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The discovery of new materials often requires collaboration between experimental and computational chemists. Web based platforms allow more flexibility in this collaboration by giving access to computational tools without the need for access to computational researchers. We present Liverpool Materials Discovery Server (lmds.liverpool.ac.uk), one such platform which currently hosts six state of the art computational tools in an easy to use format. We describe the development of this platform, highlighting the advantages and disadvantages the methods used. In addition, we provide source code, and setup scripts to enable other research groups to create similar platforms, to promote collaboration both within and between research groups.

Crystal Structure Prediction (CSP) is a fundamental computational problem in materials science. Basin-hopping is a prominent CSP method that combines global Monte Carlo sampling to search over candidate trial structures with local energy minimisation of these candidates. The sampling uses a stochastic policy to randomly choose which action (such as a swap of atoms) will be used to transform the current structure into the next. Typically hand-tuned for a specific system before the run starts, such a policy is simply a fixed discrete probability distribution of possible actions, which does not depend on the current structure and does not adapt during a CSP run. We show that reinforcement learning (RL) can generate a dynamic policy that both depends on the current structure and improves on the fly during the CSP run. We demonstrate the efficacy of our approach on two CSP codes, FUSE and MC-EMMA. Specifically, we show that, when applied to the autonomous exploration of a phase field to identify the accessible crystal structures, RL can save up to 46% of the computation time.

In the search for novel positive electrode materials for lithium-ion cells, Li-rich sulfides are attracting increasing interest. Despite the success of polyoxyanion-based cathodes such as LiFePO4, their thiophosphate counterparts have remained largely unexplored. Here, the Li-rich thiophosphate Li2FeP2S6, which exhibits the highest known voltage (3 V) for a sulfide electrode, is investigated in a solid-state configuration. Through examination of isostructural transition-metal substitutions, we identify a novel Mn-substituted compound, Li2Fe0.8Mn0.2P2S6, with higher capacity than the parent Fe system while maintaining the high voltage. Hard X-ray Photoelectron Spectroscopy and ab initio molecular dynamics simulations indicate that Mn substitution activates P2S6 polyanionic redox involving interlayer S--S bond formation with no evidence of Fe or Mn cation migration, and increases capacity beyond the formal transition-metal redox limit. This demonstration of polyanionic redox in a thiophosphate material highlights the opportunity to explore alternative Li-rich thiophosphate structures as high-capacity lithium-ion cathodes.

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., Comment: 16 pages including references, 9 figures

High-performance and stability for protonic ceramic fuel cell cathode applications are realised in a self-assembled nanocomposite material Ba0.5Sr0.5(Co0.7Fe0.3)0.6875W0.3125O3−δ (BSCFW) through interplay of single and double perovskite phases.

Li-rich rocksalt oxides are promising candidates as high-energy density cathode materials for next-generation Li-ion batteries because they present extremely diverse structures and compositions. Most reported materials in this family contain as many cations as anions, a characteristic of the ideal cubic closed-packed rocksalt composition. In this work, a new rocksalt-derived structure type is stabilized by selecting divalent Cu and pentavalent Sb cations to favor the formation of oxygen vacancies during synthesis. The structure and composition of the oxygen-deficient Li4CuSbO5.5□0.5 phase is characterized by combining X-ray and neutron diffraction, ICP-OES, XAS, and magnetometry measurements. The ordering of cations and oxygen vacancies is discussed in comparison with the related Li2CuO2□1 and Li5SbO5□1 phases. The electrochemical properties of this material are presented, with only 0.55 Li+ extracted upon oxidation, corresponding to a limited utilization of cationic and/or anionic redox, whereas more than 2 Li+ ions can be reversibly inserted upon reduction to 1 V vs Li+/Li, a large capacity attributed to a conversion reaction and the reduction of Cu2+ to Cu0. Control of the formation of oxygen vacancies in Li-rich rocksalt oxides by selecting appropriate cations and synthesis conditions affords a new route for tuning the electrochemical properties of cathode materials for Li-ion batteries. Furthermore, the development of material models of the required level of detail to predict phase diagrams and electrochemical properties, including oxygen release in Li-rich rocksalt oxides, still relies on the accurate prediction of crystal structures. Experimental identification of new accessible structure types stabilized by oxygen vacancies represents a valuable step forward in the development of predictive models., Controlling the composition and synthesis conditions of Li-rich rocksalt oxides can lead to to new structure types stabilized by oxygen vacancies. The complex atomic ordering of Li4CuSbO5.5 is described and discussed in the context of other oxygen-deficient rocksalt oxides.

