jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Lehan Yao | Hyosung An | Shan Zhou | Ahyoung Kim | Erik Luijten | Qian Chen
Source: Nanoscale (article, 2022-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Seeking regularity from irregularity: unveiling the synthesis–nanomorphology relationships of heterogeneous nanomaterials using unsupervised machine learning Nanoscale morphology of functional materials determines their chemical and physical properties. However, despite increasing use of transmission electron microscopy (TEM) to directly image nanomorphology, it remains challenging to quantify the information embedded in TEM data sets, and to use nanomorphology to link synthesis and processing conditions to properties. We develop an automated, descriptor-free analysis workflow for TEM data that utilizes convolutional neural networks and unsupervised learning to quantify and classify nanomorphology, and thereby reveal synthesis-nanomorphology relationships in three different systems. While TEM records nanomorphology readily in two-dimensional (2D) images or three-dimensional (3D) tomograms, we advance the analysis of these images by identifying and applying a universal shape fingerprint function to characterize nanomorphology. After dimensionality reduction through principal component analysis, this function then serves as the input for morphology grouping through unsupervised learning. We demonstrate the wide applicability of our workflow to both 2D and 3D TEM data sets, and to both inorganic and organic nanomaterials, including tetrahedral gold nanoparticles mixed with irregularly shaped impurities, hybrid polymer-patched gold nanoprisms, and polyamide membranes with irregular and heterogeneous 3D crumple structures. In each of these systems, unsupervised nanomorphology grouping identifies both the diversity and the similarity of the nanomaterial across different synthesis conditions, revealing how synthetic parameters guide nanomorphology development. Our work opens possibilities for enhancing synthesis of nanomaterials through artificial intelligence and for understanding and controlling complex nanomorphology, both for 2D systems and in the far less explored case of 3D structures, such as those with embedded voids or hidden interfaces. Nanomaterials ; Materials science ; Nanotechnology Gold and Silver Nanoparticles Synthesis and Applications ; Pickering emulsions and particle stabilization ; Enhanced Oil Recovery Techniques
Authors: Koki Ikemoto | Misato Akiyoshi | Tatsuru Mio | Kaito Nishioka | Sota Sato | Hiroyuki Isobe
Source: Angewandte Chemie International Edition (article, 2022-05-23)
Field: Materials Science — Materials Chemistry

Synthesis of a Negatively Curved Nanocarbon Molecule with an Octagonal Omphalos via Design‐of‐Experiments Optimizations Supplemented by Machine Learning A saddle-shaped nanocarbon molecule was synthesized, which revealed the existence of negative Gauss curvatures on a >3-nm molecular structure possessing 192 π-electrons. The synthesis was facilitated by a protocol developed with Design-of-Experiments optimizations and machine-learning predictions, and spectroscopy and crystallography were used to reveal the saddle-shaped structure of the molecule. Solution-phase analyses showed the presence of dimeric assembly, and crystallographic analyses revealed the stacked dimeric structures. The stacked crystal structure was scrutinized by various methods, including Gauss curvatures derived from the discrete surface theory of geometry, to reveal the important role of the molecular Gauss curvature in dimeric assembly. Saddle ; Molecule ; Curvature ; Gauss ; Crystal structure ; Crystallography ; Molecular machine ; Materials science ; Chemistry ; Nanotechnology ; Geometry ; Physics ; Mathematics ; Engineering ; Quantum mechanics ; Mechanical engineering Machine Learning in Materials Science ; Crystallography and molecular interactions ; Fullerene Chemistry and Applications
Authors: Heejung W. Chung | Rodrigo Freitas | Gowoon Cheon | Evan J. Reed
Source: Physical Review Materials (article, 2022-04-05)
Field: Materials Science — Materials Chemistry

