen ion is dramatically restrained. As a result, the oxygen release from the lattice is suppressed, as well as the undesired irreversible phase transition and intergranular mechanical cracking. Meanwhile, the introduced dual-ion conductor can also facilitate lithium-ion diffusion kinetics and electronic conductivity on the particle surface. This work demonstrates that accommodating the anionic redox chemistry by dual-ion conductors is an effective strategy for capacity versus stability juggling of the high-energy cathodes.

Phenoxazine Polymer-based p-type Positive Electrode for Aluminum-ion Batteries with Ultra-long Cycle Life.

Aluminum-ion batteries (AIBs) are a promising candidate for large-scale energy storage due to the abundant reserves, low cost, good safety, and high theoretical capacity of Al. However, AIBs with inorganic positive electrodes still suffer from sluggish kinetics and structural collapse upon cycling. Herein, we propose a novel p-type poly(vinylbenzyl-N-phenoxazine) (PVBPX) positive electrode for AIBs. The dual active sites enable PVBPX to deliver a high capacity of 133 mAh g-1 at 0.2 A g-1 . More impressively, the expanded π-conjugated construction, insolubility, and anionic redox chemistry without bond rearrangement of PVBPX for AIBs contribute to an amazing ultra-long lifetime of 50000 cycles. The charge storage mechanism is that the AlCl4 - ions can reversibly coordinate/dissociate with the N and O sites in PVBPX sequentially, which is evidenced by both experimental and theoretical results. These findings establish a foundation to advance organic AIBs for large-scale energy storage.

Machine learning for a sustainable energy future.

Transitioning from fossil fuels to renewable energy sources is a critical global challenge; it demands advances - at the materials, devices and systems levels - for the efficient harvesting, storage, conversion and management of renewable energy. Energy researchers have begun to incorporate machine learning (ML) techniques to accelerate these advances. In this Perspective, we highlight recent advances in ML-driven energy research, outline current and future challenges, and describe what is required to make the best use of ML techniques. We introduce a set of key performance indicators with which to compare the benefits of different ML-accelerated workflows for energy research. We discuss and evaluate the latest advances in applying ML to the development of energy harvesting (photovoltaics), storage (batteries), conversion (electrocatalysis) and management (smart grids). Finally, we offer an overview of potential research areas in the energy field that stand to benefit further from the application of ML.

On scientific understanding with artificial intelligence.

An oracle that correctly predicts the outcome of every particle physics experiment, the products of every possible chemical reaction or the function of every protein would revolutionize science and technology. However, scientists would not be entirely satisfied because they would want to comprehend how the oracle made these predictions. This is scientific understanding, one of the main aims of science. With the increase in the available computational power and advances in artificial intelligence, a natural question arises: how can advanced computational systems, and specifically artificial intelligence, contribute to new scientific understanding or gain it autonomously? Trying to answer this question, we adopted a definition of 'scientific understanding' from the philosophy of science that enabled us to overview the scattered literature on the topic and, combined with dozens of anecdotes from scientists, map out three dimensions of computer-assisted scientific understanding. For each dimension, we review the existing state of the art and discuss future developments. We hope that this Perspective will inspire and focus research directions in this multidisciplinary emerging field.

Fluorinated Rocksalt Cathode with Ultra-high Active Li Content for Lithium-ion Batteries.

The key to increasing the energy density of lithium-ion batteries is to incorporate high contents of extractable Li into the cathode. Unfortunately, this triggers formidable challenges including structural instability and irreversible chemistry under operation. Here, we report a new kind of ultra-high Li compound: Li4+x MoO5 Fx (1≤x≤3) for cathode with an unprecedented level of electrochemically active Li (>3 Li+ per formula), delivering a reversible capacity up to 438 mAh g-1 . Unlike other reported Li-rich cathodes, Li4+x MoO5 Fx presents distinguished structure stability to immunize against irreversible behaviors. Through spectroscopic and electrochemical techniques, we find an anionic redox-dominated charge compensation with negligible oxygen release and voltage decay. Our theoretical analysis reveals a "reductive effect" of high-level fluorination stabilizes the anionic redox by reducing the oxygen ions in pure-Li conditions, enabling a facile, reversible, and high Li-portion cycling.