Journal of the American Chemical Society 143(43), 18216 - 18232 (2021). doi:10.1021/jacs.1c07874, Extended anionic frameworks based on condensation of polyhedral main group non-metal anions offer a wide range of structure types. Despite the widespread chemistry and earth abundance of phosphates and silicates, there are no reports of extended ultraphosphate anions with lithium. We describe the lithium ultraphosphates Li$_3$P$_5$O$_{14}$ and Li$_4$P$_6$O$_{17}$ based on extended layers and chains of phosphate, respectively. Li$_3$P$_5$O$_{14}$ presents a complex structure containing infinite ultraphosphate layers with 12-membered rings that are stacked alternately with lithium polyhedral layers. Two distinct vacant tetrahedral sites were identified at the end of two distinct finite Li$_6$O_{16}$^{26���}$ chains. Li$_4$P$_6$O$_{17}$ features a new type of loop-branched chain defined by six PO43��� tetrahedra. The ionic conductivities and electrochemical properties of Li$_3$P$_5$O$_{14}$ were examined by impedance spectroscopy combined with DC polarization, NMR spectroscopy, and galvanostatic plating/stripping measurements. The structure of Li$_3$P$_5$O$_{14}$ enables three-dimensional lithium migration that affords the highest ionic conductivity (8.5(5) �� 10$^{���7}$ S cm$^{���1}$ at room temperature for bulk), comparable to that of commercialized LiPON glass thin film electrolytes, and lowest activation energy (0.43(7) eV) among all reported ternary Li���P���O phases. Both new lithium ultraphosphates are predicted to have high thermodynamic stability against oxidation, especially Li$_3$P$_5$O$_{14}$ which is predicted to be stable to 4.8 V, significantly higher than that of LiPON and other solid electrolytes. The condensed phosphate units defining these ultraphosphate structures offer a new route to optimize the interplay of conductivity and electrochemical stability required, for example, in cathode coatings for lithium ion batteries., Published by American Chemical Society, Washington, DC

Exploration of chemical composition and structural configuration space is the central problem in crystal structure prediction. Even in limiting structure space to a single structure type, many different compositions and configurations are possible. In this work, we attempt to address this problem using an extension to the existing ChemDASH code in which variable compositions can be explored. We show that ChemDASH is an efficient method for exploring a fixed-composition space of spinel structures and build upon this to include variable compositions in the Mn-Fe-Zn-O spinel phase field. This work presents the first basin-hopping crystal structure prediction method that can explore variable compositions.