Data-centric framework for crystal structure identification in atomistic simulations using machine learning The spatial complexity of cross-scale atomistic simulations renders them unsuitable for simple human visual inspection. Instead, specialized structure characterization techniques are required to aid interpretation. These have historically been challenging to construct, requiring significant intuition and effort. In this article the authors introduce a data-centric framework that favors the employment of machine learning over heuristic rules of classification. It is demonstrated that the data-centric framework outperforms all of the most popular heuristic methods while introducing a systematic route for generalization to new crystal structures. Intuition ; Heuristic ; Machine learning ; Generalization ; Artificial intelligence ; Construct (python library) ; Computer science ; Simple (philosophy) ; Materials science ; Mathematics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Enzyme Structure and Function
Authors: Edirisuriya M. Dilanga Siriwardane | Yong Zhao | Indika Perera | Jianjun Hu
Source: npj Computational Materials (article, 2022-08-03)
Field: Materials Science — Materials Chemistry

Generative design of stable semiconductor materials using deep learning and density functional theory Abstract Semiconductor device technology has greatly developed in complexity since discovering the bipolar transistor. In this work, we developed a computational pipeline to discover stable semiconductors by combining generative adversarial networks (GAN), classifiers, and high-throughput first-principles calculations. We used CubicGAN, a GAN-based algorithm for generating cubic materials and developed a classifier to screen the semiconductors and studied their stability using first principles. We found 12 stable AA $${}^{\prime}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow/> <mml:mrow> <mml:mo>′</mml:mo> </mml:mrow> </mml:msup> </mml:math> MH 6 semiconductors in the F-43m space group including BaNaRhH 6 , BaSrZnH 6 , BaCsAlH 6 , SrTlIrH 6 , KNaNiH 6 , NaYRuH 6 , CsKSiH 6 , CaScMnH 6 , YZnMnH 6 , NaZrMnH 6 , AgZrMnH 6 , and ScZnMnH 6 . Previous research reported that five AA $${}^{\prime}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow/> <mml:mrow> <mml:mo>′</mml:mo> </mml:mrow> </mml:msup> </mml:math> IrH6 semiconductors with the same space group were synthesized. Our research shows that AA $${}^{\prime}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow/> <mml:mrow> <mml:mo>′</mml:mo> </mml:mrow> </mml:msup> </mml:math> MnH 6 and NaYRuH 6 semiconductors have considerably different properties compared to the rest of the AA $${}^{\prime}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow/> <mml:mrow> <mml:mo>′</mml:mo> </mml:mrow> </mml:msup> </mml:math> MH 6 semiconductors. Based on the accurate hybrid functional calculations, AA $${}^{\prime}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow/> <mml:mrow> <mml:mo>′</mml:mo> </mml:mrow> </mml:msup> </mml:math> MH 6 semiconductors are found to be wide-bandgap semiconductors. Moreover, BaSrZnH 6 and KNaNiH 6 are direct-bandgap semiconductors, whereas others exhibit indirect bandgaps. Algorithm ; Machine learning ; Artificial intelligence ; Computer science Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Thermoelectric Materials and Devices
Authors: Xiujuan Liu | Yueyue Shao | Tian Lu | Dongping Chang | Minjie Li | Wencong Lu
Source: Materials & Design (article, 2022-03-18)
Field: Materials Science — Materials Chemistry

Accelerating the discovery of high-performance donor/acceptor pairs in photovoltaic materials via machine learning and density functional theory The gigantically unexplored chemical space for the combinations of donor and acceptor poses a mammoth challenge in enhancing power conversion efficiency. Herein, a strategy integrating machine learning and density functional theory was proposed to assist the rational screening of prominent donor/acceptor pairs. Notably, 10 promising donor/acceptor pairs were ultimately sieved out with power conversion efficiency lager than 18.22%. Furthermore, the density functional theory computation expressed that the new donor/acceptor pair of D18/BTP-eC9 possessed stronger absorption intensity, while the KCS/KCR (ratio of charge separation rate and charge recombination rate) and μe (electron mobility) were larger than those of the best known D18/Y6 with the increments of 201.31% and 125.98% respectively. The SHapley Additive exPlanations analysis revealed that the two most important features of A_nR08 and D_D/Dtr12 were positively related to the improvement of power conversion efficiency. It was found that A_nR08 was positively associated with π-conjugation strength, and high D_D/Dtr12 suggested the highly fused chemical structure, indicating the practical clues in discovering top-performing donor/acceptor pairs. Density functional theory ; Acceptor ; Materials science ; Energy conversion efficiency ; Charge (physics) ; Computation ; Photovoltaic system ; Electron donor ; Optoelectronics ; Chemical physics ; Computational chemistry ; Catalysis ; Computer science ; Chemistry ; Algorithm ; Organic chemistry ; Physics ; Quantum mechanics ; Biology Machine Learning in Materials Science ; Quantum Dots Synthesis And Properties ; Chalcogenide Semiconductor Thin Films
Authors: Yuta Miyake | Kakaraparthi Kranthiraja | Fumitaka Ishiwari | Akinori Saeki
Source: Chemistry of Materials (article, 2022-07-25)
Field: Materials Science — Materials Chemistry

