jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Esther Heid | Jiannan Liu | Andrea Aude | William H. Green
Source:  (preprint, 2021-09-29)
Field: Materials Science — Materials Chemistry

On the influence of template size, canonicalization and exclusivity for retrosynthesis and reaction prediction applications Heuristic and machine learning models for rank-ordering reaction templates comprise an important basis for computer-aided organic synthesis regarding both product prediction and retrosynthetic pathway planning. Their viability relies heavily on the quality and characteristics of the underlying template database. With the advent of automated reaction and template extraction software and consequently the creation of template databases too large to be curated manually, a data-driven approach to assess and improve the quality of template sets is needed. We therefore systematically studied the influence of template generality, canonicalization and exclusivity on the performance of different template ranking models. We find that duplicate and non-exclusive templates, \textit{i.e.} templates which describe the same chemical transformation on identical or overlapping sets of molecules, decrease both the accuracy of the ranking algorithm and the applicability of the respective top-ranked templates significantly. To remedy the negative effects of non-exclusivity, we developed a general and computationally efficient framework to deduplicate and hierarchically correct templates. As a result, performance improved for both heuristic and machine learning template ranking algorithms across different template sizes. The canonicalization and correction code was made freely available. Template ; Generality ; Computer science ; Ranking (information retrieval) ; Heuristic ; Data mining ; Quality (philosophy) ; Retrosynthetic analysis ; Artificial intelligence ; Rank (graph theory) ; Transformation (genetics) ; Machine learning ; Mathematics ; Programming language Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Zhexu Xi
Source: arXiv (Cornell University) (preprint, 2021-08-16)
Field: Materials Science — Materials Chemistry

Functional Nanomaterials Design in the Workflow of Building Machine-Learning Models Machine-learning (ML) techniques have revolutionized a host of research fields of chemical and materials science with accelerated, high-efficiency discoveries in design, synthesis, manufacturing, characterization and application of novel functional materials, especially at the nanometre scale. The reason is the time efficiency, prediction accuracy and good generalization abilities, which gradually replaces the traditional experimental or computational work. With enormous potentiality to tackle more real-world problems, ML provides a more comprehensive insight into combinations with molecules/materials under the fundamental procedures for constructing ML models, like predicting properties or functionalities from given parameters, nanoarchitecture design and generating specific models for other purposes. The key to the advances in nanomaterials discovery is how input fingerprints and output values can be linked quantitatively. Finally, some great opportunities and technical challenges are concluded in this fantastic field. Workflow ; Computer science ; Generalization ; Field (mathematics) ; Characterization (materials science) ; Scale (ratio) ; Systems engineering ; Nanotechnology ; Key (lock) ; Biochemical engineering ; Artificial intelligence ; Industrial engineering ; Machine learning ; Engineering ; Materials science ; Mathematics Machine Learning in Materials Science ; Advanced Nanomaterials in Catalysis
Authors: Yuki Mochizuki | Tomoharu Okada | Hiroyuki Matsui
Source: The Japan Society of Applied Physics (article, 2021-07-06)
Field: Chemistry — Physical and Theoretical Chemistry

Improving the Accuracy of HOMO/LUMO Prediction of Organic Molecules by Machine Learning HOMO/LUMO ; Materials science ; Organic molecules ; Molecule ; Artificial intelligence ; Nanotechnology ; Machine learning ; Organic chemistry ; Computer science Various Chemistry Research Topics ; Computational Drug Discovery Methods ; Machine Learning in Materials Science
Authors: Paul J. Atzberger
Source: arXiv (Cornell University) (preprint, 2021-07-29)
Field: Materials Science — Materials Chemistry