This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (NanoMOFdeli), ERC-2016-COG 726380, Innovate UK (104384) and EPSRC IAA (IAA/RG85685). N.R. acknowledges the support of the Cambridge International Scholarship and the TrinityHenry Barlow Scholarship (Honorary). O.K.F. and R.Q.S. acknowledge funding from the U.S. Department of Energy (DE-FG02-08ER15967). R.S.F. and D.B. acknowledge funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (SCoTMOF), ERC-2015-StG 677289. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology and Engineering Solutions of Sandia, LLC., a wholly owned subsidiary of Honeywell International, Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA-0003525. The authors gratefully acknowledge funding from the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, Hydrogen and Fuel Cell Technologies Office, through the Hydrogen Storage Materials Advanced Research Consortium (HyMARC). This paper describes objective technical results and analysis. Any subjective views or opinions that might be expressed in the paper do not necessarily represent the views of the U.S. Department of Energy or the United States Government. J.D.E. acknowledges the support of the Alexander von Humboldt Foundation and the Center for Information Services and High Performance Computing (ZIH) at TU Dresden. S.K.G. and S.M. acknowledge SERB (Project No. CRG/2019/000906), India for financial support. K.K. and R.K. acknowledge Active Co. Research Grant for funding. S.K. acknowledges funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (COSMOS), ERC-2017-StG 756489. N.L. and J.G.M acknowledge funding from the European Commission through the H2020-MSCA-RISE-2019 program (ZEOBIOCHEM -872102) and the Spanish MICINN and AEI/FEDER (RTI2018-099504-B-C21). N.L. thanks the University of Alicante for funding (UATALENTO17-05). ICN2 is supported by the Severo Ochoa program from the Spanish MINECO (Grant No. SEV-2017-0706) S.M.J.R. and A.L. wish to thank the Fund for Scientific Research Flanders (FWO), under grant nos. 12T3519N and 11D2220N. L.S. was supported by the EPSRC Cambridge NanoDTC EP/L015978/1. C.T.Y. and T.S.N. acknowledges funds from the National Research Foundation of Korea, NRF-2017M3A7B4042140 and NRF-2017M3A7B4042235. P.F. and H. Y. acknowledge US Department of Energy, Office of Basic Energy Sciences, Materials Sciences and Engineering Division under Award No. DE-SC0010596 (P.F.). R.O. would like to acknowledge funding support during his Ph.D. study from Indonesian Endowment Fund for Education-LPDP with the contract No. 202002220216006. Daniel Siderius: Official contribution of the National Institute of Standards and Technology (NIST), not subject to copyright in the United States of America. Daniel Siderius: Certain commercially available items may be identified in this paper. This identification does not imply recommendation by NIST, nor does it imply that it is the best available for the purposes described. B.V.L, S.T.E and A.M.P acknowledge funding from the European Research Council (ERC) under the European Union's Horizon 2020 Research and Innovation Program (Grant agreement no. 639233, COFLeaf)., Porosity and surface area analysis play a prominent role in modern materials science. At the heart of this sits the Brunauer–Emmett–Teller (BET) theory, which has been a remarkably successful contribution to the field of materials science. The BET method was developed in the 1930s for open surfaces but is now the most widely used metric for the estimation of surface areas of microand mesoporous materials. Despite its widespread use, the calculation of BET surface areas causes a spread in reported areas, resulting in reproducibility problems in both academia and industry. To prove this, for this analysis, 18 already-measured raw adsorption isotherms were provided to sixty-one labs, who were asked to calculate the corresponding BET areas. This roundrobin exercise resulted in a wide range of values. Here, the reproducibility of BET area determination from identical isotherms is demonstrated to be a largely ignored issue, raising critical concerns over the reliability of reported BET areas. To solve this major issue, a new computational approach to accurately and systematically determine the BET area of nanoporous materials is developed. The software, called “BET surface identification” (BETSI), expands on the well-known Rouquerol criteria and makes an unambiguous BET area assignment possible., European Research Council (ERC) ERC-2016-COG 726380 ERC-2015-StG 677289 ERC-2017-StG 756489 639233, UK Research & Innovation (UKRI) Innovate UK 104384 UK Research & Innovation (UKRI), Engineering & Physical Sciences Research Council (EPSRC) IAA/RG85685, Cambridge International Scholarship TrinityHenry Barlow Scholarship, United States Department of Energy (DOE) DE-FG02-08ER15967, National Nuclear Security Administration DE-NA-0003525, United States Department of Energy (DOE), Alexander von Humboldt Foundation, Center for Information Services and High Performance Computing (ZIH) at TU Dresden, Department of Science & Technology (India), Science Engineering Research Board (SERB), India CRG/2019/000906, Active Co. Research Grant, European Commission through the H2020-MSCA-RISE-2019 program ZEOBIOCHEM -872102, Spanish MICINN and AEI/FEDER RTI2018-099504-B-C21, University of Alicante UATALENTO17-05, Spanish Government SEV-2017-0706 FWO 12T3519N 11D2220N, UK Research & Innovation (UKRI), Engineering & Physical Sciences Research Council (EPSRC) EP/L015978/1, National Research Foundation of Korea NRF-2017M3A7B4042140 NRF-2017M3A7B4042235, United States Department of Energy (DOE) DE-SC0010596, Indonesian Endowment Fund for Education-LPDP 202002220216006

A proxy-based high-throughput experimental approach was used to explore the stability and activity of Co-based Fischer-Tropsch Synthesis catalysts with different promoters on a variety of supports. The protocol is based on XRD estimation of the active phase polymorph, particle size and ratio of crystalline phases of Co to support. Sequential sample libraries enabled exploration of four Co loadings with five different promoters on six support materials. Catalysts stable to aging in syngas, i.e. displaying minimal change of particle size or active phase concentration, were evaluated under industrial conditions. This procedure identified SiC as a support that confers catalyst stability and that a combination of Ru and Hf promotes the stabilisation of hcp Co. Unsupported bulk samples of Co with appropriate amounts of Ru and Hf revealed that the formation of hcp Co is independent of the support. The hcp Co-containing catalyst afforded the highest catalytic activity and C5+ selectivity amongst the samples tested in this study, confirming the effectiveness of the proxy-based high-throughput method.

