jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sara Paolillo | Alain Moussa | Gayle Murdoch | Frédéric Lazzarino | Anne-Laure Charley | Philippe Leray | Joey Hung | Roy Koret | Shay Wolfling | Avron Ger
Source:  (article, 2020-05-12)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Ruthenium direct etch scatterometry solution for self-aligning semi-damascene Beyond the 5nm technology node, interconnect scaling has an impact on metal material selection: usage of copper may hit a limit with respect to resistance and reliability performance [1]. Thickness of barrier and liner (required for copper) cannot be reduced further, meaning that trench width reduction will have a negative effect on the relative copper volume. Grain boundary scattering increases as well, which in turn further increases resistivity and resistance. One of the best alternatives is Ruthenium (Ru), but dual-damascene processing is difficult with Ru (requires improvements in Ru filling of narrow high aspect ratio trenches and in Ru CMP selectivity and defects). So, a Back End of Line (BEOL) material change may require a move to a semi-damascene integration with direct Ruthenium metal etch [2]. This shift from a well-known dual-damascene flow, based on metal CMP, to a semi-damascene flow, based on metal etch integration, will require a new set of metrology capabilities, which are studied in this paper. In the current study, Scatterometry is widely used in all semi-damascene process steps to monitor both dimensional and material properties. Important measured parameters include thickness, full profile details, grain size, and roughness of metal lines—all parameters that are required for feedback and in-line process control. We will describe how these parameters can be monitored using a single Scatterometry metrology system. The semi-damascene process development described in this paper exploits EUV lithography at a critical dimension (CD) of 16 nm and 32 nm pitch and includes the optimization of the following process steps: 1. Metal deposition techniques: ALD and PVD, with a wide range of metal thickness. 2. Anneal , affecting grain size. 3. Etch process , for optimal metal line profile and roughness. The Scatterometry results were evaluated and verified by reference techniques such as CDSEM, and HAADF-STEM. The goal of the process optimization was Ru resistivity and resistance. In-line Scatterometry was shown to accurately predict the resistance of the Ru lines — parameter that is measured at the end of the processing, and is affected by all processes, including deposition, annealing, and etch. Prediction was carried out by a machine learning algorithm, based on an E-test, combining the contribution of all three process steps into a single output, at the post-etch measurement phase. Copper interconnect ; Materials science ; Back end of line ; Critical dimension ; Lithography ; Optoelectronics ; Extreme ultraviolet lithography ; Electronic engineering ; Nanotechnology ; Optics ; Engineering ; Dielectric Copper Interconnects and Reliability ; Advancements in Photolithography Techniques ; Semiconductor materials and devices
Authors: Dakota Folmsbee | Geoffrey Hutchison
Source: ChemRxiv (preprint, 2020-05-18)
Field: Materials Science — Materials Chemistry

Assessing Conformer Energies using Electronic Structure and Machine Learning Methods We have performed a large-scale evaluation of current computational methods, including conventional small-molecule force fields, semiempirical, density functional, ab initio electronic structure methods, and current machine learning (ML) techniques to evaluate relative single-point energies. Using up to 10 local minima geometries across ~700 molecules, each optimized by B3LYP-D3BJ with single-point DLPNO-CCSD(T) triple-zeta energies, we consider over 6,500 single points to compare the correlation between different methods for both relative energies and ordered rankings of minima. We find promise from current ML methods and recommend methods at each tier of the accuracy-time tradeoff, particularly the recent GFN2 semiempirical method, the B97-3c density functional approximation, and RI-MP2 for accurate conformer energies. The ANI family of ML methods shows promise, particularly the ANI-1ccx variant trained in part on coupled-cluster energies. Multiple methods suggest continued improvements should be expected in both performance and accuracy. Maxima and minima ; Ab initio ; Density functional theory ; Coupled cluster ; Conformational isomerism ; Single point ; Electronic structure ; Point (geometry) ; Computer science ; Statistical physics ; Molecule ; Physics ; Artificial intelligence ; Mathematics ; Quantum mechanics ; Geometry ; Mathematical analysis Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Shi Jun Ang | Wujie Wang | Daniel Schwalbe‐Koda | Simon Axelrod | Rafael Gómez‐Bombarelli
Source: ChemRxiv (preprint, 2020-02-28)
Field: Materials Science — Materials Chemistry

