jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hehuan Ma | Yatao Bian | Yu Rong | Wenbing Huang | Tingyang Xu | Weiyang Xie | Geyan Ye | Junzhou Huang
Source: arXiv (Cornell University) (preprint, 2020-05-17)
Field: Materials Science — Materials Chemistry

Multi-View Graph Neural Networks for Molecular Property Prediction The crux of molecular property prediction is to generate meaningful representations of the molecules. One promising route is to exploit the molecular graph structure through Graph Neural Networks (GNNs). It is well known that both atoms and bonds significantly affect the chemical properties of a molecule, so an expressive model shall be able to exploit both node (atom) and edge (bond) information simultaneously. Guided by this observation, we present Multi-View Graph Neural Network (MV-GNN), a multi-view message passing architecture to enable more accurate predictions of molecular properties. In MV-GNN, we introduce a shared self-attentive readout component and disagreement loss to stabilize the training process. This readout component also renders the whole architecture interpretable. We further boost the expressive power of MV-GNN by proposing a cross-dependent message passing scheme that enhances information communication of the two views, which results in the MV-GNN^cross variant. Lastly, we theoretically justify the expressiveness of the two proposed models in terms of distinguishing non-isomorphism graphs. Extensive experiments demonstrate that MV-GNN models achieve remarkably superior performance over the state-of-the-art models on a variety of challenging benchmarks. Meanwhile, visualization results of the node importance are consistent with prior knowledge, which confirms the interpretability power of MV-GNN models. Interpretability ; Computer science ; Exploit ; Expressive power ; Molecular graph ; Graph ; Theoretical computer science ; Artificial neural network ; ENCODE ; Property (philosophy) ; Node (physics) ; Enhanced Data Rates for GSM Evolution ; Message passing ; Component (thermodynamics) ; Artificial intelligence ; Topology (electrical circuits) ; Distributed computing ; Chemistry ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Weile Jia | Handong Wang | Mohan Chen | Denghui Lu | Lin Lin | Roberto Car | E Weinan | Linfeng Zhang
Source: arXiv (Cornell University) (preprint, 2020-05-01)
Field: Materials Science — Materials Chemistry

Pushing the limit of molecular dynamics with ab initio accuracy to 100 million atoms with machine learning For 35 years, {\it ab initio} molecular dynamics (AIMD) has been the method of choice for modeling complex atomistic phenomena from first principles. However, most AIMD applications are limited by computational cost to systems with thousands of atoms at most. We report that a machine learning-based simulation protocol (Deep Potential Molecular Dynamics), while retaining {\it ab initio} accuracy, can simulate more than 1 nanosecond-long trajectory of over 100 million atoms per day, using a highly optimized code (GPU DeePMD-kit) on the Summit supercomputer. Our code can efficiently scale up to the entire Summit supercomputer, attaining $91$ PFLOPS in double precision ($45.5\%$ of the peak) and {$162$/$275$ PFLOPS in mixed-single/half precision}. The great accomplishment of this work is that it opens the door to simulating unprecedented size and time scales with {\it ab initio} accuracy. It also poses new challenges to the next-generation supercomputer for a better integration of machine learning and physical modeling. Supercomputer ; Ab initio ; Molecular dynamics ; Computer science ; Double-precision floating-point format ; Computational science ; Summit ; Ab initio quantum chemistry methods ; Statistical physics ; Artificial intelligence ; Parallel computing ; Computational chemistry ; Algorithm ; Chemistry ; Physics ; Floating point ; Quantum mechanics ; Molecule Machine Learning in Materials Science ; Protein Structure and Dynamics ; Advanced Chemical Physics Studies
Authors: Alexander V Shapeev | Evgeny V Podryabinkin | Konstantin Gubaev | Ferenc Tasnádi | Igor A Abrikosov
Source: New Journal of Physics (article, 2020-10-21)
Field: Materials Science — Materials Chemistry

