The discovery of new materials can bring enormous societal and technological progress. In this context, exploring completely the large space of potential materials is computationally intractable. ...Here, we review methods for achieving inverse design, which aims to discover tailored materials from the starting point of a particular desired functionality. Recent advances from the rapidly growing field of artificial intelligence, mostly from the subfield of machine learning, have resulted in a fertile exchange of ideas, where approaches to inverse molecular design are being proposed and employed at a rapid pace. Among these, deep generative models have been applied to numerous classes of materials: rational design of prospective drugs, synthetic routes to organic compounds, and optimization of photovoltaics and redox flow batteries, as well as a variety of other solid-state materials.
Parameterized quantum circuits (PQCs) play an essential role in the performance of many variational quantum algorithms. One challenge in implementing such algorithms is choosing an effective circuit ...that well represents the solution space while maintaining a low circuit depth and parameter count. To characterize and identify expressible, yet compact, circuits, several descriptors are proposed, including expressibility and entangling capability, that are statistically estimated from classical simulations. These descriptors are computed for different circuit structures, varying the qubit connectivity and selection of gates. From these simulations, circuit fragments that perform well with respect to the descriptors are identified. In particular, a substantial improvement in performance of two‐qubit gates in a ring or all‐to‐all connected arrangement, compared to that of those on a line, is observed. Furthermore, improvement in both descriptors is achieved by sequences of controlled X‐rotation gates compared to sequences of controlled Z‐rotation gates. In addition, it is investigated how expressibility “saturates” with increased circuit depth, finding that the rate and saturated value appear to be distinguishing features of a PQC. While the correlation between each descriptor and algorithm performance remains to be investigated, methods and results from this study can be useful for algorithm development and design of experiments.
Parameterized quantum circuits play an essential role in the performance of many variational quantum algorithms. Two descriptors, expressibility and entangling capability, are developed to efficiently characterize different parameterized quantum circuits. These quantities are computed for various circuit templates from past studies. Several trends and observations from these simulations may help guide the design of effective circuits as well as experiments.
Reaction prediction remains one of the major challenges for organic chemistry and is a prerequisite for efficient synthetic planning. It is desirable to develop algorithms that, like humans, “learn” ...from being exposed to examples of the application of the rules of organic chemistry. We explore the use of neural networks for predicting reaction types, using a new reaction fingerprinting method. We combine this predictor with SMARTS transformations to build a system which, given a set of reagents and reactants, predicts the likely products. We test this method on problems from a popular organic chemistry textbook.
Many applications of inorganic nanoparticles (NPs), including photocatalysis, photovoltaics, chemical and biochemical sensing, and theranostics, are governed by NP optical properties. Exploration and ...identification of reaction conditions for the synthesis of NPs with targeted spectroscopic characteristics is a time‐, labor‐, and resource‐intensive task, as it involves the optimization of multiple interdependent reaction conditions. Integration of machine learning (ML) and microfluidics (MF) offers accelerated identification and optimization of reaction conditions for NP synthesis. Here, an autonomous ML‐driven, oscillatory MF platform for the synthesis of NPs is reported. The platform utilized multiple recipes and reaction times for the synthesis of NPs with different dimensions, conducted spectroscopic NP characterization, and employed ML approaches to analyze multiple yet prioritized spectroscopic NP characteristics, and identified reaction conditions for the synthesis of NPs with targeted optical properties. The platform is also used to develop an understanding of the relationship between reaction conditions and NP properties. This study shows the strong potential of ML‐driven oscillatory MF platforms in materials science and paves the way for automated NP development.
A self‐driving platform integrating oscillatory microfluidics, online spectroscopy, and machine learning is developed for the autonomous synthesis of metal nanoparticles. The platform employs machine learning to guide the synthesis and analyze the relationship between reaction conditions and nanoparticle properties. The platform successfully identifies the most effective reaction conditions for the synthesis of nanoparticles with different spectroscopic characteristics.
The choice of simulation methods in computational materials science is driven by a fundamental trade-off: bridging large time- and length-scales with highly accurate simulations at an affordable ...computational cost. Venturing the investigation of complex phenomena on large scales requires fast yet accurate computational methods. We review the emerging field of machine-learned potentials, which promises to reach the accuracy of quantum mechanical computations at a substantially reduced computational cost. This Review will summarize the basic principles of the underlying machine learning methods, the data acquisition process and active learning procedures. We highlight multiple recent applications of machine-learned potentials in various fields, ranging from organic chemistry and biomolecules to inorganic crystal structure predictions and surface science. We furthermore discuss the developments required to promote a broader use of ML potentials, and the possibility of using them to help solve open questions in materials science and facilitate fully computational materials design.Materials simulations are now ubiquitous for explaining material properties. This Review discusses how machine-learned potentials break the limitations of system-size or accuracy, how active-learning will aid their development, how they are applied, and how they may become a more widely used approach.