Active Learning Accelerates Ab Initio Molecular Dynamics on Pericyclic Reactive Energy Surfaces Modeling dynamical effects in chemical reactions, such as post-transition state bifurcation, requires ab initio molecular dynamics simulations due to the breakdown of simpler static models like transition state theory. However, these simulations tend to be restricted to lower-accuracy electronic structure methods and scarce sampling because of their high computational cost. Here, we report the use of statistical learning to accelerate reactive molecular dynamics simulations by combining high-throughput ab initio calculations, graph-convolution interatomic potentials and active learning. This pipeline was demonstrated on an ambimodal trispericyclic reaction involving 8,8-dicyanoheptafulvene and 6,6-dimethylfulvene. With a training dataset size of approximately 31,000 M062X/def2-SVP quantum mechanical calculations, the computational cost of exploring the reactive potential energy surface was reduced by an order of magnitude. These tools are then used to automatically predict a reaction mechanism that is in agreement with the experimentally-reported product distribution. In addition, thousands of virtually costless picosecond-long reactive trajectories suggest that post-transition state bifurcation plays a very minor role in the reaction. Furthermore, a transfer-learning strategy effectively allowed to upgrade the potential energy surface to higher levels of theory (def2-TZVPD basis set and double hybrid functional) using less than 10% additional calculations. Since these methods capture intramolecular non-covalent interactions more accurately, they uncover longer lifetimes for entropic intermediates. This overall approach is broadly applicable and opens the door to the study of dynamical effects in larger, previously-intractable reactive systems. Ab initio ; Potential energy surface ; Molecular dynamics ; Statistical physics ; Transition state theory ; Transition state ; Chemistry ; Chemical physics ; Computational chemistry ; Intramolecular force ; Computer science ; Physics ; Quantum mechanics ; Reaction rate constant Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Advanced Chemical Physics Studies
Authors: Alexander Khort | Jonas Hedberg | Nanxuan Mei | Valentin Romanovski | Eva Blomberg | Inger Odnevall Wallinder
Source: Research Square (preprint, 2020-12-14)
Field: Chemistry — Organic Chemistry

Corrosion and Transformation of Solution Combustion Synthesized Co, Ni and CoNi Nanoparticles in Synthetic Freshwater with and without Natural Organic Matter Abstract Pure metallic Co, Ni, and their bimetallic compositions of Co 3 Ni, CoNi, and CoNi 3 nanomaterials were prepared by solution combustion synthesis. Microstructure, phase composition, and crystalline structure of these nanoparticles (NPs) were characterized along with studies of their corrosion and dissolution properties in synthetic freshwater with and without natural organic matter (NOM). The nanomaterials consisted of aggregates of fine NPs (3–30 nm) of almost pure metallic and bimetallic crystal phases with thin surface oxide covered by a thin carbon shell. The nanomaterials were characterized by BET surface areas ranging from ~ 1 to 8 m 2 /g for the Ni and Co NPs, to 22.93 m 2 /g, 14.86 m 2 /g, and 10.53 m 2 /g for the Co 3 Ni, CoNi, CoNi 3 NPs, respectively. More Co and Ni were released from the bimetallic NPs compared with the pure metals although their corrosion current densities were lower. In contrast to findings for the pure metal NPs, the presence of NOM increased the release of Co and Ni from the bimetallic NPs in freshwater compared to freshwater only even though its presence reduced the corrosion rate (current density). It was shown that the properties of the bimetallic nanomaterials were influenced by multiple factors such as their composition, including carbon shell, type of surface oxides, and the entropy of mixing. Bimetallic strip ; Nanomaterials ; Materials science ; Corrosion ; Nanoparticle ; Chemical engineering ; Dissolution ; Metal ; Oxide ; Microstructure ; Metallurgy ; Nanotechnology Nanomaterials for catalytic reactions ; Catalytic Processes in Materials Science ; Mercury impact and mitigation studies
Authors: Steven B. Torrisi | Arunima K. Singh | Joseph H. Montoya | Tathagata Biswas | Kristin A. Persson
Source: npj 2D Materials and Applications (preprint, 2020-07-16)
Field: Materials Science — Materials Chemistry