Elinvar effect in β-Ti simulated by on-the-fly trained moment tensor potential Abstract A combination of quantum mechanics calculations with machine learning techniques can lead to a paradigm shift in our ability to predict materials properties from first principles. Here we show that on-the-fly training of an interatomic potential described through moment tensors provides the same accuracy as state-of-the-art ab initio molecular dynamics in predicting high-temperature elastic properties of materials with two orders of magnitude less computational effort. Using the technique, we investigate high-temperature bcc phase of titanium and predict very weak, Elinvar, temperature dependence of its elastic moduli, similar to the behavior of the so-called GUM Ti-based alloys (Sato et al 2003 Science 300 464). Given the fact that GUM alloys have complex chemical compositions and operate at room temperature, Elinvar properties of elemental bcc-Ti observed in the wide temperature interval 1100–1700 K is unique. Interatomic potential ; Moment (physics) ; Molecular dynamics ; Tensor (intrinsic definition) ; Phase (matter) ; Interval (graph theory) ; Titanium alloy Machine Learning in Materials Science ; Titanium Alloys Microstructure and Properties ; Advanced Electron Microscopy Techniques and Applications
Authors: Barbara Patrizi | Concetta Cozza | Adriana Pietropaolo | Paolo Foggi | Mario Siciliani de Cumis
Source: Molecules (review, 2020-01-20)
Field: Chemistry — Physical and Theoretical Chemistry

Synergistic Approach of Ultrafast Spectroscopy and Molecular Simulations in the Characterization of Intramolecular Charge Transfer in Push-Pull Molecules The comprehensive characterization of Intramolecular Charge Transfer (ICT) stemming in push-pull molecules with a delocalized π-system of electrons is noteworthy for a bespoke design of organic materials, spanning widespread applications from photovoltaics to nanomedicine imaging devices. Photo-induced ICT is characterized by structural reorganizations, which allows the molecule to adapt to the new electronic density distribution. Herein, we discuss recent photophysical advances combined with recent progresses in the computational chemistry of photoactive molecular ensembles. We focus the discussion on femtosecond Transient Absorption Spectroscopy (TAS) enabling us to follow the transition from a Locally Excited (LE) state to the ICT and to understand how the environment polarity influences radiative and non-radiative decay mechanisms. In many cases, the charge transfer transition is accompanied by structural rearrangements, such as the twisting or molecule planarization. The possibility of an accurate prediction of the charge-transfer occurring in complex molecules and molecular materials represents an enormous advantage in guiding new molecular and materials design. We briefly report on recent advances in ultrafast multidimensional spectroscopy, in particular, Two-Dimensional Electronic Spectroscopy (2DES), in unraveling the ICT nature of push-pull molecular systems. A theoretical description at the atomistic level of photo-induced molecular transitions can predict with reasonable accuracy the properties of photoactive molecules. In this framework, the review includes a discussion on the advances from simulation and modeling, which have provided, over the years, significant information on photoexcitation, emission, charge-transport, and decay pathways. Density Functional Theory (DFT) coupled with the Time-Dependent (TD) framework can describe electronic properties and dynamics for a limited system size. More recently, Machine Learning (ML) or deep learning approaches, as well as free-energy simulations containing excited state potentials, can speed up the calculations with transferable accuracy to more complex molecules with extended system size. A perspective on combining ultrafast spectroscopy with molecular simulations is foreseen for optimizing the design of photoactive compounds with tunable properties. Photoexcitation ; Density functional theory ; Chemical physics ; Delocalized electron ; Ultrafast laser spectroscopy ; Spectroscopy ; Intramolecular force ; Nanotechnology ; Molecular electronics ; Chemistry ; Molecule ; Materials science ; Excited state ; Computational chemistry ; Physics ; Atomic physics Photochemistry and Electron Transfer Studies ; Spectroscopy and Quantum Chemical Studies ; Advanced Chemical Physics Studies
Authors: Chong Liu | Yuxin Li | Motoshi Takao | Takashi Toyao | Zen Maeno | Takashi Kamachi | Yoyo Hinuma | Ichigaku Takigawa | Ken‐ichi Shimizu
Source: The Journal of Physical Chemistry C (article, 2020-06-16)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Frontier Molecular Orbital Based Analysis of Solid–Adsorbate Interactions over Group 13 Metal Oxide Surfaces Adsorption is an essential process that takes place in heterogeneous catalysis. In the current study, solid–adsorbate interactions occurring between a variety of small molecules and surfaces of group 13 metal oxides, including β-Ga2O3(100), β-Ga2O3(001), θ-Al2O3(100), θ-Al2O3(001), θ-Al2O3(010), In2O3(110), and In2O3(111), were investigated using density functional theory calculations and a machine learning (ML)-based statistical method. The adsorbates utilized for this purpose include CO, CO2, N2, NH3, H2O, acetonitrile, acetone, acetamide, acetic acid, alkanes, alkenes, aromatic compounds, alcohols, and amines. The results show that the adsorption energies (Eads) of each metal oxide surface correlate linearly with the highest occupied molecular orbital (HOMO) energies of the adsorbates and not with energies of the lowest unoccupied molecular orbital (LUMO) of the small molecules. Moreover, in these systems, contributions to molecular adsorption are dominated by interactions between the HOMOs of the adsorbates and the surface conduction band of the metal oxides. Furthermore, the surface energy was found to be an important parameter influencing Eads values of different metal oxides. Finally, the results of statistical analysis using a ML approach confirmed that adsorbate HOMOs and surface energy of metal oxides are the most influential factors governing molecular adsorption, and also demonstrated that dipole moments of adsorbates contribute to controlling to adsorption. Adsorption ; HOMO/LUMO ; Chemistry ; Molecular orbital ; Oxide ; Molecule ; Metal ; Density functional theory ; Physical chemistry ; Acetonitrile ; Computational chemistry ; Inorganic chemistry ; Chemical physics ; Organic chemistry Ga2O3 and related materials ; Catalytic Processes in Materials Science ; Advanced Photocatalysis Techniques
Authors: Michael Hutchinson | Charline Le Lan | Sheheryar Zaidi | Emilien Dupont | Yee Whye Teh | Hyunjik Kim
Source: arXiv (Cornell University) (preprint, 2020-12-20)
Field: Materials Science — Materials Chemistry

