jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Martin Calkovsky
Source: Proceedings of the European Microscopy Congress 2020 (article, 2021-02-26)
Field: Materials Science — Surfaces, Coatings and Films

Comparison of segmentation algorithms for FIB/SEM tomography of porous polymers: Importance of image contrast for machine learning segmentation Segmentation ; Image segmentation ; Contrast (vision) ; Artificial intelligence ; Porosity ; Computer science ; Polymer ; Computer vision ; Scale-space segmentation ; Materials science ; Tomography ; High contrast ; Pattern recognition (psychology) ; Composite material ; Optics ; Physics Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications ; Advanced X-ray and CT Imaging
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 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. Chemical space ; Computer science ; Generative model ; Generative grammar ; Property (philosophy) ; Workflow ; Artificial intelligence ; Machine learning ; Theoretical computer science ; Chemistry ; Drug discovery Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Steven P. Bennett | Filip Szczypiński | Lukas Turcani | Michael E. Briggs | Rebecca L. Greenaway | Kim E. Jelfs
Source: ChemRxiv (preprint, 2021-03-31)
Field: Materials Science — Materials Chemistry

Materials Precursor Score: Modelling Chemists' Intuition for the Synthetic Accessibility of Porous Organic Cages 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. Computer science ; Workflow ; Bottleneck ; Limiting ; Organic molecules ; Artificial intelligence ; Realisation ; Computational model ; Biochemical engineering ; Porosity ; Machine learning ; Nanotechnology ; Molecule ; Chemistry ; Materials science ; Engineering ; Mechanical engineering ; Organic chemistry ; Physics ; Database Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Bumjoon Seo | Zih‐Yu Lin | Qiyuan Zhao | Michael A. Webb | Brett M. Savoie
Source: ChemRxiv (preprint, 2021-05-04)
Field: Materials Science — Materials Chemistry

Topology Automated Force-Field Interactions (TAFFI): A Framework for Developing Transferable Force-Fields Force-field development has undergone a revolution in the past decade with the proliferation of quantum chemistry based parameterizations and the introduction of machine learning approximations of the atomistic potential energy surface. Nevertheless, transferable force-fields with broad coverage of organic chemical space remain necessary for applications in materials and chemical discovery where throughput, consistency, and computational cost are paramount. Here we introduce a force-field development framework called Topology Automated Force-Field Interactions (TAFFI) for developing transferable force-fields of varying complexity against an extensible database of quantum chemistry calculations. TAFFI formalizes the concept of atom typing and makes it the basis for generating systematic training data that maintains a one-to-one correspondence with force-field terms. This feature makes TAFFI arbitrarily extensible to new chemistries while maintaining internal consistency and transferability. As a demonstration of TAFFI, we have developed a fixed-charge force-field, TAFFI-gen, from scratch that includes coverage for common organic functional groups that is comparable to established transferable force-fields. The performance of TAFFI-gen was benchmarked against OPLS and GAFF for reproducing several experimental proper- ties of 87 organic liquids. The consistent performance of these force-fields, despite their distinct origins, validates the TAFFI framework while also providing evidence of the representability limitations of fixed-charge force-fields. Force field (fiction) ; Transferability ; Computer science ; Field (mathematics) ; Topology (electrical circuits) ; Consistency (knowledge bases) ; Artificial intelligence ; Machine learning ; Mathematics Machine Learning in Materials Science ; Force Microscopy Techniques and Applications ; Various Chemistry Research Topics
Authors: Doyl Dickel | Mashroor S. Nitol | Christopher D. Barrett
Source: arXiv (Cornell University) (preprint, 2021-02-03)
Field: Materials Science — Materials Chemistry

