jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Felix Mayr | Alessio Gagliardi
Source: ACS Omega (preprint, 2021-05-03)
Field: Materials Science — Materials Chemistry

Global Property Prediction: A Benchmark Study on Open-Source, Perovskite-like Datasets Screening combinatorial space for novel materials, such as perovskite-like ones for photovoltaics, has resulted in a high amount of simulated high-throughput data and analysis thereof. This study proposes a comprehensive comparison of structural fingerprint-based machine learning models on seven open-source databases of perovskite-like materials to predict band gaps and energies. It shows that none of the given methods, including graph neural networks, are able to capture arbitrary databases evenly, while underlining that commonly used metrics are highly database-dependent in typical workflows. In addition, the applicability of variance selection and autoencoders to significantly reduce fingerprint size indicates that models built with common fingerprints only rely on a submanifold of the available fingerprint space. Benchmark (surveying) ; Fingerprint (computing) ; Computer science ; Open source ; Workflow ; Variance (accounting) ; Data mining ; Property (philosophy) ; Perovskite (structure) ; Photovoltaics ; Space (punctuation) ; Artificial intelligence ; Machine learning ; Database ; Engineering ; Cartography ; Photovoltaic system ; Geography ; Software Machine Learning in Materials Science ; Advanced Photocatalysis Techniques ; Expert finding and Q&A systems
Authors: Alan M. Lewis | Andrea Grisafi | Michele Ceriotti | Mariana Rossi
Source: Journal of Chemical Theory and Computation (preprint, 2021-10-20)
Field: Materials Science — Materials Chemistry

Learning Electron Densities in the Condensed Phase We introduce a local machine-learning method for predicting the electron densities of periodic systems. The framework is based on a numerical, atom-centered auxiliary basis, which enables an accurate expansion of the all-electron density in a form suitable for learning isolated and periodic systems alike. We show that, using this formulation, the electron densities of metals, semiconductors, and molecular crystals can all be accurately predicted using symmetry-adapted Gaussian process regression models, properly adjusted for the nonorthogonal nature of the basis. These predicted densities enable the efficient calculation of electronic properties, which present errors on the order of tens of meV/atom when compared to <i>ab initio</i> density-functional calculations. We demonstrate the key power of this approach by using a model trained on ice unit cells containing only 4 water molecules to predict the electron densities of cells containing up to 512 molecules and see no increase in the magnitude of the errors of derived electronic properties when increasing the system size. Indeed, we find that these extrapolated derived energies are more accurate than those predicted using a direct machine-learning model. Finally, on heterogeneous data sets SALTED can predict electron densities with errors below 4%. Basis (linear algebra) ; Electron density ; Atom (system on chip) ; Electron ; Statistical physics ; Ab initio ; Phase (matter) ; Symmetry (geometry) ; Physics ; Molecular physics ; Atomic physics ; Computational physics ; Computer science ; Quantum mechanics ; Mathematics Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; Advanced Chemical Physics Studies
Authors: Dakota Folmsbee | David Ryan Koes | Geoffrey Hutchison
Source: ChemRxiv (preprint, 2021-02-10)
Field: Materials Science — Materials Chemistry