Improved Predictions of Organic Photovoltaic Performance through Machine Learning Models Empowered by Artificially Generated Failure Data The conventional development of organic photovoltaic (OPV) materials has been driven by the inspiration and continuous endeavors of experimentalists, whereas machine learning (ML) approaches enable extremely high-throughput data processing. However, the predictive accuracy of ML currently remains insufficient for the design of OPV semiconductors that exhibit a complex connectivity between chemical structure and power conversion efficiency (PCE). In this study, we examined the impact of data selection and the introduction of artificially generated failure data on ML predictions of polymer/non-fullerene, small-molecular-acceptor (NFA) solar cells. We demonstrated that an ML model empowered by artificially generated failure data (∼0% PCE by insoluble polymers based on an inappropriate choice of solubilizing side alkyl chains) led to improved predictions. This approach was validated through the synthesis and characterization of twelve polymers (benzothiadiazole, thienothiophene, or tetrazine coupled with benzodithiophene; benzobisthiazole coupled with dioxo-benzodithiophene). Our work offers a facile approach to mitigate the difficulties of the ML-driven development of OPV materials that is also readily applicable to other material science fields. Photovoltaic system ; Polymer ; Energy conversion efficiency ; Characterization (materials science) ; Materials science ; Computer science ; Fullerene ; Organic solar cell ; Acceptor ; Throughput ; Nanotechnology ; Organic semiconductor ; Biochemical engineering ; Chemistry ; Optoelectronics ; Organic chemistry ; Electrical engineering ; Engineering ; Physics Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Advanced Memory and Neural Computing
Authors: Nency P. Domingues | Seyed Mohamad Moosavi | Leopold Talirz | Kevin Maik Jablonka | Christopher P. Ireland | Fatmah Mish Ebrahim | Berend Smit
Source: Communications Chemistry (article, 2022-12-10)
Field: Chemistry — Inorganic Chemistry

Using genetic algorithms to systematically improve the synthesis conditions of Al-PMOF The synthesis of metal-organic frameworks (MOFs) is often complex and the desired structure is not always obtained. In this work, we report a methodology that uses a joint machine learning and experimental approach to optimize the synthesis conditions of Al-PMOF (Al<sub>2</sub>(OH)<sub>2</sub>TCPP) [H<sub>2</sub>TCPP = meso-tetra(4-carboxyphenyl)porphine], a promising material for carbon capture applications. Al-PMOF was previously synthesized using a hydrothermal reaction, which gave a low throughput yield due to its relatively long reaction time (16 hours). Here, we use a genetic algorithm to carry out a systematic search for the optimal synthesis conditions and a microwave-based high-throughput robotic platform for the syntheses. We show that, in just two generations, we could obtain excellent crystallinity and yield close to 80% in a much shorter reaction time (50 minutes). Moreover, by analyzing the failed and partially successful experiments, we could identify the most important experimental variables that determine the crystallinity and yield. Crystallinity ; Yield (engineering) ; Throughput ; Genetic algorithm ; Hydrothermal synthesis ; Computer science ; Algorithm ; Hydrothermal circulation ; Microwave ; Reaction conditions ; Materials science ; Work (physics) ; Biological system ; Chemical engineering ; Chemistry ; Machine learning ; Engineering ; Organic chemistry ; Mechanical engineering ; Catalysis ; Composite material ; Biology Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Gas Sensing Nanomaterials and Sensors
Authors: Stephanie Felten | Cyndi Qixin He | Mark Weisel | Michael Shevlin | Marion H. Emmert
Source: Journal of the American Chemical Society (article, 2022-12-09)
Field: Chemistry — Organic Chemistry