LAMMPS Implementation of Rapid Artificial Neural Network Derived\n Interatomic Potentials While machine learning approaches have been successfully used to represent\ninteratomic potentials, their speed has typically lagged behind conventional\nformalisms. This is often due to the complexity of the structural fingerprints\nused to describe the local atomic environment and the large cutoff radii and\nneighbor lists used in the calculation of these fingerprints. Even recent\nmachine learned methods are at least 10 times slower than traditional\nformalisms. An implementation of a rapid artificial neural network (RANN) style\npotential in the LAMMPS molecular dynamics package is presented here which\nutilizes angular screening to reduce computational complexity without reducing\naccuracy. For the smallest neural network architectures, this formalism rivals\nthe modified embedded atom method (MEAM) for speed and accuracy, while the\nnetworks approximately one third as fast as MEAM were capable of reproducing\nthe training database with chemical accuracy. The numerical accuracy of the\nLAMMPS implementation is assessed by verifying conservation of energy and\nagreement between calculated forces and pressures and the observed derivatives\nof the energy as well as by assessing the stability of the potential in dynamic\nsimulation. The potential style is tested using a force field for magnesium and\nthe computational efficiency for a variety of architectures is compared to a\ntraditional potential models as well as alternative ANN formalisms. The\npredictive accuracy is found to rival that of slower methods.\n Rotation formalisms in three dimensions ; Artificial neural network ; Computer science ; Molecular dynamics ; Artificial intelligence ; Algorithm ; Interatomic potential ; Computational science ; Machine learning ; Computational chemistry ; Chemistry ; Mathematics Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions ; Protein Structure and Dynamics
Authors: Peiyuan Gao | Xiu Yang | Yuhang Tang | Muqing Zheng | Amity Anderson | Vijayakumar Murugesan | Aaron Hollas | Wei Wang
Source: eScholarship (California Digital Library) (preprint, 2021-06-15)
Field: Materials Science — Materials Chemistry

Graphical Gaussian Process Regression Model for Aqueous Solvation Free Energy Prediction of Organic Molecules in Redox Flow Battery The solvation free energy of organic molecules is a critical parameter in determining emergent properties such as solubility, liquid-phase equilibrium constants, and pKa and redox potentials in an organic redox flow battery. In this work, we present a machine learning (ML) model that can learn and predict the aqueous solvation free energy of an organic molecule using Gaussian process regression method based on a new molecular graph kernel. To investigate the performance of the ML model on electrostatic interaction, the nonpolar interaction contribution of solvent and the conformational entropy of solute in solvation free energy, three data sets with implicit or explicit water solvent models, and contribution of conformational entropy of solute are tested. We demonstrate that our ML model can predict the solvation free energy of molecules at chemical accuracy with a mean absolute error of less than 1 kcal/mol for subsets of the QM9 dataset and the Freesolv database. To solve the general data scarcity problem for a graph-based ML model, we propose a dimension reduction algorithm based on the distance between molecular graphs, which can be used to examine the diversity of the molecular data set. It provides a promising way to build a minimum training set to improve prediction for certain test sets where the space of molecular structures is predetermined. Solvation ; Chemistry ; Molecular descriptor ; Molecule ; Implicit solvation ; Gaussian ; Computational chemistry ; Statistical physics ; Thermodynamics ; Chemical physics ; Quantitative structure–activity relationship ; Physics ; Organic chemistry ; Stereochemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Battery Technologies Research
Authors: Pablo A. Unzueta | Chandler Greenwell | Gregory J. O. Beran
Source: Bulletin of the American Physical Society (article, 2021-03-19)
Field: Chemistry — Spectroscopy

Predicting Density Functional Theory-Quality Nuclear Magnetic Resonance Chemical Shifts via Δ-Machine Learning First-principles prediction of nuclear magnetic resonance chemical shifts plays an increasingly important role in the interpretation of experimental spectra, but the required density functional the... Quality (philosophy) ; Density functional theory ; Nuclear magnetic resonance ; Chemical shift ; Computer science ; Physics ; Quantum mechanics Molecular spectroscopy and chirality ; Metabolomics and Mass Spectrometry Studies ; Advanced MRI Techniques and Applications
Authors: Maxime Ducamp | François‐Xavier Coudert
Source: ChemRxiv (preprint, 2021-12-19)
Field: Chemistry — Inorganic Chemistry