Evaluation of Thermochemical Machine Learning for Potential Energy Curves and Geometry Optimization While many machine learning methods, particularly deep neural networks have been trained for density functional and quantum chemical energies and properties, the vast majority of these methods focus on single-point energies. In principle, such ML methods, once trained, offer thermochemical accuracy on par with density functional and wave function methods but at speeds comparable to traditional force fields or approximate semiempirical methods. So far, most efforts have focused on optimized equilibrium single-point energies and properties. In this work, we evaluate the accuracy of several leading ML methods across a range of bond potential energy curves and torsional potentials. Methods were trained on the existing ANI-1 training set, calculated using the ωB97X / 6-31G(d) single points at non-equilibrium geometries. We find that across a range of small molecules, several methods offer both qualitative accuracy (e.g., correct minima, both repulsive and attractive bond regions, anharmonic shape, and single minima) and quantitative accuracy in terms of the mean absolute percent error near the minima. At the moment, ANI-2x, FCHL, and our new grid-based convolutional neural net show good performance. Maxima and minima ; Anharmonicity ; Artificial neural network ; Potential energy ; Convolutional neural network ; Range (aeronautics) ; Grid ; Hyperparameter optimization ; Work (physics) ; Quantum ; Computer science ; Density functional theory ; Energy minimization ; Statistical physics ; Algorithm ; Artificial intelligence ; Physics ; Mathematics ; Geometry ; Materials science ; Quantum mechanics ; Mathematical analysis Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Koushik Pal | Cheol Woo Park | Yi Xia | Jiahong Shen | Chris Wolverton
Source: arXiv (Cornell University) (preprint, 2021-09-08)
Field: Materials Science — Materials Chemistry

Scale-invariant Machine-learning Model Accelerates the Discovery of Quaternary Chalcogenides with Ultralow Lattice Thermal Conductivity Intrinsically low lattice thermal conductivity ($κ_l$) is a desired requirement in many crystalline solids such as thermal barrier coatings and thermoelectrics. Here, we design an advanced machine-learning (ML) model based on crystal graph convolutional neural network that is insensitive to volumes (i.e., scale) of the input crystal structures to discover novel quaternary chalcogenides, AMM'Q$_3$ (A/M/M'=alkali, alkaline-earth, post-transition metals, lanthanides, Q=chalcogens). Upon screening the thermodynamic stability of $\sim$ 1 million compounds using the ML model iteratively and performing density functional theory (DFT) calculations for a small fraction of compounds, we discover 99 compounds that are validated to be stable in DFT. Taking several DFT-stable compounds, we calculate their $κ_l$ using phonon-Boltzmann transport equation, which reveals ultralow-$κ_l$ ($ Phonon ; Materials science ; Density functional theory ; Anharmonicity ; Thermal conductivity ; Boltzmann equation ; Thermodynamics ; Thermoelectric materials ; Machine learning ; Condensed matter physics ; Chemistry ; Physics ; Computational chemistry ; Computer science Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; Thermal properties of materials
Authors: Mingjian Wen | Yaser Afshar | Ryan S. Elliott | Ellad B. Tadmor
Source: arXiv (Cornell University) (preprint, 2021-08-07)
Field: Materials Science — Materials Chemistry

KLIFF: A framework to develop analytic and machine learning interatomic potentials Interatomic potentials (IPs) are reduced-order models for calculating the potential energy of a system of atoms given their positions in space and species. IPs treat atoms as classical particles without explicitly modeling electrons and thus are computationally far less expensive than first-principles methods, enabling molecular simulations of significantly larger systems over longer times. Developing an IP is a complex iterative process involving multiple steps: assembling a training set, designing a functional form, optimizing the function parameters, testing model quality, and deployment to molecular simulation packages. This paper introduces the KIM-based learning-integrated fitting framework (KLIFF), a package that facilitates the entire IP development process. KLIFF supports both analytic and machine learning IPs. It adopts a modular approach whereby various components in the fitting process, such as atomic environment descriptors, functional forms, loss functions, optimizers, quality analyzers, and so on, work seamlessly with each other. This provides a flexible framework for the rapid design of new IP forms. Trained IPs are compatible with the Knowledgebase of Interatomic Models (KIM) application programming interface (API) and can be readily used in major materials simulation packages compatible with KIM, including ASE, DL_POLY, GULP, LAMMPS, and QC. KLIFF is written in Python with computationally intensive components implemented in C++. It is parallelized over data and supports both shared-memory multicore desktop machines and high-performance distributed memory computing clusters. We demonstrate the use of KLIFF by fitting an analytic Stillinger--Weber potential and a machine learning neural network potential for silicon. The KLIFF package, together with its documentation, is publicly available at: this https URL. Computer science ; Python (programming language) ; Interatomic potential ; Artificial neural network ; Computational science ; Modular design ; Artificial intelligence ; Machine learning ; Molecular dynamics ; Programming language ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Nicolae C. Iovanac | Robert MacKnight | Brett M. Savoie
Source: ChemRxiv (preprint, 2021-05-24)
Field: Materials Science — Materials Chemistry