MLMOD: Machine Learning Methods for Data-Driven Modeling in LAMMPS MLMOD is a software package for incorporating machine learning approaches and models into simulations of microscale mechanics and molecular dynamics in LAMMPS. Recent machine learning approaches provide promising data-driven approaches for learning representations for system behaviors from experimental data and high fidelity simulations. The package faciliates learning and using data-driven models for (i) dynamics of the system at larger spatial-temporal scales (ii) interactions between system components, (iii) features yielding coarser degrees of freedom, and (iv) features for new quantities of interest characterizing system behaviors. MLMOD provides hooks in LAMMPS for (i) modeling dynamics and time-step integration, (ii) modeling interactions, and (iii) computing quantities of interest characterizing system states. The package allows for use of machine learning methods with general model classes including Neural Networks, Gaussian Process Regression, Kernel Models, and other approaches. Here we discuss our prototype C++/Python package, aims, and example usage. The package is integrated currently with the mesocale and molecular dynamics simulation package LAMMPS and PyTorch. For related papers, examples, updates, and additional information see https://github.com/atzberg/mlmod and http://atzberger.org/. Python (programming language) ; Computer science ; Artificial intelligence ; Machine learning ; Programming language Machine Learning in Materials Science ; Protein Structure and Dynamics ; Gaussian Processes and Bayesian Inference
Authors: Sarah Reehl | Elizabeth J. Kautz | Matthew J. Olszta | Derek Hopkins | Bethany E. Matthews | Le Wang | Yingge Du | Steven R. Spurgeon
Source: Microscopy and Microanalysis (article, 2021-07-30)
Field: Materials Science — Surfaces, Coatings and Films

Rapid and Flexible Few Shot Learning-Based Classification of Scanning Transmission Electron Microscopy Data Control of property-defining materials defects for quantum computing and energy storage depends on the ability to precisely probe structure and chemistry at the highest spatial and temporal resolutions. Modern scanning transmission electron microscopy (STEM) is well-suited to this task, having yielded rich insights into defect populations in many systems. However, the dilute nature and complexity of materials defects, coupled with their varied representations in STEM data, makes reliable, accurate, high-throughput statistical defect analysis a significant challenge. Possible analysis approaches include low-level pixel processing, or even the application of machine learning methods for classification and image segmentation. However, the latter requires large sets of labeled training data that are difficult to obtain for many practical materials science studies. Here, we describe the use of an emerging few shot learning capability for rapid and flexible STEM data classification. This approach requires minimal information at the start of the analysis and uses a generally pre-trained encoder network to make inferences on experimental data. Our results show drastic improvements in data annotation costs, reproducibility, and scalability in comparison to neural network training from scratch. We demonstrate how few shot techniques can quickly extract feature maps and global statistics from a variety of STEM data, enabling a new quantitative understanding of defect populations. Single shot ; Shot (pellet) ; Content (measure theory) ; Materials science ; Transmission (telecommunications) ; Computer science ; Transmission electron microscopy ; One shot ; Nanotechnology ; Optics ; Physics ; Engineering ; Telecommunications ; Mathematics ; Mechanical engineering ; Metallurgy Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications ; Machine Learning in Materials Science
Authors: Michael Maser | Sarah E. Reisman
Source:  (preprint, 2021-07-12)
Field: Materials Science — Materials Chemistry

3D Computer Vision Models Predict DFT-Level HOMO-LUMO Gap Energies from Force-Field-Optimized Geometries We investigate 3D deep learning methods for predicting quantum mechanical energies at high-theory-level accuracy from inexpensive, rapidly computed molecular geometries. Using space-filled volumetric representations (voxels), we explore the effects of radial decay from atom centers and rotational data augmentation on learnability. We test several published computer vision models for 3D shape learning, and construct our own architecture based on 3D inception networks with physically meaningful kernels. We provide a framework for further studies and propose a modeling challenge for the computer vision and molecular machine learning communities. Learnability ; Computer science ; Voxel ; Artificial intelligence ; Deep learning ; Construct (python library) ; Machine learning Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Electron and X-Ray Spectroscopy Techniques
Authors: Paolo Restuccia | E. Ahmad | N. M. Harrison
Source: arXiv (Cornell University) (preprint, 2021-08-20)
Field: Materials Science — Materials Chemistry

