cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.24435/materialscloud:v1-rz
title: Mining the C-C Cross-Coupling Genome using Machine Learning

description: Applications of machine-learning (ML) techniques to the study of catalytic processes have begun to appear in the literature with increasing frequency. The computational speed up provided by ML allows the properties and energetics of thousands of prospective catalysts to be rapidly assessed. These results, once compiled into a database containing different properties, can be mined with the goal of establishing relationships between the intrinsic chemical properties of different catalysts and their overall catalytic performance. Previously, we applied ML models to predict the performance of 18,000 prospective catalysts for a Suzuki coupling reaction using molecular volcano plots. Here, we expand on our earlier work by examining a larger section of the C-C cross-coupling genome by using a dimensionality-reducing data-clustering algorithms (i.e., sketch-map) to, first, identify the compatibility of each catalyst with different C-C cross-coupling variants (e.g., Suzuki, Kumada, Negishi, Stille, and/or Hiyama) and, second, to uncover links between the chemical property of a catalyst and its catalytic activity. Our findings, based on the analysis of 18,000 catalysts, reveal strong correlations between a catalyst's HOMO energy and the suitability of its thermodynamic profile. These values can, subsequently, be tuned in order to maximize the thermodynamics of the catalytic cycle through the judicious choice of metal centers and the π-accepting/σ-donating nature of the flanking ligands. Overall, group 10 metals (Ni, Pd, Pt) are best coupled with the strong π-acceptor ligands and group 11 metals (Cu, Ag, Au) with weak π-acceptors, which maximize the thermodynamic drive of the catalytic cycle.
DOI: 10.24435/materialscloud:2019.0085/v1
title: Using collective knowledge to assign oxidation states

description: Knowledge of the oxidation state of a metal centre in a material is essential to understand its properties. Chemists have developed several theories to predict the oxidation state on the basis of the chemical formula. These methods are quite successful for simple compounds but often fail to describe the oxidation states of more complex systems, such as metal-organic frameworks. In this work, we present a data-driven approach to automatically assign oxidation states, using a machine learning algorithm trained on the assignments by chemists encoded in the chemical names in the Cambridge Crystallographic Database. Our approach only considers the immediate local chemical environment around a metal centre and, in this way, is robust to most of the experimental uncertainties in these structures (like incorrect protonation or unbound solvents). We find such excellent accuracy (>98%) in our predictions that we can use our method to identify a large number of incorrect assignments in the database. The predictions of our model follow chemical intuition, without explicitly having taught the model those heuristics. This work nicely illustrates how powerful the collective knowledge of chemists actually is. Machine learning can harvest this knowledge and convert it into a useful tool for chemists.
DOI: 10.24435/materialscloud:2019.0016/v3
title: Multithermal-multibaric molecular simulations from a variational principle

description: We present a method for performing multithermal-multibaric molecular dynamics simulations that sample entire regions of the temperature-pressure (TP) phase diagram. The method uses a variational principle [Valsson and Parrinello, Phys. Rev. Lett. 113, 090601 (2014)] in order to construct a bias that leads to a uniform sampling in energy and volume. The intervals of temperature and pressure are taken as inputs and the relevant energy and volume regions are determined on the fly. In this way the method guarantees adequate statistics for the chosen TP region. We show that our multithermal-multibaric simulations can be used to calculate all static physical quantities for all temperatures and pressures in the targeted region of the TP plane. We illustrate our approach by studying the density anomaly of TIP4P/Ice water.

This record includes input and output files, and Jupyter Notebooks describing the analysis of the simulations and the creation of the figures for the paper.
DOI: 10.24435/materialscloud:dd-qg
title: Multithermal-multibaric molecular simulations from a variational principle

description: We present a method for performing multithermal-multibaric molecular dynamics simulations that sample entire regions of the temperature-pressure (TP) phase diagram. The method uses a variational principle [Valsson and Parrinello, Phys. Rev. Lett. 113, 090601 (2014)] in order to construct a bias that leads to a uniform sampling in energy and volume. The intervals of temperature and pressure are taken as inputs and the relevant energy and volume regions are determined on the fly. In this way the method guarantees adequate statistics for the chosen TP region. We show that our multithermal-multibaric simulations can be used to calculate all static physical quantities for all temperatures and pressures in the targeted region of the TP plane. We illustrate our approach by studying the density anomaly of TIP4P/Ice water.