Two-dimensional forms of robust CO2 reduction photocatalysts Abstract Photoelectrocatalysts that use sunlight to power the CO 2 reduction reaction will be crucial for carbon-neutral power and energy-efficient industrial processes. Scalable photoelectrocatalysts must satisfy a stringent set of criteria, such as stability under operating conditions, product selectivity, and efficient light absorption. Two-dimensional materials can offer high specific surface area, tunability, and potential for heterostructuring, providing a fresh landscape of candidate catalysts. From a set of promising bulk CO 2 reduction photoelectrocatalysts, we screen for candidate monolayers of these materials, then study their catalytic feasibility and suitability. For stable monolayer candidates, we verify the presence of visible-light band gaps, check that band edges can support CO 2 reduction, determine exciton binding energies, and compute surface reactivity. We find visible light absorption for SiAs, ZnTe, and ZnSe monolayers, and that due to a lack of binding, CO selectivity is possible. We thus identify SiAs, ZnTe, and ZnSe monolayers as targets for further investigation, expanding the chemical space for CO 2 photoreduction candidates. Monolayer ; Reduction (mathematics) ; Absorption (acoustics) ; Selectivity ; Materials science ; Catalysis ; Band gap ; Optoelectronics ; Scalability ; Photochemistry ; Nanotechnology ; Chemistry ; Computer science ; Mathematics ; Organic chemistry Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Advanced Photocatalysis Techniques
Authors: Laia Vilà‐Nadal
Source:  (preprint, 2020-06-01)
Field: Materials Science — Materials Chemistry

POMzites a roadmap for inverse design in metal oxide chemistry Exploring the space generated by the self-assembly of known metal oxides computationally and the ability to predict new architectures is a challenging task. Narrowing it down to a family of all-inorganic porous materials, composed of ring-shaped tungsten oxide building blocks connected with transition metal linkers forming zero to three dimensional frameworks. Structurally like zeolites (213 recognized structures), but an order of magnitude smaller, the POMzite library has only 14 members. Applying the concept of ‘materials by design’ applied to molecular metal oxide chemistry synthesis, and their assembly into frameworks. Structures with new topologies, but aiming for pure inorganic systems, will be targeted from the beginning. Oxide ; Tungsten oxide ; Nanotechnology ; Tungsten ; Transition metal ; Inverse ; Materials science ; Porosity ; Metal ; Space (punctuation) ; Computer science ; Topology (electrical circuits) ; Chemistry ; Engineering ; Catalysis ; Geometry ; Metallurgy ; Composite material ; Organic chemistry ; Mathematics ; Electrical engineering X-ray Diffraction in Crystallography
Authors: Wei Zhao | Qing Li | Xian-Hui Huang | Lihua Bie | Jun Gao
Source: Frontiers in Chemistry (article, 2020-03-31)
Field: Materials Science — Materials Chemistry

Toward the Prediction of Multi-Spin State Charges of a Heme Model by Random Forest Regression The random forest regression (RFR) model was introduced to predict the multiple spin state charges of a heme model, which is important for the molecular dynamic simulation of the spin crossover phenomenon. In this work, a multiple spin state structure data set with 39,368 structures of the simplified heme-oxygen binding model was built from the non-adiabatic dynamic simulation trajectories. The ESP charges of each atom were calculated and used as the real-valued response. The conformational adapted charge model (CAC) of three spin states was constructed by an RFR model using symmetry functions. The results show that our RFR model can effectively predict the on the fly atomic charges with the varying conformations as well as the atomic charge of different spin states in the same conformation, thus achieving the balance of accuracy and efficiency. The average mean absolute error of the predicted charges of each spin state is <0.02 e. The comparison studies on descriptors showed a maximum 0.06 e improvement in prediction of the charge of <i>Fe</i> <sup>2+</sup> by using 11 manually selected structural parameters. We hope that this model can not only provide variable parameters for developing the force field of the multi-spin state but also facilitate automation, thus enabling large-scale simulations of atomistic systems. Charge (physics) ; Spin (aerodynamics) ; Statistical physics ; Adiabatic process ; Molecular dynamics ; Random forest ; Collinearity ; Chemistry ; Physics ; Computer science ; Thermodynamics ; Computational chemistry ; Mathematics ; Quantum mechanics ; Artificial intelligence ; Statistics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Daniel Salley | Graham Keenan | De‐Liang Long | Nicola L. Bell | Leroy Cronin
Source: ChemRxiv (preprint, 2020-04-09)
Field: Materials Science — Materials Chemistry