The implementation of machine learning models has brought major changes in the decision-making process for materials design. One matter of concern for the data-driven approaches is the lack of negative data from unsuccessful synthetic attempts, which might generate inherently imbalanced datasets. We propose the application of the one-class classification methodology as an effective tool for tackling these limitations on the materials design problems. This is a concept of learning based only on a well-defined class without counter examples. An extensive study on the different one-class classification algorithms is performed until the most appropriate workflow is identified for guiding the discovery of emerging materials belonging to a relatively small class, that being the weakly bound polyaromatic hydrocarbon co-crystals. The two-step approach presented in this study first trains the model using all the known molecular combinations that form this class of co-crystals extracted from the Cambridge Structural Database (1722 molecular combinations), followed by scoring possible yet unknown pairs from the ZINC15 database (21 736 possible molecular combinations). Focusing on the highest-ranking pairs predicted to have higher probability of forming co-crystals, materials discovery can be accelerated by reducing the vast molecular space and directing the synthetic efforts of chemists. Further on, using interpretability techniques a more detailed understanding of the molecular properties causing co-crystallization is sought after. The applicability of the current methodology is demonstrated with the discovery of two novel co-crystals, namely pyrene-6H-benzo[c]chromen-6-one (1) and pyrene-9,10-dicyanoanthracene (2)., Machine learning using one class classification on a database of existing co-crystals enables the identification of co-formers which are likely to form stable co-crystals, resulting in the synthesis of two co-crystals of polyaromatic hydrocarbons.

We apply molecular simulations to screen a database of reported metal-organic framework structures from the computation-ready, experimental (CoRE) MOF database to identify materials potentially capable of separating propane and propene by diffusion. We report a screening workflow that uses descriptor analysis, conventional molecular dynamics (MD), and Nudged Elastic Band (NEB) energy barrier calculations at both classical force field and Density Functional Theory (DFT) levels. For the first time, the effects of framework flexibility on guest transport properties were fully considered in a screening process and led to the identification of candidate MOFs. The hits identified by this proof-of-concept workflow include ZIF-8 and ZIF-67 previously shown to have large differences in propane and propene diffusivities as well as two other materials that have not been tested experimentally yet. This work emphasises the importance of taking into account framework flexibility when studying guest transport in porous materials, demonstrates the potential of the data-driven identification of high-performance materials and highlights the ways of improving the predictive power of the screening workflow.

We present a new method of evolving crystal structures for crystal structure prediction. The method of chemically directed structure evolution uses chemical models to quantify the environment of atoms and vacancy sites in a crystal structure, with that information used to inform how to modify the structure to make a move on the potential energy surface. We have developed a method of chemically directed swapping, where we swap atoms in the least favourable chemical environments. This method has been implemented in the crystal structure prediction code ChemDASH (Chemically Directed Atom Swap Hopping), which explores the potential energy surface using a basin-hopping approach, and evaluates chemical environments using either the bond valence sum or electrostatic site potential. ChemDASH has a variety of methods of initialising structures, optimising structures, and swapping atoms. This gives ChemDASH the flexibility to be applied to a wide range of systems. We used ChemDASH to examine the effectiveness of the directed swapping method. Directed swapping finds the ground states of TiO2 and SrTiO3 faster than random (non-directed) swapping, but is less effective than random swapping for Y2Ti2O7.

The choice of metal and linker together define the structure and therefore the guest accessibility of a metal-organic framework (MOF), but the large number of possible metal-linker combinations makes the selection of components for synthesis challenging. We predict the guest accessibility of a MOF with 80.5 % certainty based solely on the identity of these two components as chosen by the experimentalist, by decomposing reported experimental three-dimensional MOF structures in the Cambridge Structural Database into metal and linker and then learning the connection between the components' chemistry and the MOF porosity. Pore dimensions of the guest-accessible space are classified into four ranges with three sequential models. Both the dataset and the predictive models are available to download and offer simple guidance in prioritization of the choice of the components for exploratory MOF synthesis for separation and catalysis based on guest accessibility considerations.