This record includes input and output files, and Jupyter Notebooks describing the analysis of the simulations and the creation of the figures for the paper.
DOI: 10.24435/materialscloud:dq-ey
title: Using collective knowledge to assign oxidation states

description: Knowledge of the oxidation state of a metal centre in a material is essential to understand its properties. Chemists have developed theories to predict the oxidation state based on counting rules, which can fail to describe the oxidation states of systems such as metal-organic frameworks.
Here we present a data-driven approach to automatically assign oxidation states, using a machine learning model trained on assignments by chemists encoded in the chemical names in the Cambridge Crystallographic Database.
Our approach only considers the immediate local environment around a metal centre, and is robust to experimental uncertainties (like incorrect protonation, unbound solvents, or changes in bondlength).
We find such excellent accuracy in our predictions that we can use our method to detect incorrect assignments. 
This work nicely illustrates how powerful the collective knowledge of chemists is. Machine learning can harvest this knowledge and convert it into a useful tool for chemists.
DOI: 10.24435/materialscloud:2020.0031/v1
title: Learning the energy curvature versus particle number in approximate density functionals

description: The average energy curvature as a function of the particle number is a molecule-specific quantity, which measures the deviation of a given functional from the exact conditions of density functional theory (DFT). Related to the lack of derivative discontinuity in approximate exchange-correlation potentials, the information about the curvature has been successfully used to restore the physical meaning of Kohn-Sham orbital eigenvalues and to develop non-empirical tuning and correction schemes for density functional approximations. In this work, we propose the construction of a machine-learning framework targeting the average energy curvature between the neutral and the radical cation state of thousands of small organic molecules (QM7 database). The applicability of the model is demonstrated in the context of system-specific gamma-tuning of the LC-ωPBE functional and validated against the molecular first ionization potentials at equation-of-motion (EOM) coupled-cluster references. In addition, we propose a local version of the non-linear regression model and demonstrate its transferability and predictive power by determining the optimal range-separation parameter for two large molecules relevant to the field of hole-transporting materials. Finally, we explore the underlying structure of the QM7 database with the t-SNE dimensionality-reduction algorithm and identify structural and compositional patterns that promote the deviation from the piecewise linearity condition.
DOI: 10.24435/materialscloud:p2-k0
title: Using collective knowledge to assign oxidation states

description: Knowledge of the oxidation state of a metal centre in a material is essential to understand its properties. Chemists have developed theories to predict the oxidation state based on counting rules, which can fail to describe the oxidation states of systems such as metal-organic frameworks.
Here we present a data-driven approach to automatically assign oxidation states, using a machine learning model trained on assignments by chemists encoded in the chemical names in the Cambridge Crystallographic Database.
Our approach only considers the immediate local environment around a metal centre, and is robust to experimental uncertainties (like incorrect protonation, unbound solvents, or changes in bondlength).
We find such excellent accuracy in our predictions that we can use our method to detect incorrect assignments. 
This work nicely illustrates how powerful the collective knowledge of chemists is. Machine learning can harvest this knowledge and convert it into a useful tool for chemists.
DOI: 10.24435/materialscloud:g6-zv
title: Learning the energy curvature versus particle number in approximate density functionals

description: The average energy curvature as a function of the particle number is a molecule-specific quantity, which measures the deviation of a given functional from the exact conditions of density functional theory (DFT). Related to the lack of derivative discontinuity in approximate exchange-correlation potentials, the information about the curvature has been successfully used to restore the physical meaning of Kohn-Sham orbital eigenvalues and to develop non-empirical tuning and correction schemes for density functional approximations. In this work, we propose the construction of a machine-learning framework targeting the average energy curvature between the neutral and the radical cation state of thousands of small organic molecules (QM7 database). The applicability of the model is demonstrated in the context of system-specific gamma-tuning of the LC-ωPBE functional and validated against the molecular first ionization potentials at equation-of-motion (EOM) coupled-cluster references. In addition, we propose a local version of the non-linear regression model and demonstrate its transferability and predictive power by determining the optimal range-separation parameter for two large molecules relevant to the field of hole-transporting materials. Finally, we explore the underlying structure of the QM7 database with the t-SNE dimensionality-reduction algorithm and identify structural and compositional patterns that promote the deviation from the piecewise linearity condition.
DOI: 10.24435/materialscloud:2019.0018/v1
title: MgTa2N3: A reference Dirac semimetal

description: MgTa2N3 is predicted to host the topological Dirac semimetal phase. This archive includes input data necessary for reproducing first-principles calculation described in the publication.
DOI: 10.24435/materialscloud:k7-83
title: MgTa2N3: A reference Dirac semimetal

description: MgTa2N3 is predicted to host the topological Dirac semimetal phase. This archive includes input data necessary for reproducing first-principles calculation described in the publication.
DOI: 10.24435/materialscloud:2019.0019/v2
title: TopoMat: a database of high-throughput first-principles calculations of topological materials