LieTransformer: Equivariant self-attention for Lie Groups Group equivariant neural networks are used as building blocks of group invariant neural networks, which have been shown to improve generalisation performance and data efficiency through principled parameter sharing. Such works have mostly focused on group equivariant convolutions, building on the result that group equivariant linear maps are necessarily convolutions. In this work, we extend the scope of the literature to self-attention, that is emerging as a prominent building block of deep learning models. We propose the LieTransformer, an architecture composed of LieSelfAttention layers that are equivariant to arbitrary Lie groups and their discrete subgroups. We demonstrate the generality of our approach by showing experimental results that are competitive to baseline methods on a wide range of tasks: shape counting on point clouds, molecular property regression and modelling particle trajectories under Hamiltonian dynamics. Equivariant map ; Lie group ; Generality ; Group (periodic table) ; Computer science ; Pure mathematics ; Invariant (physics) ; Mathematics ; Artificial intelligence ; Theoretical computer science ; Algebra over a field ; Physics ; Psychology Machine Learning in Materials Science ; Topic Modeling ; Advanced Graph Neural Networks
Authors: Luchun Yan | Yupeng Diao | Kewei Gao
Source: Materials (article, 2020-07-23)
Field: Materials Science — Metals and Alloys

Analysis of Environmental Factors Affecting the Atmospheric Corrosion Rate of Low-Alloy Steel Using Random Forest-Based Models As one of the factors (e.g., material properties, surface quality, etc.) influencing the corrosion processes, researchers have always been exploring the role of environmental factors to understand the mechanism of atmospheric corrosion. This study proposes a random forest algorithm-based modeling method that successfully maps both the steel's chemical composition and environmental factors to the corrosion rate of low-alloy steel under the corresponding environmental conditions. Using the random forest models based on the corrosion data of three different atmospheric environments, the environmental factors were proved to have different importance sequence in determining the environmental corrosivity of open and sheltered exposure test conditions. For each exposure test site, the importance of environmental features to the corrosion rate is also ranked and analyzed. Additionally, the feasibility of the random forest model to predict the corrosion rate of steel samples in the new environment is also demonstrated. The volume and representativeness of the corrosion data in the training data are considered to be the critical factors in determining its prediction performance. The above results prove that machine learning provides a useful tool for the analysis of atmospheric corrosion mechanisms and the evaluation of corrosion resistance. Random forest ; Corrosion ; Alloy ; Materials science ; Environmental science ; Metallurgy ; Computer science ; Machine learning Hydrogen embrittlement and corrosion behaviors in metals ; Non-Destructive Testing Techniques ; Corrosion Behavior and Inhibition
Authors: Sanni Voutilainen | Markus Heinonen | Martina Andberg | Emmi Jokinen | Hannu Maaheimo | Johan Pääkkönen | Nina Hakulinen | Juha Rouvinen | Harri Lähdesmäki | Samuel Kaski | Juho Rousu | Merja Penttilä | Anu Koivula
Source: Applied Microbiology and Biotechnology (article, 2020-11-04)
Field: Materials Science — Materials Chemistry

