Atomistic simulations of matter, especially those that leverage first-principles (ab initio) electronic structure theory, provide a microscopic view of the world, underpinning much of our understanding of chemistry and materials science. Over the last decade or so, machine-learned force fields have transformed atomistic modeling by enabling simulations of ab initio quality over unprecedented time and length scales. However, early machine-learning (ML) force fields have largely been limited by (i) the substantial computational and human effort required to develop and validate potentials for each particular system of interest and (ii) a general lack of transferability from one chemical system to the next. Here, we show that it is possible to create a general-purpose atomistic ML model, trained on a public dataset of moderate size, that is capable of running stable molecular dynamics for a wide range of molecules and materials. We demonstrate the power of the MACE-MP-0 model-and its qualitative and at times quantitative accuracy-on a diverse set of problems in the physical sciences, including properties of solids, liquids, gases, chemical reactions, interfaces, and even the dynamics of a small protein. The model can be applied out of the box as a starting or "foundation" model for any atomistic system of interest and, when desired, can be fine-tuned on just a handful of application-specific data points to reach ab initio accuracy. Establishing that a stable force-field model can cover almost all materials changes atomistic modeling in a fundamental way: experienced users obtain reliable results much faster, and beginners face a lower barrier to entry. Foundation models thus represent a step toward democratizing the revolution in atomic-scale modeling that has been brought about by ML force fields.

This chapter explores some recent developments in inorganic materials chemistry. Many of these have come in the area of new materials. The chemistry of high temperature superconducting phases, in terms of the synthesis and structure of these compounds, lies within the area of this text. More recently, the structural chemistry of C 60 in its ionic compounds is also relevant, though the chemistry of this species is developing rapidly in many areas. Consideration of several superconducting systems shows two features in common. They all contain planes of the stoichiometry CuO 2 formed by linking CuO 4 square planes at all vertices, and they all have an average copper oxidation state in excess of 2+. The chapter then looks at the structural chemistry of fullerenes and fullerides.

The synthesis of nanodiamond-perylene diimide composites is reported. Suitably hydroxyl-functionalised perylene diimides (PDIs) are reacted with carboxylic acid functionalised nanodiamonds (NDs) through ester formation. The ND-PDI nanocomposite materials were characterised using a variety of different techniques confirming retention of the ND cores and interestingly the dye properties of the PDIs. In particular, fluorescence measurements suggest that PDIs tethered to NDs retain the characteristics of solution-phase PDIs rather than the optical properties associated with solid-state PDIs which are typically modified due to aggregation. Our relatively simple approach provides a mechanism for maintaining the solution-phase properties of PDIs in solid-state materials

Machine learning plays an increasingly important role in many areas of chemistry and materials science, e.g. to predict materials properties, to accelerate simulations, to design new materials, and to predict synthesis routes of new materials. Graph neural networks (GNNs) are one of the fastest growing classes of machine learning models. They are of particular relevance for chemistry and materials science, as they directly work on a graph or structural representation of molecules and materials and therefore have full access to all relevant information required to characterize materials. In this review article, we provide an overview of the basic principles of GNNs, widely used datasets, and state-of-the-art architectures, followed by a discussion of a wide range of recent applications of GNNs in chemistry and materials science, and concluding with a road-map for the further development and application of GNNs.

Computational chemistry, at the intersection of theoretical chemistry and computer science, employs various models to analyze molecular structures and properties, enabling the understanding and prediction of intricate chemical processes. The integration of artificial intelligence (AI) has revolutionized several fields, particularly in materials chemistry, with applications spanning drug discovery, materials design, and quantum mechanics. However, challenges related to quantum system complexity, model interpretability, and data quality remain a few of the Achilles’ heel of AI applications. This paper provides an overview of AI’s evolution in computational and materials chemistry, focusing on several applications. AI’s transformative potential in materials chemistry is emphasized, facilitating precise material property predictions, crucial for industries reliant on materials innovation. In materials chemistry, AI has led to substantial advancements, enabling the rapid discovery of materials with tailored properties. Yet, the challenges of modeling complex quantum systems, achieving model interpretability, and accessing high-quality data remain. The integration of AI into computational and materials chemistry promises to reshape the field, revolutionizing chemical research, materials design, and technological innovation. In order to harness AI’s full potential, transparent AI models, advanced quantum simulations, optimized data utilization, scalable computing, interdisciplinary collaboration, and ethical AI practices are essential.

