arXiv subjects

Matthew J. Rosseinsky

Publications and source records attributed to Matthew J. Rosseinsky.

17 recordsLinked to original sources

arXiv2026

Scientific applications of quantum computing: challenges and opportunities

The predictive simulation of molecules and materials has had a broad and significant impact. It nevertheless remains constrained by the cost of accurately treating electronic correlation, excited states, and complex energy landscapes. Quantum computing offers a fundamentally different computational paradigm in which quantum states are encoded and manipulated directly rather than approximated on classical hardware. Here we discuss where this approach may provide a genuine scientific advantage in chemistry, materials science, and biochemistry. Promising directions include the high-accuracy treatment of correlated active spaces, improved excited-state simulations, and accelerated exploration of combinatorial structure spaces. The central challenge is therefore not qubit scaling alone, but demonstrably chemically meaningful gains in predictive reliability. We argue that near-term value is most likely to come from disciplined workflow integration rather than wholesale replacement of classical methods. Noisy physical devices, error-mitigated utility experiments, early fault-tolerant devices, and fully fault-tolerant quantum computers offer different scientific prospects, and claims of usefulness must be tied to the specific regime being discussed. Quantum computing will become scientifically valuable when it demonstrably reduces uncertainty in computed energies, rates, spectra, or materials stability after the full costs of state preparation, measurement, error handling, and coupling to classical simulation are included.

quant-ph↗

arXiv2025

Introducing physics-informed generative models for targeting structural novelty in the exploration of chemical space

Discovering materials with new structural chemistry is key to achieving transformative functionality. Generative artificial intelligence offers a scalable route to propose candidate crystal structures. We introduce a reliable low-cost proxy for structural novelty as a conditioning property to steer generation towards novel yet physically plausible structures. We then develop a physics-informed diffusion model that embeds this descriptor of local environment diversity together with compactness as a stability metric to balance physical plausibility with structural novelty. Conditioning on these metrics improves generative performance across diffusion models, shifting generation away from structural motifs that dominate the training data. A chemically grounded validation protocol isolates those candidates that combine plausibility with structural novelty for physics-based calculation of energetic stability. Both the stability and the novelty of candidates emerging from this workflow can however change when the full potential energy surface at a candidate composition is evaluated with crystal structure prediction (CSP). This suggests a practical generative-CSP synergy for discovery-oriented exploration, where AI targets physically viable yet structurally distinct regions of chemical space for detailed physics-based assessment of novelty and stability.

cond-mat.mtrl-sci↗

arXiv2025

MACS: Multi-Agent Reinforcement Learning for Optimization of Crystal Structures

Geometry optimization of atomic structures is a common and crucial task in computational chemistry and materials design. Following the learning to optimize paradigm, we propose a new multi-agent reinforcement learning method called Multi-Agent Crystal Structure optimization (MACS) to address periodic crystal structure optimization. MACS treats geometry optimization as a partially observable Markov game in which atoms are agents that adjust their positions to collectively discover a stable configuration. We train MACS across various compositions of reported crystalline materials to obtain a policy that successfully optimizes structures from the training compositions as well as structures of larger sizes and unseen compositions, confirming its excellent scalability and zero-shot transferability. We benchmark our approach against a broad range of state-of-the-art optimization methods and demonstrate that MACS optimizes periodic crystal structures significantly faster, with fewer energy calculations, and the lowest failure rate.

cs.LG↗

arXiv2024

Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials

Machine Learning (ML) has offered innovative perspectives for accelerating the discovery of new functional materials, leveraging the increasing availability of material databases. Despite the promising advances, data-driven methods face constraints imposed by the quantity and quality of available data. Moreover, ML is often employed in tandem with simulated datasets originating from density functional theory (DFT), and assessed through in-sample evaluation schemes. This scenario raises questions about the practical utility of ML in uncovering new and significant material classes for industrial applications. Here, we propose a data-driven framework aimed at accelerating the discovery of new transparent conducting materials (TCMs), an important category of semiconductors with a wide range of applications. To mitigate the shortage of available data, we create and validate unique experimental databases, comprising several examples of existing TCMs. We assess state-of-the-art (SOTA) ML models for property prediction from the stoichiometry alone. We propose a bespoke evaluation scheme to provide empirical evidence on the ability of ML to uncover new, previously unseen materials of interest. We test our approach on a list of 55 compositions containing typical elements of known TCMs. Although our study indicates that ML tends to identify new TCMs compositionally similar to those in the training data, we empirically demonstrate that it can highlight material candidates that may have been previously overlooked, offering a systematic approach to identify materials that are likely to display TCMs characteristics.