Niobium-doped layered cathode material for high-power and low-temperature sodium-ion batteries.

The application of sodium-based batteries in grid-scale energy storage requires electrode materials that facilitate fast and stable charge storage at various temperatures. However, this goal is not entirely achievable in the case of P2-type layered transition-metal oxides because of the sluggish kinetics and unfavorable electrode|electrolyte interphase formation. To circumvent these issues, we propose a P2-type Na0.78Ni0.31Mn0.67Nb0.02O2 (P2-NaMNNb) cathode active material where the niobium doping enables reduction in the electronic band gap and ionic diffusion energy barrier while favoring the Na-ion mobility. Via physicochemical characterizations and theoretical calculations, we demonstrate that the niobium induces atomic scale surface reorganization, hindering metal dissolution from the cathode into the electrolyte. We also report the testing of the cathode material in coin cell configuration using Na metal or hard carbon as anode active materials and ether-based electrolyte solutions. Interestingly, the Na||P2-NaMNNb cell can be cycled up to 9.2 A g-1 (50 C), showing a discharge capacity of approximately 65 mAh g-1 at 25 °C. Furthermore, the Na||P2-NaMNNb cell can also be charged/discharged for 1800 cycles at 368 mA g-1 and -40 °C, demonstrating a capacity retention of approximately 76% and a final discharge capacity of approximately 70 mAh g-1.

Theory-guided experimental design in battery materials research.

A reliable energy storage ecosystem is imperative for a renewable energy future, and continued research is needed to develop promising rechargeable battery chemistries. To this end, better theoretical and experimental understanding of electrochemical mechanisms and structure-property relationships will allow us to accelerate the development of safer batteries with higher energy densities and longer lifetimes. This Review discusses the interplay between theory and experiment in battery materials research, enabling us to not only uncover hitherto unknown mechanisms but also rationally design more promising electrode and electrolyte materials. We examine specific case studies of theory-guided experimental design in lithium-ion, lithium-metal, sodium-metal, and all-solid-state batteries. We also offer insights into how this framework can be extended to multivalent batteries. To close the loop, we outline recent efforts in coupling machine learning with high-throughput computations and experiments. Last, recommendations for effective collaboration between theorists and experimentalists are provided.

Unblocking Oxygen Charge Compensation for Stabilized High-Voltage Structure in P2-Type Sodium-Ion Cathode.

Layered transition-metal (TM) oxides are ideal hosts for Li+ charge carriers largely due to the occurrence of oxygen charge compensation that stabilizes the layered structure at high voltage. Hence, enabling charge compensation in sodium layered oxides is a fascinating task for extending the cycle life of sodium-ion batteries. Herein a Ti/Mg co-doping strategy for a model P2-Na2/3 Ni1/3 Mn2/3 O2 cathode material is put forward to activate charge compensation through highly hybridized O2 p TM3 d covalent bonds. In this way, the interlayer OO electrostatic repulsion is weakened upon deeply charging, which strongly affects the systematic total energy that transforms the striking P2-O2 interlayer contraction into a moderate solid-solution-type evolution. Accordingly, the cycling stability of the codoped cathode material is improved superiorly over the pristine sample. This study starts a perspective way of optimizing the sodium layered cathodes by rational structural design coupling electrochemical reactions, which can be extended to widespread battery researches.

Artificial Neuron Networks Enabled Identification and Characterizations of 2D Materials and van der Waals Heterostructures.