description: We present a database of topological materials predicted from high-throughput first-principles calculations. The database contains electronic band structures and topological indices of 13628 materials calculated on experimental crystal structures taken from the Inorganic Crystal Structure Database (ICSD) and the Crystallography Open Database (COD). The calculations have been performed on non-magnetic phases taking into account the spin-orbit interactions using the Quantum ESPRESSO package. The Fu-Kane method and the Wannier charge center method implemented in the Z2pack code have been utilized to calculate the Z2 topological numbers of centrosymmetric and non-centrosymmetric materials, respectively. Over 4000 topologically non-trivial materials have been identified.
DOI: 10.24435/materialscloud:gr-w3
title: A unified approach to enhanced sampling

description: The sampling problem lies at the heart of atomistic simulations and over the years many different enhanced sampling methods have been suggested towards its solution. These methods are often grouped into two broad families. On the one hand methods such as umbrella sampling and metadynamics that build a bias potential based on few order parameters or collective variables. On the other hand tempering methods such as replica exchange that combine different thermodynamic ensembles in one single expanded ensemble. We adopt instead a unifying perspective, focusing on the target probability distribution sampled by the different methods. This allows us to introduce a new method that can sample any of the ensembles normally sampled via replica exchange, but does so in a collective-variables-based scheme. This method is an extension of the recently developed on-the-fly probability enhanced sampling method [Invernizzi and Parrinello, J. Phys. Chem. Lett. 11.7 (2020)] that has been previously used for metadynamics-like sampling. The method is thus very general and can be used to achieve different types of enhanced sampling. It is also reliable and simple to use, since it presents only few and robust external parameters and has a straightforward reweighting scheme. Furthermore, it can be used with any number of parallel replicas. We show the versatility of our approach with applications to multicanonical and multithermal-multibaric simulations, thermodynamic integration, umbrella sampling, and combinations thereof.
DOI: 10.24435/materialscloud:2019.0019/v1
title: TopoMat: a database of high-throughput first-principles calculations of topological materials

description: We present a database of topological materials predicted from high-throughput first-principles calculations. The database contains electronic band structures and topological indices of 13628 materials calculated on experimental crystal structures taken from the Inorganic Crystal Structure Database (ICSD) and the Crystallography Open Database (COD). The calculations have been performed on non-magnetic phases taking into account the spin-orbit interactions using the Quantum ESPRESSO package. The Fu-Kane method and the Wannier charge center method implemented in the Z2pack code have been utilized to calculate the Z2 topological numbers of centrosymmetric and non-centrosymmetric materials, respectively. Over 4000 topologically non-trivial materials have been identified.
DOI: 10.24435/materialscloud:zt-5n
title: A unified approach to enhanced sampling

description: The sampling problem lies at the heart of atomistic simulations and over the years many different enhanced sampling methods have been suggested towards its solution. These methods are often grouped into two broad families. On the one hand methods such as umbrella sampling and metadynamics that build a bias potential based on few order parameters or collective variables. On the other hand tempering methods such as replica exchange that combine different thermodynamic ensembles in one single expanded ensemble. We adopt instead a unifying perspective, focusing on the target probability distribution sampled by the different methods. This allows us to introduce a new method that can sample any of the ensembles normally sampled via replica exchange, but does so in a collective-variables-based scheme. This method is an extension of the recently developed on-the-fly probability enhanced sampling method [Invernizzi and Parrinello, J. Phys. Chem. Lett. 11.7 (2020)] that has been previously used for metadynamics-like sampling. The method is thus very general and can be used to achieve different types of enhanced sampling. It is also reliable and simple to use, since it presents only few and robust external parameters and has a straightforward reweighting scheme. Furthermore, it can be used with any number of parallel replicas. We show the versatility of our approach with applications to multicanonical and multithermal-multibaric simulations, thermodynamic integration, umbrella sampling, and combinations thereof.
DOI: 10.24435/materialscloud:2019.0083/v1
title: Geometric landscapes for material discovery within energy-structure-function maps