Making new van der Waals materials with electronic or magnetic functionality is a chemical design challenge for the development of two-dimensional nanoelectronic and energy conversion devices. We present the synthesis and properties of the van der Waals material Bi4O4SeCl2, which is a 1:1 superlattice of the structural units present in the van der Waals insulator BiOCl and the three-dimensionally connected semiconductor Bi2O2Se. The presence of three anions gives the new structure both the bridging selenide anion sites that connect pairs of Bi2O2 layers in Bi2O2Se and the terminal chloride sites that produce the van der Waals gap in BiOCl. This retains the electronic properties of Bi2O2Se, while reducing the dimensionality of the bonding network connecting the Bi2O2Se units to allow exfoliation of Bi4O4SeCl2 to 1.4 nm height. The superlattice structure is stabilised by the configurational entropy of anion disorder across the terminal and bridging sites. The reduction in connective dimensionality with retention of electronic functionality stems from the expanded anion compositional diversity.

A newly reported compound, CuAgBiI5, is synthesized as powder, crystals, and thin films. The structure consists of a 3D octahedral Ag+/Bi3+ network as in spinel, but occupancy of the tetrahedral interstitials by Cu+ differs from those in spinel. The 3D octahedral network of CuAgBiI5 allows us to identify a relationship between octahedral site occupancy (composition) and octahedral motif (structure) across the whole CuI-AgI-BiI3 phase field, giving the ability to chemically control structural dimensionality. To investigate composition-structure-property relationships, we compare the basic optoelectronic properties of CuAgBiI5 with those of Cu2AgBiI6 (which has a 2D octahedral network) and reveal a surprisingly low sensitivity to the dimensionality of the octahedral network. The absorption onset of CuAgBiI5 (2.02 eV) barely changes compared with that of Cu2AgBiI6 (2.06 eV) indicating no obvious signs of an increase in charge confinement. Such behavior contrasts with that for lead halide perovskites which show clear confinement effects upon lowering dimensionality of the octahedral network from 3D to 2D. Changes in photoluminescence spectra and lifetimes between the two compounds mostly derive from the difference in extrinsic defect densities rather than intrinsic effects. While both materials show good stability, bulk CuAgBiI5 powder samples are found to be more sensitive to degradation under solar irradiation compared to Cu2AgBiI6.

Since the emergence of lead halide perovskites for photovoltaic research, there has been mounting effort in the search for alternative compounds with improved or complementary physical, chemical, or optoelectronic properties. Here, we report the discovery of Cu2AgBiI6: a stable, inorganic, lead-free wide-band-gap semiconductor, well suited for use in lead-free tandem photovoltaics. We measure a very high absorption coefficient of 1.0 × 105 cm–1 near the absorption onset, several times that of CH3NH3PbI3. Solution-processed Cu2AgBiI6 thin films show a direct band gap of 2.06(1) eV, an exciton binding energy of 25 meV, a substantial charge-carrier mobility (1.7 cm2 V–1 s–1), a long photoluminescence lifetime (33 ns), and a relatively small Stokes shift between absorption and emission. Crucially, we solve the structure of the first quaternary compound in the phase space among CuI, AgI and BiI3. The structure includes both tetrahedral and octahedral species which are open to compositional tuning and chemical substitution to further enhance properties. Since the proposed double-perovskite Cs2AgBiI6 thin films have not been synthesized to date, Cu2AgBiI6 is a valuable example of a stable Ag+/Bi3+ octahedral motif in a close-packed iodide sublattice that is accessed via the enhanced chemical diversity of the quaternary phase space.

Polymer photocatalysts are a synthetically diverse class of materials that can be used for the production of solar fuels such as H2, but the underlying mechanisms by which they operate are poorly understood. Time-resolved vibrational spectroscopy provides a powerful structure-specific probe of photogenerated species. Here we report the use of time-resolved resonance Raman (TR3) spectroscopy to study the formation of polaron pairs and electron polarons in one of the most active linear polymer photocatalysts for H2 production, poly(dibenzo[b,d]thiophene sulfone), P10. We identify that polaron-pair formation prior to thermalization of the initially generated excited states is an important pathway for the generation of long-lived photoelectrons.