Actively Searching: Inverse Design of Novel Molecules with Simultaneously Optimized Properties &lt;p&gt;Combining quantum chemistry characterizations with generative machine learning models has the potential to accelerate molecular searches in chemical space. In this paradigm, quantum chemistry acts as a relatively cost-effective oracle for evaluating the properties of particular molecules while generative models provide a means of sampling chemical space based on learned structure-function relationships. For practical applications, multiple potentially orthogonal properties must be optimized in tandem during a discovery workflow. This carries additional difficulties associated with specificity of the targets and the ability for the model to reconcile all properties simultaneously. Here we demonstrate an active learning approach to improve the performance of multi-target generative chemical models. We first demonstrate the effectiveness of a set of baseline models trained on single property prediction tasks in generating novel compounds with various property targets, including both interpolative and extrapolative generation scenarios. For property ranges where accurate targeting proves difficult, the novel compounds suggested by the model are characterized using quantum chemistry to obtain the true values, and these new molecules closest to expressing the desired properties are fed back into the generative model for additional training. This gradually improves the generative models’ understanding of unknown areas of chemical space and shifts the distribution of generated compounds towards the targeted values. We then demonstrate the effectiveness of this active learning approach in generating compounds with multiple chemical constraints, including vertical ionization potential, electron affinity, and dipole moment targets, and validate the results at the B97X-D3/def2-TZVP level. This method requires no modifications to extant generative approaches, but rather utilizes their inherent generative and predictive aspects for self-refinement, and can be applied to situations where any number of properties with varying degrees of correlation must be optimized simultaneously.&lt;/p&gt; Chemical space ; Computer science ; Generative model ; Property (philosophy) ; Generative grammar ; Workflow ; Space (punctuation) ; Quantum chemical ; Artificial intelligence ; Machine learning ; Chemistry ; Biological system ; Molecule ; Drug discovery Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Sam Lemonick﻿
Source: C&EN Global Enterprise (article, 2021-04-26)
Field: Materials Science — Materials Chemistry

Machine learning maps atoms Machine-learning software can now accurately track atoms from reactants to products without human help. Earlier this month at the American Chemical Society Spring 2021 meeting, Philippe Schwaller of IBM Research and the University of Bern described how his team took machine-learning algorithms designed for interpreting language and repurposed them for atom mapping. He presented their findings at the meeting and in a recent paper ( Sci. Adv. 2021, DOI: 10.1126/sciadv.abe4166 ). Machine-learning models called transformers power programs like Google Translate, and Schwaller and his colleagues have recently demonstrated their ability to accurately interpret and predict chemical transformations. In an effort to understand what steps these algorithms use to forecast the outcome of reactions, Schwaller collaborated with Benjamin Hoover, also of IBM Research, to visualize connections their software was finding between atoms in reactant and product molecules. They realized that the models were working out the process of atom mapping, forming IBM ; Computer science ; Artificial intelligence ; Software ; Machine learning ; Process (computing) ; IBM PC compatible ; Atom (system on chip) ; Programming language ; Nanotechnology ; Operating system ; Materials science Machine Learning in Materials Science ; Various Chemistry Research Topics
Authors: Nicholas T. Dee | Martin Schneider | Dmitri N. Zakharov | Piran R. Kidambi | A. John Hart
Source: SSRN Electronic Journal (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Automated Processing of Environmental Transmission Electron Microscopy Images for Quantification of Thin Film Dewetting and Carbon Nanotube Nucleation Dynamics Dewetting ; Nucleation ; Materials science ; Carbon nanotube ; Nanotechnology ; Transmission electron microscopy ; Carbon fibers ; Chemical engineering ; Thin film ; Composite material ; Chemistry ; Organic chemistry ; Composite number Thermal properties of materials ; Nanopore and Nanochannel Transport Studies ; Heat Transfer and Optimization
Authors: Steven P. Bennett | Filip Szczypiński | Lukas Turcani | Michael E. Briggs | Rebecca L. Greenaway | Kim E. Jelfs
Source: ChemRxiv (preprint, 2021-07-08)
Field: Materials Science — Materials Chemistry