cond-mat.mtrl-sci↗

arXiv2024

Learning Atoms from Crystal Structure

Computational modelling of materials using machine learning, ML, and historical data has become integral to materials research. The efficiency of computational modelling is strongly affected by the choice of the numerical representation for describing the composition, structure and chemical elements. Structure controls the properties, but often only the composition of a candidate material is available. Existing elemental descriptors lack direct access to structural insights such as the coordination geometry of an element. In this study, we introduce Local Environment-induced Atomic Features, LEAFs, which incorporate information about the statistically preferred local coordination geometry for atoms in crystal structure into descriptors for chemical elements, enabling the modelling of materials solely as compositions without requiring knowledge of their crystal structure. In the crystal structure, each atomic site can be described by similarity to common local structural motifs; by aggregating these features of similarity from the experimentally verified crystal structures of inorganic materials, LEAFs formulate a set of descriptors for chemical elements and compositions. The direct connection of LEAFs to the local coordination geometry enables the analysis of ML model property predictions, linking compositions to the underlying structure-property relationships. We demonstrate the versatility of LEAFs in structure-informed property predictions for compositions, mapping of chemical space in structural terms, and prioritising elemental substitutions. Based on the latter for predicting crystal structures of binary ionic compounds, LEAFs achieve the state-of-the-art accuracy of 86 per cent. These results suggest that the structurally informed description of chemical elements and compositions developed in this work can effectively guide synthetic efforts in discovering new materials.

cond-mat.mtrl-sci↗

arXiv2024

Establishing Deep InfoMax as an effective self-supervised learning methodology in materials informatics

The scarcity of property labels remains a key challenge in materials informatics, whereas materials data without property labels are abundant in comparison. By pretraining supervised property prediction models on self-supervised tasks that depend only on the "intrinsic information" available in any Crystallographic Information File (CIF), there is potential to leverage the large amount of crystal data without property labels to improve property prediction results on small datasets. We apply Deep InfoMax as a self-supervised machine learning framework for materials informatics that explicitly maximises the mutual information between a point set (or graph) representation of a crystal and a vector representation suitable for downstream learning. This allows the pretraining of supervised models on large materials datasets without the need for property labels and without requiring the model to reconstruct the crystal from a representation vector. We investigate the benefits of Deep InfoMax pretraining implemented on the Site-Net architecture to improve the performance of downstream property prediction models with small amounts (<10^3) of data, a situation relevant to experimentally measured materials property databases. Using a property label masking methodology, where we perform self-supervised learning on larger supervised datasets and then train supervised models on a small subset of the labels, we isolate Deep InfoMax pretraining from the effects of distributional shift. We demonstrate performance improvements in the contexts of representation learning and transfer learning on the tasks of band gap and formation energy prediction. Having established the effectiveness of Deep InfoMax pretraining in a controlled environment, our findings provide a foundation for extending the approach to address practical challenges in materials informatics.

cs.LG↗

arXiv2023

Inferring energy-composition relationships with Bayesian optimization enhances exploration of inorganic materials

Computational exploration of the compositional spaces of materials can provide guidance for synthetic research and thus accelerate the discovery of novel materials. Most approaches employ high-throughput sampling and focus on reducing the time for energy evaluation for individual compositions, often at the cost of accuracy. Here, we present an alternative approach focusing on effective sampling of the compositional space. The learning algorithm PhaseBO optimizes the stoichiometry of the potential target material while improving the probability of and accelerating its discovery without compromising the accuracy of energy evaluation.