A newly reported compound, CuAgBiI5, is synthesized as powder, crystals, and thin films. The structure consists of a 3D octahedral Ag+/Bi3+ network as in spinel, but occupancy of the tetrahedral interstitials by Cu+ differs from those in spinel. The 3D octahedral network of CuAgBiI5 allows us to identify a relationship between octahedral site occupancy (composition) and octahedral motif (structure) across the whole CuI–AgI–BiI3 phase field, giving the ability to chemically control structural dimensionality. To investigate composition–structure–property relationships, we compare the basic optoelectronic properties of CuAgBiI5 with those of Cu2AgBiI6 (which has a 2D octahedral network) and reveal a surprisingly low sensitivity to the dimensionality of the octahedral network. The absorption onset of CuAgBiI5 (2.02 eV) barely changes compared with that of Cu2AgBiI6 (2.06 eV) indicating no obvious signs of an increase in charge confinement. Such behavior contrasts with that for lead halide perovskites which show clear confinement effects upon lowering dimensionality of the octahedral network from 3D to 2D. Changes in photoluminescence spectra and lifetimes between the two compounds mostly derive from the difference in extrinsic defect densities rather than intrinsic effects. While both materials show good stability, bulk CuAgBiI5 powder samples are found to be more sensitive to degradation under solar irradiation compared to Cu2AgBiI6., We describe a way to chemically control the octahedral network of potentially useful photovoltaic solar absorbers in the CuI−AgI−BiI3 phase space by the synthesis of CuAgBiI5 with a 3D octahedral network. We compare the photostability of CuAgBiI5 bulk samples and the absorption coefficient and photoluminescence of solution processed thin films with those of Cu2AgBiI6, which has a 2D octahedral network. This helps to understand structure−property relationships to direct further materials optimization.

The selection of the elements to combine delimits the possible outcomes of synthetic chemistry because it determines the range of compositions and structures, and thus properties, that can arise. For example, in the solid state, the elemental components of a phase field will determine the likelihood of finding a new crystalline material. Researchers make these choices based on their understanding of chemical structure and bonding. Extensive data are available on those element combinations that produce synthetically isolable materials, but it is difficult to assimilate the scale of this information to guide selection from the diversity of potential new chemistries. Here, we show that unsupervised machine learning captures the complex patterns of similarity between element combinations that afford reported crystalline inorganic materials. This model guides prioritisation of quaternary phase fields containing two anions for synthetic exploration to identify lithium solid electrolytes in a collaborative workflow that leads to the discovery of Li3.3SnS3.3Cl0.7. The interstitial site occupancy combination in this defect stuffed wurtzite enables a low-barrier ion transport pathway in hexagonal close-packing., Machine learning has the potential to significantly speed-up the discovery of new materials in synthetic materials chemistry. Here the authors combine unsupervised machine learning and crystal structure prediction to predict a novel quaternary lithium solid electrolyte that is then synthesized.

We report a new polymorph of lithium aluminum pyrophosphate, LiAlP2O7, discovered through a computationally guided synthetic exploration of the Li-Mg-Al-P-O phase field. The new polymorph formed at 973 K, and the crystal structure, solved by single-crystal X-ray diffraction, adopts the orthorhombic space group Cmcm with a = 5.1140(9) A, b = 8.2042(13) A, c = 11.565(3) A, and V = 485.22(17) A3. It has a three-dimensional framework structure that is different from that found in other LiMIIIP2O7 materials. It transforms to the known monoclinic form (space group P21) above ∼1023 K. Density functional theory (DFT) calculations show that the new polymorph is the most stable low-temperature structure for this composition among the seven known structure types in the AIMIIIP2O7 (A = alkali metal) families. Although the bulk Li-ion conductivity is low, as determined from alternating-current impedance spectroscopy and variable-temperature static 7Li NMR spectra, a detailed analysis of the topologies of all seven structure types through bond-valence-sum mapping suggests a potential avenue for enhancing the conductivity. The new polymorph exhibits long (>4 A) Li-Li distances, no Li vacancies, and an absence of Li pathways in the c direction, features that could contribute to the observed low Li-ion conductivity. In contrast, we found favorable Li-site topologies that could support long-range Li migration for two structure types with modest DFT total energies relative to the new polymorph. These promising structure types could possibly be accessed from innovative doping of the new polymorph.