description: Porous molecular crystals are an emerging class of porous materials formed by crystallisation of molecules with weak intermolecular interactions, which distinguishes them from extended nanoporous materials like metal-organic frameworks (MOFs). To aid discovery of porous molecular crystals for desired applications, energy-structure-function (ESF) maps were developed that combine a priori prediction of both the crystal structure and its functional properties. However, it is a challenge to represent the high-dimensional structural and functional landscapes of an ESF map and to identify energetically favourable and functionally interesting polymorphs among the 1,000s-10,000s of structures typically on a single ESF map. Here, we introduce geometric landscapes, a representation for ESF maps based on geometric similarity, quantified by persistent homology. We show that this representation allows the exploration of complex ESF maps, automatically pinpointing interesting crystalline phases available to the molecule. Furthermore, we show that geometric landscapes can serve as an accountable descriptor for porous materials to predict their performance for gas adsorption applications. A machine learning model trained using this geometric similarity could reach a remarkable accuracy in predicting the materials' performance for methane storage applications.
DOI: 10.24435/materialscloud:vm-xn
title: TopoMat: a database of high-throughput first-principles calculations of topological materials

description: We present a database of topological materials predicted from high-throughput first-principles calculations. The database contains electronic band structures and topological indices of 13628 materials calculated on experimental crystal structures taken from the Inorganic Crystal Structure Database (ICSD) and the Crystallography Open Database (COD). The calculations have been performed on non-magnetic phases taking into account the spin-orbit interactions using the Quantum ESPRESSO package. The Fu-Kane method and the Wannier charge center method implemented in the Z2pack code have been utilized to calculate the Z2 topological numbers of centrosymmetric and non-centrosymmetric materials, respectively. Over 4000 topologically non-trivial materials have been identified.
DOI: 10.24435/materialscloud:y7-5e
title: Geometric landscapes for material discovery within energy-structure-function maps

description: Porous molecular crystals are an emerging class of porous materials formed by crystallisation of molecules with weak intermolecular interactions, which distinguishes them from extended nanoporous materials like metal-organic frameworks (MOFs). To aid discovery of porous molecular crystals for desired applications, energy-structure-function (ESF) maps were developed that combine a priori prediction of both the crystal structure and its functional properties. However, it is a challenge to represent the high-dimensional structural and functional landscapes of an ESF map and to identify energetically favourable and functionally interesting polymorphs among the 1,000s-10,000s of structures typically on a single ESF map. Here, we introduce geometric landscapes, a representation for ESF maps based on geometric similarity, quantified by persistent homology. We show that this representation allows the exploration of complex ESF maps, automatically pinpointing interesting crystalline phases available to the molecule. Furthermore, we show that geometric landscapes can serve as an accountable descriptor for porous materials to predict their performance for gas adsorption applications. A machine learning model trained using this geometric similarity could reach a remarkable accuracy in predicting the materials' performance for methane storage applications.
DOI: 10.24435/materialscloud:4b-jt
title: Coupled spin states in armchair graphene nanoribbons with asymmetric zigzag edge extensions

description: In this record we provide data supporting our recent work on coupled spin states in armchair nanoribbons. Exact positioning of sublattice imbalanced nanostructures in graphene nanomaterials offers a route to control interactions between induced local magnetic moments and to obtain graphene nanomaterials with magnetically nontrivial ground states. Our results reveal that such sublattice imbalanced nanostructures can be incorporated along a large band gap armchair graphene nanoribbon on the basis of asymmetric zigzag edge extensions, achieved by incorporating specifically designed precursor monomers. Scanning tunneling spectroscopy of an isolated and electronically decoupled zigzag edge extension reveals Hubbard-split states in accordance with theoretical predictions. Mean-field Hubbard-based modeling of pairs of such zigzag edge extensions reveals ferromagnetic, antiferromagnetic, or quenching of the magnetic interactions depending on the relative alignment of the asymmetric edge extensions. Moreover, a ferromagnetic spin chain is demonstrated for a periodic pattern of zigzag edge extensions along the nanoribbon axis.
DOI: 10.24435/materialscloud:60-4a
title: Coupled spin states in armchair graphene nanoribbons with asymmetric zigzag edge extensions

description: In this record we provide data supporting our recent work on coupled spin states in armchair nanoribbons. Exact positioning of sublattice imbalanced nanostructures in graphene nanomaterials offers a route to control interactions between induced local magnetic moments and to obtain graphene nanomaterials with magnetically nontrivial ground states. Our results reveal that such sublattice imbalanced nanostructures can be incorporated along a large band gap armchair graphene nanoribbon on the basis of asymmetric zigzag edge extensions, achieved by incorporating specifically designed precursor monomers. Scanning tunneling spectroscopy of an isolated and electronically decoupled zigzag edge extension reveals Hubbard-split states in accordance with theoretical predictions. Mean-field Hubbard-based modeling of pairs of such zigzag edge extensions reveals ferromagnetic, antiferromagnetic, or quenching of the magnetic interactions depending on the relative alignment of the asymmetric edge extensions. Moreover, a ferromagnetic spin chain is demonstrated for a periodic pattern of zigzag edge extensions along the nanoribbon axis.