A Modular Programmable Inorganic Cluster Discovery Robot for the Discovery and Synthesis of Polyoxometalates The exploration of complex multi-component chemical reactions leading to new clusters, where discovery requires both molecular self-assembly and crystallization, is a major challenge. This is because the systematic approach required for an experimental search is limited when the number of parameters in a chemical space becomes too large, restricting both exploration, and reproducibility. Herein, we present a synthetic strategy to systematically search a very large set of potential reactions, using an inexpensive, high-throughput platform; modular in terms of both hardware and software, and capable of running multiple reactions with in-line analysis; for the automation of inorganic and materials chemistry. The platform has been used to explore several inorganic chemical spaces to discover new, and reproduce known, tungsten-based, mixed transition-metal polyoxometalate clusters, giving a digital code allowing the easy repeat synthesis of the clusters. Among the many species identified in this work, most significantly is the discovery of a novel, purely inorganic W24FeIII-superoxide cluster formed under ambient conditions. The Modular Wheel Platform (MWP) was then employed to undertake two chemical space explorations producing compounds [1-4]: (C2H8N)10Na2[H6Fe(O2)W24O82(H2O)25] (1, {W24Fe}), (C2H8N)72Na16[H16Co8W200O660(H2O)40] (2, {W200Co8}), (C2H8N)72Na16[H16Ni8W200 O660-(H2O)40] (3, {W200Ni8}) and (C2H8N)14[H26W34V4O130] (4, {W34V4}), along with many other known species, for example simple Keggin clusters and 1D {W11M2+} chains. Chemical space ; Cluster (spacecraft) ; Modular design ; Polyoxometalate ; Chemistry ; Nanotechnology ; Materials science ; Combinatorial chemistry ; Computer science ; Drug discovery ; Catalysis ; Organic chemistry Polyoxometalates: Synthesis and Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Nanocluster Synthesis and Applications
Authors: Alyssa J. R. Hensley | Ian deJoode | Yong Wang | Jean‐Sabin McEwen
Source: Topics in Catalysis (article, 2020-10-19)
Field: Materials Science — Materials Chemistry

Identifying Trends in the Field Ionization of Diatomic Molecules over Adsorbate Covered Pd(331) Surfaces Field ion microscope ; Ionization ; Field desorption ; Diatomic molecule ; Ionization energy ; Field (mathematics) ; Chemistry ; Ion ; Field electron emission ; Atomic physics ; Chemical physics ; Molecule ; Electron ; Physics Electronic and Structural Properties of Oxides ; Electrochemical Analysis and Applications ; Electrocatalysts for Energy Conversion
Authors: Edward O. Pyzer‐Knapp | Graeme M. Day | Linjiang Chen | Andrew I. Cooper
Source: ChemRxiv (preprint, 2020-09-30)
Field: Materials Science — Materials Chemistry