Prediction of Thermal Properties of Zeolites through Machine Learning The use of machine learning for the prediction of physical and chemical properties of crystals based on their structure alone is currently an area of intense research in computational materials science. In this work, we studied the possibility of using machine learning-trained algorithms in order to calculate the thermal properties of siliceous zeolite frameworks. We used as training data the thermal properties of 120 zeolites, calculated at the DFT level, in the quasi-harmonic approximation. We compared the statistical accuracy of trained models (based on the gradient boosting regression technique) using different types of descriptors, including ad hoc geometrical features, topology, pore space, and general geometric descriptors. While geometric descriptors were found to perform best, we also identified limitations on the accuracy of the predictions, especially for a small group of materials with very highly negative thermal expansion coefficients. We then studied the generalizability of the technique, demonstrating that the predictions were not sensitive to the refinement of framework structures at a high level of theory. Therefore, the models are suitable for the exploration and screening of large-scale databases of hypothetical frameworks, which we illustrate on the PCOD2 database of zeolites containing around 600,000 hypothetical structures. Generalizability theory ; Machine learning ; Computer science ; Artificial intelligence ; Harmonic mean ; Boosting (machine learning) ; Gradient boosting ; Thermal ; Algorithm ; Mathematics ; Geometry ; Thermodynamics ; Physics ; Statistics Zeolite Catalysis and Synthesis ; Machine Learning in Materials Science ; Catalysis and Oxidation Reactions
Authors: Oliver Mey | Arash Rahimi‐Iman
Source: arXiv (Cornell University) (preprint, 2021-11-11)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine Learning-Based Optimization of Chiral Photonic Nanostructures: Evolution- and Neural Network-Based Design Chiral photonics opens new pathways to manipulate light-matter interactions and tailor the optical response of meta-surfaces and -materials by nanostructuring nontrivial patterns. Chirality of matter, such as that of molecules, and light, which in the simplest case is given by the handedness of circular polarization, have attracted much attention for applications in chemistry, nanophotonics and optical information processing. We report the design of chiral photonic structures using two machine learning methods, the evolutionary algorithm and neural network approach, for rapid and efficient optimization of optical properties for dielectric metasurfaces. The design recipes obtained for visible light in the range of transition-metal dichalcogenide exciton resonances show a frequency-dependent modification in the reflected light's degree of circular polarization, that is represented by the difference between left- and right-circularly polarized intensity. Our results suggest the facile fabrication and characterization of optical nanopatterned reflectors for chirality-sensitive light-matter coupling scenarios employing tungsten disulfide as possible active material with features such as valley Hall effect and optical valley coherence. Nanophotonics ; Photonics ; Chirality (physics) ; Circular polarization ; Nanostructure ; Dielectric ; Optical phenomena ; Materials science ; Polarization (electrochemistry) ; Optoelectronics ; Nanotechnology ; Optics ; Physics ; Chemistry ; Quantum mechanics Metamaterials and Metasurfaces Applications ; Neural Networks and Reservoir Computing ; Molecular Communication and Nanonetworks
Authors: Clémence Corminbœuf | Alberto Fabrizio | Ksenia R. Briling
Source: Research Square (preprint, 2021-11-03)
Field: Materials Science — Materials Chemistry

SPAHM: the Spectrum of Approximated Hamiltonian Matrices representations Abstract Physics-based molecular representations are the cornerstone of all modern machine learning techniques applied to solve chemical problems. While several approaches exist to design ever more accurate fingerprints, the majority resolves in including more physics to construct larger and more complex representations. Here, we present an alternative approach to harness the complexity of chemical information into a lightweight numerical form, naturally invariant under real-space transformations, and seamlessly including the information about the charge state of a molecule. The Spectrum of Approximated Hamiltonian Matrices (SPA H M) leverages the information contained in widely-used and readily-evaluated ``guess'' Hamiltonians to form a robust fingerprint for quantum machine learning. Relying on the origin of the SPA H M fingerprints and a hierarchy of approximate Hamiltonians, we analyze the relative merits of adding physics into molecular representations and find that alternative strategies, focusing more on the machine learning task, represent a clear route towards direct improvements. Hamiltonian (control theory) ; Spectrum (functional analysis) ; Mathematics ; Mathematical physics ; Pure mathematics ; Algebra over a field ; Physics ; Quantum mechanics ; Mathematical optimization Machine Learning in Materials Science ; Neural Networks and Applications ; Advanced Graph Neural Networks
Authors: Steven P. Bennett | Filip Szczypiński | Lukas Turcani | M. S. Briggs | Rebecca L. Greenaway | Kim E. Jelfs
Source:  (preprint, 2021-07-09)
Field: Materials Science — Materials Chemistry