A predictive model of molecular adsorption to metals based on ab initio calculations The description of adsorption to surfaces and interfaces is essential in many technological fields, such as catalysis, corrosion and friction. For example, an accurate prediction of the structure and properties of molecules on substrates is essential to both the design and optimisation of coatings used to protect and enhance the properties of metals. We introduce a new model for the prediction of adsorption energies of molecules to metal substrates. The model is based on easily computed, intrinsic properties of the substrate and molecules (e.g., bulk cohesive energy, work function, molecule-cluster binding and the molecular orbital energy gap). It is parameterised by combining ab initio calculations with machine learning algorithms. The model is trained using a small set of probe molecules (e.g., H$_2$O, CO$_2$, O$_2$, N$_2$) adsorbed to a range of pure metal substrates. The model predicts the computed dissociative adsorption energy to metal surfaces with a correlation coefficient of 0.93 and a mean absolute error of 0.77 eV for the large database of molecular adsorptions stored in the this http URL database. The model therefore significantly enhances current efforts to identify new molecular coatings in many research fields and thus facilitates the discovery of new environmentally friendly and inexpensive materials with specific adhesion characteristics. Adsorption ; Ab initio ; Molecule ; Materials science ; Binding energy ; Cluster expansion ; Computational chemistry ; Cluster (spacecraft) ; Metal ; Chemical physics ; Chemistry ; Thermodynamics ; Physical chemistry ; Computer science ; Atomic physics ; Physics ; Organic chemistry Machine Learning in Materials Science ; Molecular Junctions and Nanostructures ; Advanced Chemical Physics Studies
Authors: Yuxing Zhou | William Kirkpatrick | Volker L. Deringer
Source: Advanced Materials (preprint, 2021-11-03)
Field: Materials Science — Materials Chemistry

Cluster Fragments in Amorphous Phosphorus and their Evolution under Pressure Amorphous phosphorus (a-P) has long attracted interest because of its complex atomic structure, and more recently as an anode material for batteries. However, accurately describing and understanding a-P at the atomistic level remains a challenge. Here, it is shown that large-scale molecular-dynamics simulations, enabled by a machine-learning (ML)-based interatomic potential for phosphorus, can give new insights into the atomic structure of a-P and how this structure changes under pressure. The structural model so obtained contains abundant five-membered rings, as well as more complex seven- and eight-atom clusters. Changes in the simulated first sharp diffraction peak during compression and decompression indicate a hysteresis in the recovery of medium-range order. An analysis of cluster fragments, large rings, and voids suggests that moderate pressure (up to about 5 GPa) does not break the connectivity of clusters, but higher pressure does. The work provides a starting point for further computational studies of the structure and properties of a-P, and more generally it exemplifies how ML-driven modeling can accelerate the understanding of disordered functional materials. Cluster (spacecraft) ; Chemical physics ; Amorphous solid ; Molecular dynamics ; Atomic units ; Materials science ; Hysteresis ; Diffraction ; Phosphorus ; Nanotechnology ; Crystallography ; Chemistry ; Physics ; Computational chemistry ; Computer science ; Condensed matter physics ; Metallurgy ; Optics Machine Learning in Materials Science ; Diamond and Carbon-based Materials Research ; 2D Materials and Applications
Authors: Andrew E. Sifain | Levi Lystrom | Richard A. Messerly | Justin S. Smith | Benjamin Nebgen | Kipton Barros | Sergei Tretiak | Nicholas Lubbers | Brendan J. Gifford
Source:  (preprint, 2021-06-30)
Field: Materials Science — Materials Chemistry