Given the power of large language and large vision models, it is of profound and fundamental interest to ask if a foundational model based on data and parameter scaling laws and pre-training strategies is possible for learned simulations of chemistry and materials. The scaling of large and diverse datasets and highly expressive architectures for chemical and materials sciences should result in a foundation model that is more efficient and broadly transferable, robust to out-of-distribution challenges, and easily fine-tuned to a variety of downstream observables, when compared to specific training from scratch on targeted applications in atomistic simulation. In this Perspective we aim to cover the rapidly advancing field of machine learned interatomic potentials (MLIP), and to illustrate a path to create chemistry and materials MLIP foundation models at larger scale.

Synthetic extracellular matrix (ECM) mimics that can recapitulate the complex biochemical and mechanical nature of native tissues are needed for advanced models of development and disease. Biomedical research has heavily relied on the use of animal‐derived biomaterials, which is now impeding their translational potential and convoluting the biological insights gleaned from in vitro tissue models. Natural hydrogels have long served as a convenient and effective cell culture tool, but advances in materials chemistry and fabrication techniques now present promising new avenues for creating xenogenic‐free ECM substitutes appropriate for organotypic models and microphysiological systems. However, significant challenges remain in creating synthetic matrices that can approximate the structural sophistication, biochemical complexity, and dynamic functionality of native tissues. This review summarizes key properties of the native ECM, and discusses recent approaches used to systematically decouple and tune these properties in synthetic matrices. The importance of dynamic ECM mechanics, such as viscoelasticity and matrix plasticity, is also discussed, particularly within the context of organoid and engineered tissue matrices. Emerging design strategies to mimic these dynamic mechanical properties are reviewed, such as multi‐network hydrogels, supramolecular chemistry, and hydrogels assembled from biological monomers.

While the uptake of data-driven approaches for materials science and chemistry is at an exciting, early stage, to realise the true potential of machine learning models for successful scientific discovery, they must have qualities beyond purely predictive power. The predictions and inner workings of models should provide a certain degree of explainability by human experts, permitting the identification of potential model issues or limitations, building trust on model predictions and unveiling unexpected correlations that may lead to scientific insights. In this work, we summarize applications of interpretability and explainability techniques for materials science and chemistry and discuss how these techniques can improve the outcome of scientific studies. We discuss various challenges for interpretable machine learning in materials science and, more broadly, in scientific settings. In particular, we emphasize the risks of inferring causation or reaching generalization by purely interpreting machine learning models and the need of uncertainty estimates for model explanations. Finally, we showcase a number of exciting developments in other fields that could benefit interpretability in material science and chemistry problems.

Metal halide perovskite semiconductors are excellent materials for next-generation solar cells. As a result of research and development all over the world, the photoelectric conversion efficiency for single-cell devices has rapidly improved to over 26% (as of July 2023), while the record efficiency for silicon-on-perovskite tandem devices currently stands at 33.7% (reported in May 2023). Materials chemistry has made many important contributions toward these remarkable results. In this account, we will introduce our achievements in these areas of material chemistry research for improving the performance of perovskite photovoltaics, including the development of high-purity precursor materials, and precursor inks tailored for large-area printed perovskite films. Studies of the nucleation and crystallization process, including simulations, have led to the reliable fabrication of high-quality perovskite films. Diverse new charge collection materials, as well as compounds to effectively modify the perovskite surfaces, have improved solar cell performance.

Topology, a mathematical concept, has recently become a popular and truly transdisciplinary topic encompassing condensed matter physics, solid state chemistry, and materials science. Since there is a direct connection between real space, namely atoms, valence electrons, bonds and orbitals, and reciprocal space, namely bands and Fermi surfaces, via symmetry and topology, classifying topological materials within a single-particle picture is possible. Currently, most materials are classified as trivial insulators, semimetals and metals, or as topological insulators, Dirac and Weyl nodal-line semimetals, and topological metals. The key ingredients for topology are: certain symmetries, the inert pair effect of the outer electrons leading to inversion of the conduction and valence bands, and spin-orbit coupling. This review presents the topological concepts related to solids from the viewpoint of a solid-state chemist, summarizes techniques for growing single crystals, and describes basic physical property measurement techniques to characterize topological materials beyond their structure and provide examples of such materials. Finally, a brief outlook on the impact of topology in other areas of chemistry is provided at the end of the article.

Graph neural networks (GNNs) have received intense interest as a rapidly expanding class of machine learning models remarkably well-suited for materials applications. To date, a number of successful GNNs have been proposed and demonstrated for systems ranging from crystal stability to electronic property prediction and to surface chemistry and heterogeneous catalysis. However, a consistent benchmark of these models remains lacking, hindering the development and consistent evaluation of new models in the materials field. Here, we present a workflow and testing platform, MatDeepLearn, for quickly and reproducibly assessing and comparing GNNs and other machine learning models. We use this platform to optimize and evaluate a selection of top performing GNNs on several representative datasets in computational materials chemistry. From our investigations we note the importance of hyperparameter selection and find roughly similar performances for the top models once optimized. We identify several strengths in GNNs over conventional models in cases with compositionally diverse datasets and in its overall flexibility with respect to inputs, due to learned rather than defined representations. Meanwhile several weaknesses of GNNs are also observed including high data requirements, and suggestions for further improvement for applications in materials chemistry are discussed.