Accessing Diverse Azole Carboxylic Acid Building Blocks via Mild C–H Carboxylation: Parallel, One-Pot Amide Couplings and Machine-Learning-Guided Substrate Scope Design This manuscript describes a mild, functional group tolerant, and metal-free C-H carboxylation that enables direct access to azole-2-carboxylic acids, followed by amide coupling in one pot. This demonstrates a significant expansion of the accessible chemical space of azole-2-amides, compared to previously known methodologies. Key to the described reactivity is the use of silyl triflate reagents, which serve as reaction mediators in C-H deprotonation and stabilizers of (otherwise unstable) azole carboxylic acid intermediates. A diverse azole substrate scope designed via machine-learning-guided analysis demonstrates the broad utility of the sequence. Density functional theory calculations provide detailed insights into the role of silyl triflates in the reaction mechanism. Transferrable applications of the protocol are successfully established: (i) A low pressure (CO<sub>2</sub> balloon) option for synthesizing azole-2-carboxylic acids without the need for high-pressure equipment; (ii) the use of <sup>13</sup>CO<sub>2</sub> for the synthesis of labeled compounds; (iii) isocyanates as alternative electrophiles for direct C-H amidation; (iv) and the use of the developed chemistry in a 24 × 12 parallel synthesis workflow with a 90% library success rate. Fundamentally, the reported protocol expands the use of heterocycle C-H functionalization from late-stage functionalization applications toward its use in library synthesis. It provides general access to densely functionalized azole-2-carboxylic acid building blocks and demonstrates their one-pot diversification. Chemistry ; Carboxylation ; Silylation ; Azole ; Amide ; Carboxylic acid ; Electrophile ; Combinatorial chemistry ; Deprotonation ; Substrate (aquarium) ; Reagent ; Organic chemistry ; Catalysis ; Antifungal Catalytic C–H Functionalization Methods ; Asymmetric Hydrogenation and Catalysis ; Carbon dioxide utilization in catalysis
Authors: Zhao Wen | Hu Qiu | Wanlin Guo
Source: The Journal of Physical Chemistry C (article, 2022-06-16)
Field: Materials Science — Materials Chemistry

A Deep Neural Network Potential for Water Confined in Graphene Nanocapillaries Water under nanoconfinement exhibits structural and dynamical properties remarkably different from those of bulk water, and empirical potential-based molecular dynamics has played an important role in furthering our understanding of the behavior of confined water. However, existing potentials for water were commonly parametrized based on the properties of the bulk water and may be unreliable for describing nanoconfined water. Here, we develop a machine learning potential for water confined in graphene nanocapillaries using deep neural networks trained on quantum mechanical density-functional theory (DFT) calculations. This deep neural network potential offers near-DFT accuracy in terms of potential energy and atomic forces but at a computational cost much lower than DFT-based ab initio molecular dynamics methods. In particular, this potential reproduces well the DFT reference for a wide range of properties, including O–H bond length distribution, density distribution, radial distribution functions, hydrogen bonding, etc. The developed deep neural network potential opens the door to simulations of nanoconfined water with large system sizes and time scales at near-DFT accuracy. Density functional theory ; Graphene ; Molecular dynamics ; Potential energy ; Artificial neural network ; Ab initio ; Chemical physics ; Statistical physics ; Materials science ; Biological system ; Nanotechnology ; Computational chemistry ; Computer science ; Chemistry ; Physics ; Artificial intelligence ; Quantum mechanics Machine Learning in Materials Science ; Nanopore and Nanochannel Transport Studies ; Spectroscopy and Quantum Chemical Studies
Authors: Maarten Cools‐Ceuppens | Joni Dambre | Toon Verstraelen
Source: Journal of Chemical Theory and Computation (article, 2022-02-16)
Field: Materials Science — Materials Chemistry