We report a new polymorph of lithium aluminum pyrophosphate, LiAlP

International audience; The thermal conductivity of crystalline materials cannot be arbitrarily low, as the intrinsic limit depends on the phonon dispersion. We used complementary strategies to suppress the contribution of the longitudinal and transverse phonons to heat transport in layered materials that contain different types of intrinsic chemical interfaces. BiOCl and Bi2O2Se encapsulate these design principles for longitudinal and transverse modes, respectively, and the bulk superlattice material Bi4O4SeCl2 combines these effects by ordering both interface types within its unit cell to reach an extremely low thermal conductivity of 0.1 watts per kelvin per meter at room temperature along its stacking direction. This value comes within a factor of four of the thermal conductivity of air. We demonstrated that chemical control of the spatial arrangement of distinct interfaces can synergically modify vibrational modes to minimize thermal conductivity.

Mixed anion materials and anion doping are very promising strategies to improve solid-state electrolyte properties by enabling an optimized balance between good electrochemical stability and high ionic conductivity. In this work, we present the discovery of a novel lithium aluminum sulfide–chloride phase, obtained by substitution of chloride for sulfur in Li3AlS3 and Li5AlS4 materials. The structure is strongly affected by the presence of chloride anions on the sulfur site, as the substitution was shown to be directly responsible for the stabilization of a higher symmetry phase presenting a large degree of cationic site disorder, as well as disordered octahedral lithium vacancies. The effect of disorder on the lithium conductivity properties was assessed by a combined experimental–theoretical approach. In particular, the conductivity is increased by a factor 103 compared to the pure sulfide phase. Although it remains moderate (10–6 S·cm–1), ab initio molecular dynamics and maximum entropy (applied to neutron diffraction data) methods show that disorder leads to a 3D diffusion pathway, where Li atoms move thanks to a concerted mechanism. An understanding of the structure–property relationships is developed to determine the limiting factor governing lithium ion conductivity. This analysis, added to the strong step forward obtained in the determination of the dimensionality of diffusion, paves the way for accessing even higher conductivity in materials comprising an hcp anion arrangement.

We report the aperiodic titanate Ba10Y6Ti4O27 with a room‐temperature thermal conductivity that equals the lowest reported for an oxide. The structure is characterised by discontinuous occupancy modulation of each of the sites and can be considered as a quasicrystal. The resulting localisation of lattice vibrations suppresses phonon transport of heat. This new lead material for low‐thermal‐conductivity oxides is metastable and located within a quaternary phase field that has been previously explored. Its isolation thus requires a precisely defined synthetic protocol. The necessary narrowing of the search space for experimental investigation was achieved by evaluation of titanate crystal chemistry, prediction of unexplored structural motifs that would favour synthetically accessible new compositions, and assessment of their properties with machine‐learning models., Ba10Y6Ti4O27 has a complex aperiodic structure with the lowest thermal conductivity of any first‐transition‐series oxide. It is metastable, which makes it challenging to identify. Experimental exploration was focused sufficiently by structure prediction and property‐targeted machine learning to enable the isolation of this material (see picture), whose structural motifs provide new insight into the control of heat transport.

Lead-free silver–bismuth semiconductors have become increasingly popular materials for optoelectronic applications, building upon the success of lead halide perovskites. In these materials, charge-lattice couplings fundamentally determine charge transport, critically affecting device performance. In this study, we investigate the optoelectronic properties of the recently discovered lead-free semiconductor Cu2AgBiI6 using temperature-dependent photoluminescence, absorption, and optical-pump terahertz-probe spectroscopy. We report ultrafast charge-carrier localization effects, evident from sharp THz photoconductivity decays occurring within a few picoseconds after excitation and a rise in intensity with decreasing temperature of long-lived, highly Stokes-shifted photoluminescence. We conclude that charge carriers in Cu2AgBiI6 are subject to strong charge-lattice coupling. However, such small polarons still exhibit mobilities in excess of 1 cm2 V–1 s–1 at room temperature because of low energetic barriers to formation and transport. Together with a low exciton binding energy of ∼29 meV and a direct band gap near 2.1 eV, these findings highlight Cu2AgBiI6 as an attractive lead-free material for photovoltaic applications.