The advancement of chemistry and materials science relies on transformative analytical tools which can overcome the sensitivity, spatial resolution, and throughput limitations of conventional techniques. This review explores the application of quantum sensors - specifically optically pumped magnetometers (OPMs) and nitrogen-vacancy (NV) centers in diamond - as robust platforms for molecular and materials analysis. We contrast the extreme magnetic sensitivity of macroscopic OPM ensembles with the atomic-scale resolution and multimodal capabilities of solid-state NV centers. We highlight their deployment in zero- to ultralow-field and nanoscale NMR spectroscopy, real-time reaction monitoring, and transient radical and pH detection. Furthermore, we discuss their integration into high-throughput chemical assays and non-destructive materials diagnostics, such as operando battery monitoring. With the ongoing commercialization of these technologies and advances in quantum-enhanced sensitivities, quantum sensors are poised to routinely address complex real-world analytical challenges.

Abstract The advance of materials chemistry has influenced significantly the lifestyle of mankind. By virtue of their fascinating physicochemical nature – including ultrasmall size (

Graph deep learning models, which incorporate a natural inductive bias for a collection of atoms, are of immense interest in materials science and chemistry. Here, we introduce the Materials Graph Library (MatGL), an open-source graph deep learning library for materials science and chemistry. Built on top of the popular Deep Graph Library (DGL) and Python Materials Genomics (Pymatgen) packages, our intention is for MatGL to be an extensible ``batteries-included'' library for the development of advanced graph deep learning models for materials property predictions and interatomic potentials. At present, MatGL has efficient implementations for both invariant and equivariant graph deep learning models, including the Materials 3-body Graph Network (M3GNet), MatErials Graph Network (MEGNet), Crystal Hamiltonian Graph Network (CHGNet), TensorNet and SO3Net architectures. MatGL also includes a variety of pre-trained universal interatomic potentials (aka ``foundational materials models (FMM)'') and property prediction models are also included for out-of-box usage, benchmarking and fine-tuning. Finally, MatGL includes support for Pytorch Lightning for rapid training of models.

Electrochemical impedance spectroscopy (EIS) is a highly applicable electrochemical, analytical, and non-invasive technique for materials characterization, which allows the user to evaluate the impact, efficiency, and magnitude of different components within an electrical circuit at a higher resolution than other common electrochemical techniques such as cyclic voltammetry (CV) or chronoamperometry. EIS can be used to study mechanisms of surface reactions, evaluate kinetics and mass transport, and study the level of corrosion on conductive materials, just to name a few. Therefore, this review demonstrates the scope of physical properties of the materials that can be studied using EIS, such as for characterization of supercapacitors, dye-sensitized solar cells (DSSCs), conductive coatings, sensors, self-assembled monolayers (SAMs), and other materials. This guide was created to support beginner and intermediate level researchers in EIS studies to inspire a wider application of this technique for materials characterization. In this work, we provide a summary of the essential background theory of EIS, including experimental design, signal responses, and instrumentation. Then, we discuss the main graphical representations for EIS data, including a scope of the foundation principles of Nyquist, Bode phase angle, Bode magnitude, capacitance and Randles plots, followed by detailed step-by-step explanations of the corresponding calculations that evolve from these graphs and direct examples from the literature highlighting practical applications of EIS for characterization of different types of materials. In addition, we discuss various applications of EIS technique for materials research.

Nitrogen hydrides, including ammonia (NH3), hydrazine (N2H4), hydrazoic acid (HN3) and etc, are compounds of great fundamental and applied importance. Their high-pressure behavior is important because of their abundance in giant planets and because of the hopes of discoverying high-energy-density materials. Here, we have performed a systematic investi- gation on the structural stability of N-H system in a pressure range up to 800 GPa through evolutionary structure prediction simulations. Surprisingly, we found that high pressure stabilizes a series of previously unreported compounds with peculiar structural and electronic properties, such as the N4H, N3H, N2H and NH phases composed of nitrogen backbones, the N9H4 phase containing two dimensional metallic nitrogen planes and novel N8H, NH2, N3H7, NH4 and NH5 molecular phases. Another surprise is that NH3 becomes thermodynamically unstable above ~460 GPa. We found that high-pressure chemistry is much more diverse that hydrocarbon chemistry at normal conditions, leading to expectations that N-H-O and N-H-O-S systems under pressure are likely to possess richer chemistry than the known organic chemistry. This, in turn, opens a possibility of nitrogen-based life at high pressure. The predicted phase diagram of the N-H system also provides a reference for synthesis of high-energy-density materials.