Materials Precursor Score: Modelling Chemists' Intuition for the Synthetic Accessibility of Porous Organic Cage Precursors &lt;div&gt;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. &lt;/div&gt; Computer science ; Bottleneck ; Workflow ; Limiting ; Organic molecules ; Computational model ; Artificial intelligence ; Porosity ; Biochemical engineering ; Realisation ; Nanotechnology ; Machine learning ; Molecule ; Chemistry ; Materials science ; Engineering ; Organic chemistry ; Physics ; Mechanical engineering ; Database Machine Learning in Materials Science ; Supramolecular Chemistry and Complexes ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Rick Mullin﻿
Source: C&EN Global Enterprise (article, 2021-04-26)
Field: Materials Science — Materials Chemistry

Evonik, IBM extend digitalization pact Evonik Industries and IBM have extended until 2025 a partnership in which the specialty chemical company is working with the computing firm on digitalization. Evonik will also begin work with IBM to apply artificial intelligence (AI) in materials discovery at the Massachusetts Institute of Technology, becoming the first chemical company to participate in the MIT-IBM Watson AI Lab. Evonik has worked with IBM since 2017 on developing a deep neural network, a type of AI, to predict the properties of high-performance polymers. IBM ; General partnership ; Watson ; IBM PC compatible ; Management ; Speciality chemicals ; Computer science ; Engineering ; Manufacturing engineering ; Engineering management ; Artificial intelligence ; Business ; Operating system ; Nanotechnology ; Finance ; Economics ; Software Machine Learning in Materials Science
Authors: Vidushi Sharma | Dibakar Datta
Source: ECS Meeting Abstracts (article, 2021-05-30)
Field: Materials Science — Materials Chemistry

Understanding 3D/2D Interfaces for Ion Battery Technologies Composites and heterostructures composed of 3D bulk layered with two dimensional (2D) materials are actively being utilized in energy technologies for enhanced performance, mechanical stability and flexibility. 2D materials such as graphene and transition metal carbides (MXenes) provide a mechanically stable skeleton to high volumetric capacity electrodes such as silicon(Si), selenium(Se) and tin(Sn) to name a few. The physical and chemical attributes of the formed 3D/2D interfaces directly propels the electrochemical performance of the composite electrode. We utilize Density functional theory (DFT) to quantify the strength of 3D/2D interfaces formed and further corelated the interface strength to the crystal structure, surface chemistry and electrochemistry of the electrodes. We investigate the interface strength variations between amorphous Si and Ti 3 C 2 T x MXene as the MXene surface functional groups (T x ) are varied from -OH to -OH/O mixed, and -F. The Si/MXene interface is further compared with Si/Graphene interface for Lithium ion battery applications. In additional, ability of Deep Learning(DP) potential to accurately describe structural and dynamic properties of such interfaces has been explored. A DP based potential is generated from vast 3D/2D interface dataset from quantum mechanical calculations and its accuracy has been compared with DFT output of interface dynamics. Issues related to the development of DP for such systems will also be discussed. MXenes ; Materials science ; Density functional theory ; Graphene ; Heterojunction ; Battery (electricity) ; Electrode ; Lithium-ion battery ; Electrochemistry ; Nanotechnology ; Interface (matter) ; Transition metal ; Optoelectronics ; Composite material ; Computational chemistry ; Physical chemistry ; Chemistry ; Thermodynamics MXene and MAX Phase Materials ; Graphene research and applications ; Advancements in Battery Materials