Distributed Multi-Objective Bayesian Optimization for the Intelligent Navigation of Energy Structure Function Maps For Efficient Property Discovery Energy-structure-function (ESF) maps have emerged as a powerful tool for in silico materials design, coupling crystal structure prediction techniques with property simulations to assess the potential for new candidate materials to display desirable properties. Despite continuing increases to accessible computational power, however, the computational cost of acquiring an ESF map often remains too high to allow integration into true high-throughput virtual screening techniques. In this paper, we propose the next evolution of the ESF map, which uses parallel Bayesian optimization to selectively acquire energy and property data, generating the same levels of insight at a fraction of the computational cost by limiting the expensive property calculations to a small fraction of the predicted crystal structures associated with a molecule. We utilize this approach to obtain a two orders of magnitude speedup on a previous ESF study that focused on methane capture materials, saving over 500,000 CPUh from the original protocol. Through acceleration of the acquisition of ESF-type insight, we pave the way for the use of ESF maps in automated ultra-high throughput screening pipelines. This greatly reduce the opportunity risk associated with the choice of system to calculate. For example, it will allow researchers to use ESF maps in the search for physical properties where the computational costs are currently just intractable, or to investigate orders of magnitude more systems for a given computational cost. Computer science ; Speedup ; Property (philosophy) ; Bayesian optimization ; Throughput ; Function (biology) ; Bayesian probability ; Pipeline (software) ; Distributed computing ; Data mining ; Machine learning ; Artificial intelligence ; Parallel computing Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Advanced Chemical Physics Studies
Authors: Torstein Fjermestad | Wenqing Li | Alexander Genest | Notker Rösch
Source: SN Applied Sciences (article, 2020-10-28)
Field: Materials Science — Materials Chemistry

Configurations of V4+ centers in the MoVO catalyst material. A systematic stability analysis of DFT results Polaron ; Catalysis ; Stability (learning theory) ; Density functional theory ; Lattice (music) ; Work (physics) ; Reactivity (psychology) ; Chemical physics ; Occupancy ; Chemistry ; Materials science ; Thermodynamics ; Computational chemistry ; Physics ; Statistical physics ; Computer science ; Electron ; Nuclear physics Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Catalysis and Hydrodesulfurization Studies
Authors: Robert H. Coridan
Source: Chemical Communications (article, 2020-01-01)
Field: Materials Science — Polymers and Plastics

A neural network-based approach to predicting absorption in nanostructured, disordered photoelectrodes Disordered nanostructures in photoelectrodes can increase light absorption in photoelectrochemical system designs. Predicting their optical properties is an elusive task due to the immensity of unique configurations and the intrinsic variance of each. A neural network trained from a small subset of simulations can emulate the complex absorption properties of the entire configuration space for a model disordered system with quantifiable accuracy and computational efficiency. Absorption (acoustics) ; Nanostructure ; Artificial neural network ; Materials science ; Nanotechnology ; Space (punctuation) ; Computer science ; Chemical physics ; Biological system ; Chemistry ; Machine learning Transition Metal Oxide Nanomaterials ; Random lasers and scattering media ; Photonic Crystals and Applications
Authors: Jahaun Azadmanesh | William E. Lutz | Leighton Coates | Kevin L. Weiss | Gloria E. O. Borgstahl
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2020-11-02)
Field: Chemistry — Inorganic Chemistry

Direct detection of coupled proton and electron transfers in human manganese superoxide dismutase Abstract Human manganese superoxide dismutase (MnSOD) is a critical oxidoreductase found in the mitochondrial matrix. Concerted proton and electron transfers (CPETs) are used by the enzyme to rid the mitochondria of O 2 •− , a precursor to other harmful reactive oxygen and nitrogen species. The mechanisms of CPET-utilizing enzymes are typically unknown due to the difficulties in detecting the protonation states of specific residues and solvent molecules involved in catalysis while controlling the redox state of the enzyme. Here, neutron diffraction of redox-controlled MnSOD crystals revealed the all-atom structures of Mn 3+ SOD and Mn 2+ SOD delivering unique data on sites that change protonation state. A novel mechanism is proposed from the direct observation of glutamine deprotonation, the involvement of Tyr and His with altered pK a s, and four unusual strong-short hydrogen bonds, including a low barrier hydrogen bond, that change with the oxidation state of the metal. Quantum calculations provide insight into the electronic modulation of the observed structures and the enzymatic mechanism. Chemistry ; Protonation ; Oxidoreductase ; Deprotonation ; Superoxide dismutase ; Redox ; Photochemistry ; Manganese ; Enzyme ; Biochemistry ; Inorganic chemistry ; Organic chemistry Metal-Catalyzed Oxygenation Mechanisms ; Mitochondrial Function and Pathology ; Electron Spin Resonance Studies