The rapid growth of materials chemistry data, driven by advancements in large-scale radiation facilities as well as laboratory instruments, has outpaced conventional data analysis and modelling methods, which can require enormous manual effort. To address this bottleneck, we investigate the application of supervised and unsupervised machine learning (ML) techniques for scattering and spectroscopy data analysis in materials chemistry research. Our perspective focuses on ML applications in powder diffraction (PD), pair distribution function (PDF), small-angle scattering (SAS), inelastic neutron scattering (INS), and X-ray absorption spectroscopy (XAS) data, but the lessons that we learn are generally applicable across materials chemistry. We review the ability of ML to identify physical and structural models and extract information efficiently and accurately from experimental data. Furthermore, we discuss the challenges associated with supervised ML and highlight how unsupervised ML can mitigate these limitations, thus enhancing experimental materials chemistry data analysis. Our perspective emphasises the transformative potential of ML in materials chemistry characterisation and identifies promising directions for future applications. The perspective aims to guide newcomers to ML-based experimental data analysis.

Convolutional networks have been widely applied for computer vision system. Encouraged by these results, a U-Net convolutional network was applied to recognition of vessels and materials in chemistry lab using the recent Vector-LabPics dataset, which contains 2187 images of materials within mostly transparent vessels in a chemistry lab and other general settings, labeled with 13 classes. By optimizing hyperparameters including learning rates and learning rate decays, 87% accuracy in vessel recognition was achieved. In the case of relatively small training and test sets (relatively rare materials states, the number of training set samples less than 500 and the number of test set samples less than 100), a comprehensive improvement over 18% in IoU and 19% in accuracy for the best model were achieved. Further improvements may be achievable by incorporating improved convolutional network structure into our models.

Safety problems hinder the utilization of high-energy lithium and lithium-ion batteries, although some electrochemical materials chemistries look promising. Herein, this perspective provides current opinions of authors’ group on dealing with...

A theoretical study on the influence of organic functional groups on the electronic properties of graphene related carbon materials was carried out. Here we report, using density functional theory and tight-binding approach, that the best candidates for conducting supramolecular devices can be obtained by engineering the surface chemistry and stacking conformation of these materials.

Synthetic 3D Extracellular Matrices (ECM) find application in cell studies, regenerative medicine and drug discovery. While cells cultured in a monolayer may exhibit unnatural behavior and develop very different phenotypes and genotypes than in vivo, great efforts in materials chemistry have been devoted to reproduce in vitro the in vivo cell mi-croenvironments, which requires a fine tuning of the biochemical and structural actors varying in a spatiotemporal fashion to regulate the cell fate. This review will present the fundamentals of ECM, cover the chemical and structural features of the scaffolds used to generate ECM mimics, discuss the nature of the signaling biomolecules required and exploited to generate bioresponsive cell microenvironments able to induce a specific cell fate, and highlights the syn-thetic strategies involved to create functional 3D ECM mimics.

Chemistry and materials agents integrate literature retrieval, candidate generation, property prediction, and protocol planning into continuous discovery workflows. Consequently, the relevant safety question is shifting from whether a model answers a hazardous question to whether an agent releases a hazardous protocol through a tool-mediated workflow. We introduce \bench, a benchmark that evaluates whether chemistry and materials agents can be steered toward hazardous endpoints through user input, tool observations, or persistent memory. The benchmark contains 432 fixed harmful case specifications spanning eight hazard classes, three scenario shells, four tool-and-memory environments, a single-turn direct-attack baseline, and five online long-horizon attacks: intent hijacking, tool chaining, objective drifting, task injection, and memory poisoning. The concrete language of each online attack is generated from the evolving trajectory at runtime and is therefore not counted in the static benchmark size. In the four-model main experiment with a fixed attacker, agents release complete hazardous synthesis or preparation procedures in 25.6\% of runs. Replacing the attacker model yields mean success rates from 18.4\% to 26.5\%, indicating that the risk is not an artifact of a single attacker. Input- and state-level defenses adapted from general-purpose agent safety, as well as candidate checks designed for chemistry and materials, reduce some failures but still leave complete-path release rates between 9.2\% and 22.5\%. Existing defenses therefore do not simultaneously cover multi-entry contamination, tool state, and the final artifact boundary. These results highlight a widening gap between the rapid development of scientific agents and the safety evaluation and defenses available to the chemistry and materials community.