Modeling Electronic Response Properties with an Explicit-Electron Machine Learning Potential Explicit-electron force fields introduce electrons or electron pairs as semiclassical particles in force fields or empirical potentials, which are suitable for molecular dynamics simulations. Even though semiclassical electrons are a drastic simplification compared to a quantum-mechanical electronic wave function, they still retain a relatively detailed electronic model compared to conventional polarizable and reactive force fields. The ability of explicit-electron models to describe chemical reactions and electronic response properties has already been demonstrated, yet the description of short-range interactions for a broad range of chemical systems remains challenging. In this work, we present the electron machine learning potential (eMLP), a new explicit electron force field in which the short-range interactions are modeled with machine learning. The electron pair particles will be located at well-defined positions, derived from localized molecular orbitals or Wannier centers, naturally imposing the correct dielectric and piezoelectric behavior of the system. The eMLP is benchmarked on two newly constructed data sets: eQM7, an extension of the QM7 data set for small molecules, and a data set for the crystalline β-glycine. It is shown that the eMLP can predict dipole moments, polarizabilities, and IR-spectra of unseen molecules with high precision. Furthermore, a variety of response properties, for example, stiffness or piezoelectric constants, can be accurately reproduced. Polarizability ; Semiclassical physics ; Electron ; Wannier function ; Dipole ; Force field (fiction) ; Wave function ; Electronic structure ; Physics ; Computer science ; Statistical physics ; Quantum ; Quantum mechanics ; Molecule Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Advanced Chemical Physics Studies
Authors: Xingyi Guan | Akshaya Kumar Das | Christopher J. Stein | Farnaz Heidar‐Zadeh | Luke W. Bertels | Meili Liu | Mojtaba Haghighatlari | Jie Li | Oufan Zhang | Hongxia Hao | Itai Leven | Martin Head‐Gordon | Teresa Head‐Gordon
Source: Scientific Data (article, 2022-05-17)
Field: Materials Science — Materials Chemistry

A benchmark dataset for Hydrogen Combustion The generation of reference data for deep learning models is challenging for reactive systems, and more so for combustion reactions due to the extreme conditions that create radical species and alternative spin states during the combustion process. Here, we extend intrinsic reaction coordinate (IRC) calculations with ab initio MD simulations and normal mode displacement calculations to more extensively cover the potential energy surface for 19 reaction channels for hydrogen combustion. A total of ∼290,000 potential energies and ∼1,270,000 nuclear force vectors are evaluated with a high quality range-separated hybrid density functional, ωB97X-V, to construct the reference data set, including transition state ensembles, for the deep learning models to study hydrogen combustion reaction. Potential energy surface ; Combustion ; Ab initio ; Benchmark (surveying) ; Hydrogen ; Reaction coordinate ; Chemistry ; Potential energy ; Density functional theory ; Computer science ; Statistical physics ; Computational chemistry ; Physics ; Atomic physics ; Physical chemistry Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Crystallography and molecular interactions
Authors: Ryosuke Asahara | Tomoyuki Miyao
Source: ACS Omega (article, 2022-07-25)
Field: Materials Science — Materials Chemistry

Extended Connectivity Fingerprints as a Chemical Reaction Representation for Enantioselective Organophosphorus-Catalyzed Asymmetric Reaction Prediction Predicting the outcomes of organic reactions using data-driven approaches aids in the acceleration of research. In laboratory-scale experiments, only a small number of reaction data can be accessed for machine learning model construction, where reaction representations play a pivotal role in the success of model construction. Nevertheless, representation comparison for a small data set is not adequate. Herein, focusing on the enantioselectivity of phosphoric-acid-catalyzed reactions, various two-dimensional and three-dimensional reaction representations (descriptors) were compared. Overall, the concatenated form of the extended connectivity fingerprints showed the best predictive capability for the two types of data sets: high-throughput experimental data and manually collected literature data sets. Furthermore, highlighting the substructure contribution to the prediction outcome was shown to be informative for guiding catalyst development. Representation (politics) ; Computer science ; Data set ; Scale (ratio) ; Set (abstract data type) ; Catalysis ; Substructure ; Throughput ; Enantioselective synthesis ; Outcome (game theory) ; Training set ; Artificial intelligence ; Chemistry ; Mathematics ; Organic chemistry ; Engineering ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Asymmetric Hydrogenation and Catalysis