cond-mat.mtrl-sci↗

arXiv2023

Metrics for quantifying isotropy in high dimensional unsupervised clustering tasks in a materials context

Clustering is a common task in machine learning, but clusters of unlabelled data can be hard to quantify. The application of clustering algorithms in chemistry is often dependant on material representation. Ascertaining the effects of different representations, clustering algorithms, or data transformations on the resulting clusters is difficult due to the dimensionality of these data. We present a thorough analysis of measures for isotropy of a cluster, including a novel implantation based on an existing derivation. Using fractional anisotropy, a common method used in medical imaging for comparison, we then expand these measures to examine the average isotropy of a set of clusters. A use case for such measures is demonstrated by quantifying the effects of kernel approximation functions on different representations of the Inorganic Crystal Structure Database. Broader applicability of these methods is demonstrated in analysing learnt embedding of the MNIST dataset. Random clusters are explored to examine the differences between isotropy measures presented, and to see how each method scales with the dimensionality. Python implementations of these measures are provided for use by the community.

cs.LG↗

arXiv2023

Epitaxial growth, optical and electrical conductivity of the metallic pyrochlore Bi$_2$Ru$_2$O$_7$ on Y-stabilized ZrO$_2$ substrate

We report on the epitaxial growth, structural and electrical properties of metallic pyrochlore bismuth ruthenate heterostructures grown along both the [001] and [111] directions. Ordered pyrochlore thin films were obtained with highly oriented texture along the [001] and [111] crystallographic directions. Density functional theory calculations of the electronic band structure and density of states indicated that Bi$_2$Ru$_2$O$_7$ is semimetallic and that hybridization of the Ru 4$d$ and Bi 6$p$ orbitals via the anion network at the Fermi energy was responsible for the metallicity. Electrical conductivity measurements confirmed that the compound is weakly metallic in agreement with the reported conductivity for the stoichiometric bulk compound. The carrier concentration and mobility of the electrons compared favorably with previous reports on bulk material and indicate strong electron-electron interactions. The measured and computed optical conductivities were found to share coincident spectral features and confirm the electronic correlation. Comparison of the electrical and optical properties of the two distinct orientations indicates differences that cannot be attributed to differences in crystalline quality or dislocations and may indicate anisotropy in the electronic structure of Bi$_2$Ru$_2$O$_7$. This study will enable access to the kagome lattice arising naturally in the {111} planes of the pyrochlore B cation sublattice which may be used to uncover emergent topological properties.

cond-mat.mtrl-sci↗

arXiv2023

Element selection for functional materials discovery by integrated machine learning of elemental contributions to properties

Fundamental differences between materials originate from the unique nature of their constituent chemical elements. Before specific differences emerge according to the precise ratios of elements in a given crystal structure, a material can be represented by the set of its constituent chemical elements. By working at the level of the periodic table, assessment of materials at the level of their phase fields reduces the combinatorial complexity to accelerate screening, and circumvents the challenges associated with composition-level approaches such as poor extrapolation within phase fields, and the impossibility of exhaustive sampling. This early stage discrimination combined with evaluation of novelty of phase fields aligns with the outstanding experimental challenge of identifying new areas of chemistry to investigate, by prioritising which elements to combine in a reaction. Here, we demonstrate that phase fields can be assessed with respect to the maximum expected value of a target functional property and ranked according to chemical novelty. We develop and present PhaseSelect, an end-to-end machine learning model that combines the representation, classification, regression and ranking of phase fields. First, PhaseSelect constructs elemental characteristics from the co-occurrence of chemical elements in computationally and experimentally reported materials, then it employs attention mechanisms to learn representation for phase fields and assess their functional performance. At the level of the periodic table, PhaseSelect quantifies the probability of observing a functional property, estimates its value within a phase field and also ranks a phase field novelty, which we demonstrate with significant accuracy for three avenues of materials applications for high-temperature superconductivity, high-temperature magnetism, and targeted bandgap energy.

cond-mat.mtrl-sci↗

arXiv2022

Site-Net: Using global self-attention and real-space supercells to capture long-range interactions in crystal structures