Practical challenges in simulating quantum systems on classical computers have been widely recognized in the quantum physics and quantum chemistry communities over the past century. Although many ...approximation methods have been introduced, the complexity of quantum mechanics remains hard to appease. The advent of quantum computation brings new pathways to navigate this challenging and complex landscape. By manipulating quantum states of matter and taking advantage of their unique features such as superposition and entanglement, quantum computers promise to efficiently deliver accurate results for many important problems in quantum chemistry, such as the electronic structure of molecules. In the past two decades, significant advances have been made in developing algorithms and physical hardware for quantum computing, heralding a revolution in simulation of quantum systems. This Review provides an overview of the algorithms and results that are relevant for quantum chemistry. The intended audience is both quantum chemists who seek to learn more about quantum computing and quantum computing researchers who would like to explore applications in quantum chemistry.
Abstract
Metallic lithium is a promising anode to increase the energy density of rechargeable lithium batteries. Despite extensive efforts, detrimental reactivity of lithium metal with electrolytes ...and uncontrolled dendrite growth remain challenging interconnected issues hindering highly reversible Li-metal batteries. Herein, we report a rationally designed amide-based electrolyte based on the desired interface products. This amide electrolyte achieves a high average Coulombic efficiency during cycling, resulting in an outstanding capacity retention with a 3.5 mAh cm
−2
high-mass-loaded LiNi
0.8
Co
0.1
Mn
0.1
O
2
cathode. The interface reactions with the amide electrolyte lead to the predicted solid electrolyte interface species, having favorable properties such as high ionic conductivity and high stability.
Operando
monitoring the lithium spatial distribution reveals that the highly reversible behavior is related to denser deposition as well as top-down stripping, which decreases the formation of porous deposits and inactive lithium, providing new insights for the development of interface chemistries for metal batteries.
A hybrid quantum–classical approach to model continuous classical probability distributions using a variational quantum circuit is proposed. The architecture of this quantum generator consists of a ...quantum circuit that encodes a classical random variable into a quantum state and a parameterized quantum circuit trained to mimic the target distribution. The model allows for easy interfacing with a classical function, such as a neural network, and is trained using an adversarial learning approach. It is shown that the quantum generator is able to learn using either a classical neural network or a variational quantum circuit as the discriminator model. This implementation takes advantage of automatic differentiation tools to perform the optimization of the variational circuits employed. The framework presented here for the design and implementation of the variational quantum generators can serve as a blueprint for designing hybrid quantum–classical models for other machine learning tasks.
Continuous classical probability distributions are modeled using hybrid quantum–classical generative adversarial networks, where both generator and discriminator consist of a quantum encoder, that maps classical information to quantum states, and a variational circuit. Classical distributions are obtained by sampling the generator and measuring observables on the states generated. This framework provides a blueprint for designing hybrid quantum–classical machine learning architectures.
We report a method to convert discrete representations of molecules to and from a multidimensional continuous representation. This model allows us to generate new molecules for efficient exploration ...and optimization through open-ended spaces of chemical compounds. A deep neural network was trained on hundreds of thousands of existing chemical structures to construct three coupled functions: an encoder, a decoder, and a predictor. The encoder converts the discrete representation of a molecule into a real-valued continuous vector, and the decoder converts these continuous vectors back to discrete molecular representations. The predictor estimates chemical properties from the latent continuous vector representation of the molecule. Continuous representations of molecules allow us to automatically generate novel chemical structures by performing simple operations in the latent space, such as decoding random vectors, perturbing known chemical structures, or interpolating between molecules. Continuous representations also allow the use of powerful gradient-based optimization to efficiently guide the search for optimized functional compounds. We demonstrate our method in the domain of drug-like molecules and also in a set of molecules with fewer that nine heavy atoms.
Many quantum algorithms have daunting resource requirements when compared to what is available today. To address this discrepancy, a quantum-classical hybrid optimization scheme known as 'the quantum ...variational eigensolver' was developed (Peruzzo et al 2014 Nat. Commun. 5 4213) with the philosophy that even minimal quantum resources could be made useful when used in conjunction with classical routines. In this work we extend the general theory of this algorithm and suggest algorithmic improvements for practical implementations. Specifically, we develop a variational adiabatic ansatz and explore unitary coupled cluster where we establish a connection from second order unitary coupled cluster to universal gate sets through a relaxation of exponential operator splitting. We introduce the concept of quantum variational error suppression that allows some errors to be suppressed naturally in this algorithm on a pre-threshold quantum device. Additionally, we analyze truncation and correlated sampling in Hamiltonian averaging as ways to reduce the cost of this procedure. Finally, we show how the use of modern derivative free optimization techniques can offer dramatic computational savings of up to three orders of magnitude over previously used optimization techniques.
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