Materials Precursor Score: Modelling Chemists' Intuition for the Synthetic Accessibility of Porous Organic Cage Precursors <div>Computation is increasingly being used to try to accelerate the discovery of new materials. One specific example of this is porous molecular materials, specifically porous organic cages, where the porosity of the materials predominantly comes from the internal cavities of the molecules themselves. The computational discovery of novel structures with useful properties is currently hindered by the difficulty in transitioning from a computational prediction to synthetic realisation. Attempts at experimental validation are often time-consuming, expensive and, frequently, the key bottleneck of material discovery. In this work, we developed a computational screening workflow for porous molecules that includes consideration of the synthetic difficulty of material precursors, aimed at easing the transition between computational prediction and experimental realisation. We trained a machine learning model by first collecting data on 12,553 molecules categorised either as `easy-to-synthesise' or `difficult-to-synthesise' by expert chemists with years of experience in organic synthesis. We used an approach to address the class imbalance present in our dataset, producing a binary classifier able to categorise easy-to-synthesise molecules with few false positives. We then used our model during computational screening for porous organic molecules to bias towards precursors whose easier synthesis requirements would make them promising candidates for experimental realisation and material development. We found that even by limiting precursors to those that are easier-to-synthesise, we are still able to identify cages with favourable, and even some rare, properties. </div> Computer science ; Workflow ; Bottleneck ; Limiting ; Organic molecules ; Artificial intelligence ; Realisation ; Computational model ; Biochemical engineering ; Machine learning ; Nanotechnology ; Molecule ; Chemistry ; Materials science ; Engineering ; Mechanical engineering ; Organic chemistry ; Physics ; Database Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; Supramolecular Chemistry and Complexes
Authors: Volker L. Deringer | Albert P. Bartók | Noam Bernstein | David M. Wilkins | Michele Ceriotti | Gábor Cśanyi
Source: Research Portal (Queen's University Belfast) (article, 2021-07-13)
Field: Materials Science — Materials Chemistry

Gaussian Process Regression for Materials and Molecules. We provide an introduction to Gaussian process regression (GPR) machine-learning methods in computational materials science and chemistry. The focus of the present review is on the regression of atomistic properties: in particular, on the construction of interatomic potentials, or force fields, in the Gaussian Approximation Potential (GAP) framework; beyond this, we also discuss the fitting of arbitrary scalar, vectorial, and tensorial quantities. Methodological aspects of reference data generation, representation, and regression, as well as the question of how a data-driven model may be validated, are reviewed and critically discussed. A survey of applications to a variety of research questions in chemistry and materials science illustrates the rapid growth in the field. A vision is outlined for the development of the methodology in the years to come. Kriging ; Gaussian process ; Statistical physics ; Representation (politics) ; Regression ; Computer science ; Gaussian ; Field (mathematics) ; Scalar (mathematics) ; Regression analysis ; Machine learning ; Artificial intelligence ; Mathematics ; Physics ; Chemistry ; Computational chemistry ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Jingbai Li | Rachel Stein | Daniel Adrion | Steven A. Lopez
Source:  (preprint, 2021-07-29)
Field: Materials Science — Materials Chemistry