Predicting Phosphorescence Energies and Inferring Wavefunction Localization with Machine Learning &lt;p&gt;Phosphorescence is commonly utilized for applications including light-emitting diodes and photovoltaics. Machine learning (ML) approaches trained on &lt;i&gt;ab-initio&lt;/i&gt; datasets of singlet-triplet energy gaps may expedite the discovery of phosphorescent compounds with the desired emission energies. However, we show that standard ML approaches for modeling potential energy surfaces inaccurately predict singlet-triplet energy gaps due to the failure to account for spatial localities of spin transitions. To solve this, we introduce localization layers in a neural network model that weight atomic contributions to the energy gap, thereby allowing the model to isolate the most determinative chemical environments. Trained on the singlet-triplet energy gaps of organic molecules, we apply our method to an out-of-sample test set of large phosphorescent compounds and demonstrate the substantial improvement that localization layers have on predicting their phosphorescence energies. Remarkably, the inferred localization weights have a strong relationship with the &lt;i&gt;ab-initio&lt;/i&gt; spin density of the singlet-triplet transition, and thus infer localities of the molecule that determine the spin transition, despite the fact that no direct electronic information was provided during training. The use of localization layers is expected to improve the modeling of many localized, non-extensive phenomena and could be implemented in any atom-centered neural network model.&lt;/p&gt; Phosphorescence ; Singlet state ; Ab initio ; Artificial neural network ; Energy (signal processing) ; Physics ; Chemistry ; Computer science ; Atomic physics ; Artificial intelligence ; Excited state ; Quantum mechanics ; Fluorescence Machine Learning in Materials Science
Authors: Winkler, David | Fronzi, Marco | Sherrell, Peter | Ellis, Amanda | Ford, Mike | Shapter, Joseph
Source: Open MIND (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

A Bright Future for Engineering 2D Crystal Piezoelectricity The piezoelectric effect, mechanical-to-electrical and electrical-to-mechanical energy conversion, is highly beneficial for functional and responsive electronic devices. To fully exploit this property, miniaturization of piezoelectric materials is the subject of intense research. Indeed, select atomically thin 2D materials strongly exhibit the piezoelectric effect. The family of 2D crystals consists of over 7000 chemically distinct members that can be further manipulated in terms of strain, functionalization, elemental substitution (i.e. Janus 2D crystals), and defect engineering to induce a piezoelectric response. Additionally, most 2D crystals can stack with othersimilar or dissimilar 2D crystalsto form a much greater number of complex 2D heterostructures whose properties are quite different to those of the individual constituents. The unprecedented flexibility in tailoring 2D crystal properties, coupled with their minimal thickness, make these emerging highly attractive for advanced piezoelectric applications that include pressure sensing, piezocatalysis, piezotronics, and energy harvesting. This review summarizes literature on piezoelectricity, particularly out-of-plane piezoelectricity, in the vast family of 2D materials as well as their heterostructures. It also describes methods to induce, enhance, and control the piezoelectric properties. The volume of data and role of machine learning in predicting piezoelectricity is discussed in detail, and a prospective outlook on the 2D piezoelectric field is provided. Piezoelectricity ; Stack (abstract data type) ; Miniaturization ; Piezoelectric coefficient ; Flexibility (engineering) ; Crystal (programming language) ; Heterojunction 2D Materials and Applications ; Advanced Sensor and Energy Harvesting Materials ; MXene and MAX Phase Materials
Authors: Graeme M. Day
Source: Acta Crystallographica Section A Foundations and Advances (article, 2021-08-14)
Field: Materials Science — Materials Chemistry