Site-Net is a transformer architecture that models the periodic crystal structures of inorganic materials as a labelled point set of atoms and relies entirely on global self-attention and geometric information to guide learning. Site-Net processes standard crystallographic information files to generate a large real-space supercell, and the importance of interactions between all atomic sites is flexibly learned by the model for the prediction task presented. The attention mechanism is probed to reveal Site-Net can learn long-range interactions in crystal structures, and that specific attention heads become specialized to deal with primarily short- or long-range interactions. We perform a preliminary hyperparameter search and train Site-Net using a single graphics processing unit (GPU), and show Site-Net achieves state-of-the-art performance on a standard band gap regression task.

cond-mat.mtrl-sci↗

arXiv2022

Random projections and Kernelised Leave One Cluster Out Cross-Validation: Universal baselines and evaluation tools for supervised machine learning for materials properties

With machine learning being a popular topic in current computational materials science literature, creating representations for compounds has become common place. These representations are rarely compared, as evaluating their performance - and the performance of the algorithms that they are used with - is non-trivial. With many materials datasets containing bias and skew caused by the research process, leave one cluster out cross validation (LOCO-CV) has been introduced as a way of measuring the performance of an algorithm in predicting previously unseen groups of materials. This raises the question of the impact, and control, of the range of cluster sizes on the LOCO-CV measurement outcomes. We present a thorough comparison between composition-based representations, and investigate how kernel approximation functions can be used to better separate data to enhance LOCO-CV applications. We find that domain knowledge does not improve machine learning performance in most tasks tested, with band gap prediction being the notable exception. We also find that the radial basis function improves the linear separability of chemical datasets in all 10 datasets tested and provide a framework for the application of this function in the LOCO-CV process to improve the outcome of LOCO-CV measurements regardless of machine learning algorithm, choice of metric, and choice of compound representation. We recommend kernelised LOCO-CV as a training paradigm for those looking to measure the extrapolatory power of an algorithm on materials data.

cs.LG↗

arXiv2021

4D visualization of the photoexcited coherent magnon by an X-ray free electron laser

X-ray free electron lasers (XFEL) create femtosecond X-ray pulses with high brightness and high longitudinal coherence allowing to extend X-ray spectroscopy and scattering techniques into the ultrafast time-domain. These X-rays are a powerful probe for studying coherent quasiparticle excitations in condensed matter triggered by an impulsive optical laser pump. However, unlike coherent phonons, other quasiparticles have been rarely observed due to small signal changes and lack of standards for the identification. Here, we exploit resonant magnetic X-ray diffraction using an XFEL to visualize a photoexcited coherent magnon in space and time. Large intensity oscillations in antiferromagnetic and ferromagnetic Bragg reflections from precessing moment are observed in a multiferroic Y-type hexaferrite. The precession trajectory reveals that a large, long-lived, photoinduced magnetic-field changes the net magnetization substantially through the large-amplitude of the magnon. This work demonstrates an efficient XFEL probe for the coherent magnon in the spotlight for opto-spintronics application.

cond-mat.str-el↗

arXiv2020

Crystal Structure Prediction via Oblivious Local Search

We study Crystal Structure Prediction, one of the major problems in computational chemistry. This is essentially a continuous optimization problem, where many different, simple and sophisticated, methods have been proposed and applied. The simple searching techniques are easy to understand, usually easy to implement, but they can be slow in practice. On the other hand, the more sophisticated approaches perform well in general, however almost all of them have a large number of parameters that require fine tuning and, in the majority of the cases, chemical expertise is needed in order to properly set them up. In addition, due to the chemical expertise involved in the parameter-tuning, these approaches can be {\em biased} towards previously-known crystal structures. Our contribution is twofold. Firstly, we formalize the Crystal Structure Prediction problem, alongside several other intermediate problems, from a theoretical computer science perspective. Secondly, we propose an oblivious algorithm for Crystal Structure Prediction that is based on local search. Oblivious means that our algorithm requires minimal knowledge about the composition we are trying to compute a crystal structure for. In addition, our algorithm can be used as an intermediate step by {\em any} method. Our experiments show that our algorithms outperform the standard basin hopping, a well studied algorithm for the problem.