Two-dimensional (2D) materials and their in-plane and out-of-plane (i.e., van der Waals, vdW) heterostructures are promising building blocks for next-generation electronic and optoelectronic devices. Since the performance of the devices is strongly dependent on the crystalline quality of the materials and the interface characteristics of the heterostructures, a fast and nondestructive method for distinguishing and characterizing various 2D building blocks is desirable to promote the device integrations. In this work, based on the color space information on 2D materials' optical microscopy images, an artificial neural network-based deep learning algorithm is developed and applied to identify eight kinds of 2D materials with accuracy well above 90% and a mean value of 96%. More importantly, this data-driven method enables two interesting functionalities: (1) resolving the interface distribution of chemical vapor deposition (CVD) grown in-plane and vdW heterostructures and (2) identifying defect concentrations of CVD-grown 2D semiconductors. The two functionalities can be utilized to quickly identify sample quality and optimize synthesis parameters in the future. Our work improves the characterization efficiency of atomically thin materials and is therefore valuable for their research and applications.

In Situ, Atomic-Resolution Observation of Lithiation and Sodiation of WS2 Nanoflakes: Implications for Lithium-Ion and Sodium-Ion Batteries.

WS2 nanoflakes have great potential as electrode materials of lithium-ion batteries (LIBs) and sodium-ion batteries (SIBs) because of their unique 2D structure, which facilitates the reversible intercalation and extraction of alkali metal ions. However, a fundamental understanding of the electrochemical lithiation/sodiation dynamics of WS2 nanoflakes especially at the nanoscale level, remains elusive. Here, by combining battery electrochemical measurements, density functional theory calculations, and in situ transmission electron microscopy, the electrochemical-reaction kinetics and mechanism for both lithiation and sodiation of WS2 nanoflakes are investigated at the atomic scale. It is found that compared to LIBs, SIBs exhibit a higher reversible sodium (Na) storage capacity and superior cyclability. For sodiation, the volume change due to ion intercalation is smaller than that in lithiation. Also, sodiated WS2 maintains its layered structure after the intercalation process, and the reduced metal nanoparticles after conversion in sodiation are well-dispersed and aligned forming a pattern similar to the layered structure. Overall, this work shows a direct interconnection between the reaction dynamics of lithiated/sodiated WS2 nanoflakes and their electrochemical performance, which sheds light on the rational optimization and development of advanced WS2 -based electrodes.

Data-Driven Strategies for Accelerated Materials Design.

The ongoing revolution of the natural sciences by the advent of machine learning and artificial intelligence sparked significant interest in the material science community in recent years. The intrinsically high dimensionality of the space of realizable materials makes traditional approaches ineffective for large-scale explorations. Modern data science and machine learning tools developed for increasingly complicated problems are an attractive alternative. An imminent climate catastrophe calls for a clean energy transformation by overhauling current technologies within only several years of possible action available. Tackling this crisis requires the development of new materials at an unprecedented pace and scale. For example, organic photovoltaics have the potential to replace existing silicon-based materials to a large extent and open up new fields of application. In recent years, organic light-emitting diodes have emerged as state-of-the-art technology for digital screens and portable devices and are enabling new applications with flexible displays. Reticular frameworks allow the atom-precise synthesis of nanomaterials and promise to revolutionize the field by the potential to realize multifunctional nanoparticles with applications from gas storage, gas separation, and electrochemical energy storage to nanomedicine. In the recent decade, significant advances in all these fields have been facilitated by the comprehensive application of simulation and machine learning for property prediction, property optimization, and chemical space exploration enabled by considerable advances in computing power and algorithmic efficiency.In this Account, we review the most recent contributions of our group in this thriving field of machine learning for material science. We start with a summary of the most important material classes our group has been involved in, focusing on small molecules as organic electronic materials and crystalline materials. Specifically, we highlight the data-driven approaches we employed to speed up discovery and derive material design strategies. Subsequently, our focus lies on the data-driven methodologies our group has developed and employed, elaborating on high-throughput virtual screening, inverse molecular design, Bayesian optimization, and supervised learning. We discuss the general ideas, their working principles, and their use cases with examples of successful implementations in data-driven material discovery and design efforts. Furthermore, we elaborate on potential pitfalls and remaining challenges of these methods. Finally, we provide a brief outlook for the field as we foresee increasing adaptation and implementation of large scale data-driven approaches in material discovery and design campaigns.