Learning structure–energy relationships for the prediction of molecular crystal structures The discovery of new functional materials can be guided by computational screening, particularly if the structure of a material can be reliably predicted from its chemical composition.For this application, we have been developing the use energy-structure-function maps [1] of the crystal structures available to a molecule.These maps help understand the properties of predicted crystal structures and their energetic stabilities.However, the use of these methods is still limited by the computational cost of crystal structure prediction (CSP), most of which is associated with the calculation of the relative energies of predicted crystal structures using energy models that are sufficiently accurate to provide reliable energetic rankings.To accelerate these methods, we have been developing machine learning approaches to predict high quality energies (e.g. from solid state density functional theory) from structures that have been generated with computationally efficient energy models.These approaches rely on statistical models, in our case Gaussian Process Regression, to relate lattice energies to geometric descriptors of crystal structures.The talk will discuss two approaches that we have developed: learning of total energies calculated using solid state density functional theory [2,3], and a fragment-based approach [4] where we learn high level dimer energies, which are used to build up the total lattice energies of predicted structures. Crystal structure prediction ; Density functional theory ; Crystal structure ; Lattice energy ; Lattice (music) ; Statistical physics ; Crystal (programming language) ; Gaussian ; Computer science ; Algorithm ; Artificial intelligence ; Machine learning ; Physics ; Chemistry ; Computational chemistry ; Crystallography Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Anna Lucia de Marco | Davide Bochicchio | Andrea Gardin | Giovanni Doni | Giovanni M. Pavan
Source: ChemRxiv (preprint, 2021-01-29)
Field: Materials Science — Biomaterials

Controlling Exchange Pathways in Dynamic Supramolecular Polymers by Controlling Defects Supramolecular fibers, composed of monomers that self-assemble directionally &lt;i&gt;via&lt;/i&gt; non-covalent interactions, are ubiquitous in nature and of great interest in chemistry. In these structures, the constitutive monomers continuously exchange in-and-out the assembly according to a well-defined supramolecular equilibrium. However, unraveling the exchange pathways and their molecular determinants constitutes a non-trivial challenge. Here we combine coarse-grained modeling, enhanced sampling, and machine learning to investigate the key factors controlling the monomer exchange pathways in synthetic supramolecular polymers having an intrinsic dynamic behavior. We demonstrate how the competition of directional &lt;i&gt;vs. &lt;/i&gt;non-directional interactions between the monomers controls the creation/annihilation of defects in the supramolecular polymers, from where monomers exchange proceeds. This competition determines the exchange pathway, dictating whether a fiber statistically swaps monomers from the tips or all along its length. Finally, thanks to their generality, our models allow the investigation of molecular approaches to control the exchange pathways in these dynamic assemblies.&lt;br&gt; Supramolecular chemistry ; Monomer ; Supramolecular polymers ; Dynamic covalent chemistry ; Polymer ; Chemistry ; Nanotechnology ; Chemical physics ; Materials science ; Molecule ; Organic chemistry Supramolecular Self-Assembly in Materials ; Polymer composites and self-healing ; Advanced Polymer Synthesis and Characterization
Authors: David E. Farache | Juan C. Verduzco | Zachary D. McClure | Saaketh Desai | Alejandro Strachan
Source: arXiv (Cornell University) (preprint, 2021-10-15)
Field: Materials Science — Materials Chemistry

Active learning and molecular dynamics simulations to find high melting temperature alloys Active learning (AL) can drastically accelerate materials discovery; its power has been shown in various classes of materials and target properties. Prior efforts have used machine learning models for the optimal selection of physical experiments or physics-based simulations. However, the latter efforts have been mostly limited to the use of electronic structure calculations and properties that can be obtained at the unit cell level and with negligible noise. We couple AL with molecular dynamics simulations to identify multiple principal component alloys (MPCAs) with high melting temperatures. Building on cloud computing services through nanoHUB, we present a fully autonomous workflow for the efficient exploration of the high dimensional compositional space of MPCAs. We characterize how uncertainties arising from the stochastic nature of the simulations and the acquisition functions used to select simulations affect the convergence of the approach. Interestingly, we find that relatively short simulations with significant uncertainties can be used to efficiently find the desired alloys as the random forest models used for AL average out fluctuations. Workflow ; Convergence (economics) ; Computer science ; Molecular dynamics ; Statistical physics ; Random forest ; Space (punctuation) ; Physics ; Artificial intelligence ; Database Machine Learning in Materials Science ; Ion-surface interactions and analysis ; Advanced Materials Characterization Techniques