cs.CE↗

arXiv2013

Mott localization in the correlated superconductor Cs3C60 resulting from the molecular Jahn-Teller effect

Cs3C60 is a correlated superconductor under pressure, but an insulator under ambient conditions. The mechanism causing this insulating behavior is the combination of Mott localization and the dynamic Jahn-Teller effect. We show evidence from infrared spectroscopy for the dynamic Jahn-Teller distortion. The continuous change with temperature of the splitting of infrared lines is typical Jahn-Teller behavior, reflecting the change in population of solid-state conformers. We conclude that the electronic and magnetic solid-state properties of the insulating state are controlled by molecular phenomena. We estimate the time scale of the dynamic Jahn-Teller effect to be above 10^(-11) s and the energy difference between the conformers less than 20 cm-1.

cond-mat.str-el↗

arXiv2012

Structural and magnetic characterization of the complete delafossite solid solution (CuAlO2){1-x}(CuCrO2){x}

We have prepared the complete delafossite solid solution series between diamagnetic CuAlO2 and the t2g^3 frustrated antiferromagnet CuCrO2. The evolution with composition x in CuAl(1-x)Cr(x)O2 of the crystal structure and magnetic properties has been studied and is reported here. The room-temperature unit cell parameters follow the Vegard law and increase with x as expected. The effective moment is equal to the Cr^3+ spin-only S = 3/2 value throughout the entire solid solution. Theta is negative, indicating that the dominant interactions are antiferromagnetic, and its magnitude increases with Cr substitution. For dilute Cr compositions, J_BB was estimated by mean-field theory to be 2.0 meV. Despite the sizable Theta, long-range antiferromagnetic order does not develop until very large x, and is preceeded by glassy behavior. Data presented here, and that on dilute Al-substitution from Okuda et al., suggest that the reduction in magnetic frustration due to the presence of non-magnetic Al does not have as dominant an effect on magnetism as chemical disorder and dilution of the magnetic exchange. For all samples, the 5 K isothermal magnetization does not saturate in fields up to 5 T and minimal hysteresis is observed. The presence of antiferromagnetic interactions is clearly evident in the sub-Brillouin behavior with a reduced magnetization per Cr atom. An inspection of the scaled Curie plot reveals that significant short-range antiferromagnetic interactions occur in CuCrO2 above its Neel temperature, consistent with its magnetic frustration. Uncompensated short-range interactions are present in the Al-substituted samples and are likely a result of chemical disorder.

cond-mat.mtrl-sci↗

arXiv2012

Electrical and magnetic properties of the complete solid solution series between SrRuO3 and LaRhO3: Filling t2g versus tilting

A complete solid solution series between the t2g^4 perovskite ferromagnet SrRuO3 and the diamagnetic t2g^6 perovskite LaRhO3 has been prepared. The evolution with composition x in (SrRuO3)(1-x)(LaRhO3)(x) of the crystal structure and electrical and magnetic properties has been studied and is reported here. As x increases, the octahedral tilt angle gradually increases, along with the pseudocubic lattice parameter and unit cell volume. Electrical resistivity measurements reveal a compositionally driven metal to insulator transition between x = 0.1 and 0.2. Ferromagnetic ordering gives over to glassy magnetism for x > 0.3 and no magnetic ordering is found above 2 K for x > 0.5. M_sat and Theta_CW decrease with increasing x and remain constant after x = 0.5. The magnetism appears poised between localized and itinerant behavior, and becomes more localized with increasing x as evidenced by the evolution of the Rhodes-Wohlfarth ratio. mu_eff per Ru is equal to the quenched spin-only S value across the entire solid solution. Comparisons with Sr(1-x)Ca(x)RuO3 reinforce the important role of structural distortions in determining magnetic ground state. It is suggested that electrical transport and magnetic properties are not strongly coupled in this system.

cond-mat.mtrl-sci↗