Machine-learning photodynamics simulations uncover the role of substituent effects on the photochemical formation of cubanes Photochemical [2+2]-photocycloadditions are used to efficiently access strained organic molecular architectures, storing solar energy in chemical bonds. Functionalized [3]-ladderenes have been shown to undergo [2+2]-photocycloadditions to afford cubanes, an energy-dense class of organic molecules. The substituents (e.g., methyl, trifluoromethyl, and cyclopropyl) affect the overall reactivities of these cubane precursors leading to a yield from 1% to 48%. We now integrate single and multireference calculations and our machine-learning-accelerated non-adiabatic molecular dynamics (ML-NAMD) to understand how substituents affect the mechanistic photodynamics of [2+2]-photocycloadditions. Our calculations show that steric clashes destabilize the 4π-electrocyclic ring-opening pathway and minimum energy conical intersections by 0.72–1.15 eV and reaction energies by 0.68–2.34 eV. In contrast, favorable dispersive interactions stabilize the [2+2]-photocycloaddition pathway, lower the conical intersection energies by 0.31–0.85 eV and cubane reaction energies by 0.03–0.82 eV. The 2 ps ML-NAMD trajectories reveal that closed-shell repulsions block a 6π-conrotatory electrocyclic ring-opening pathway with increasing steric bulk. 57% of the methyl-substituted [3]-ladderene trajectories proceed through the 6π-conrotatory electrocyclic ring-opening, whereas the trifluoromethyl- and cyclopropyl-substituted 3-ladderenes chemoselectively proceed through [2+2]-photocycloaddition pathways. The predicted cubane yields (H: 0.4% &lt; CH3: 1% &lt; CF3: 14% &lt; cPr: 20%) match the experimental trend; these substituents pre-distort the reactants to resemble the conical intersection leading to cubane. Conrotatory and disrotatory ; Chemistry ; Conical intersection ; Steric effects ; Cubane ; Ring (chemistry) ; Trifluoromethyl ; Photochemistry ; Substituent ; Computational chemistry ; Stereochemistry ; Molecule ; Organic chemistry Photochromic and Fluorescence Chemistry ; Radical Photochemical Reactions ; Machine Learning in Materials Science
Authors: Varvara Gribova | Анастасія Наваліхіна | Oleksandr Lysenko | Cynthia Calligaro | Eloïse Lebaudy | Lucie Deiber | Bernard Senger | Philippe Lavall�e | Nihal Engin Vrana
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2021-09-21)
Field: Materials Science — Polymers and Plastics

Prediction of coating thickness for polyelectrolyte multilayers via machine learning Layer-by-layer (LbL) deposition method of polyelectrolytes is a versatile way of developing functional nanoscale coatings. Even though the mechanisms of LbL flm development are well-established, currently there are no predictive models that can link flm components with their fnal properties. The current health crisis has shown the importance of accelerated development of biomedical solutions such as antiviral coatings, and the implementation of machine learning methodologies for coating development can enable achieving this. In this work, using literature data and newly generated experimental results, we frst analyzed the relative impact of 23 coating parameters on the coating thickness. Next, a predictive model has been developed using aforementioned parameters and molecular descriptors of polymers from the DeepChem library. Model performance was limited because of insufcient number of data points in the training set, due to the scarce availability of data in the literature. Despite this limitation, we demonstrate, for the frst time, utilization of machine learning for prediction of LbL coating properties. It can decrease the time necessary to obtain functional coating with desired properties, as well as decrease experimental costs and enable the fast frst response to crisis situations (such as pandemics) where coatings can positively contribute. Besides coating thickness, which was selected as an output value in this study, machine learning approach can be potentially used to predict functional properties of multilayer coatings, e.g. biocompatibility, cell adhesive, antibacterial, antiviral or anti-infammatory properties. Polyelectrolyte ; Coating ; Materials science ; Computer science ; Composite material ; Polymer Dendrimers and Hyperbranched Polymers