Substrate specificity of 2-deoxy-D-ribose 5-phosphate aldolase (DERA) assessed by different protein engineering and machine learning methods In this work, deoxyribose-5-phosphate aldolase (Ec DERA, EC 4.1.2.4) from Escherichia coli was chosen as the protein engineering target for improving the substrate preference towards smaller, non-phosphorylated aldehyde donor substrates, in particular towards acetaldehyde. The initial broad set of mutations was directed to 24 amino acid positions in the active site or in the close vicinity, based on the 3D complex structure of the E. coli DERA wild-type aldolase. The specific activity of the DERA variants containing one to three amino acid mutations was characterised using three different substrates. A novel machine learning (ML) model utilising Gaussian processes and feature learning was applied for the 3rd mutagenesis round to predict new beneficial mutant combinations. This led to the most clear-cut (two- to threefold) improvement in acetaldehyde (C2) addition capability with the concomitant abolishment of the activity towards the natural donor molecule glyceraldehyde-3-phosphate (C3P) as well as the non-phosphorylated equivalent (C3). The Ec DERA variants were also tested on aldol reaction utilising formaldehyde (C1) as the donor. Ec DERA wild-type was shown to be able to carry out this reaction, and furthermore, some of the improved variants on acetaldehyde addition reaction turned out to have also improved activity on formaldehyde. KEY POINTS: • DERA aldolases are promiscuous enzymes. • Synthetic utility of DERA aldolase was improved by protein engineering approaches. • Machine learning methods aid the protein engineering of DERA. Aldolase A ; Directed evolution ; Protein engineering ; Biochemistry ; Acetaldehyde ; Chemistry ; Fructose-bisphosphate aldolase ; Active site ; Substrate (aquarium) ; Stereochemistry ; Mutagenesis ; Metabolic engineering ; Enzyme ; Mutant ; Biology ; Gene Enzyme Structure and Function ; Bacterial Genetics and Biotechnology ; RNA and protein synthesis mechanisms
Authors: Zhongli Liu | Peng Kang | Yu Zhu | Lei Liu | Hong Guo
Source: APL Materials (article, 2020-06-01)
Field: Materials Science — Materials Chemistry

Material informatics for layered high-<i>T</i> <i>C</i> superconductors Superconductors were typically discovered by trial-and-error aided by the knowledge and intuition of individual researchers. In this work, using materials informatics aided by machine learning (ML), we build an ML model of superconductors, which is based on several material descriptors with apparent physical meanings to efficiently predict critical superconducting temperature TC. The descriptors include the average atomic mass of a compound, the average number of electrons in an unfilled shell, the average ground state atomic magnetic moments, the maximum difference of electronegativity, etc. To fully optimize the ML model, we develop a multi-step learning and multi-algorithm cross-verification approach. For known high TC superconductors, our ML model predicts excellent TC values with over 92% confidence. When the ML model is applied to about 2500 layered materials in the inorganic crystal structure database, 25 of them are predicted to be superconductors not known before, including 12 cuprates, 7 iron-based crystals, and 6 others, with TC ranging from ∼32 K to ∼138 K. The findings shed considerable light on the mapping between the material descriptors and TC for layered superconductors. The ML calculates that in our descriptors, the maximum difference of electronegativity is the most important one. Electronegativity ; Superconductivity ; Materials science ; Cuprate ; High-temperature superconductivity ; Condensed matter physics ; Atomic radius ; Materials informatics ; Machine learning ; Computer science ; Physics ; Quantum mechanics ; Health informatics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; X-ray Diffraction in Crystallography
Authors: Vikas Rana | Shuank Malik | Gyanesh Joshi | Nikhil Kumar Rajput | Praveen Kumar Gupta
Source: International Journal of Biological Macromolecules (article, 2020-12-30)
Field: Materials Science — Biomaterials

Preparation of alpha cellulose from sugarcane bagasse and its cationization: Synthesis, characterization, validation and application as wet-end additive Bagasse ; Cationic polymerization ; Cellulose ; Pulp (tooth) ; Chemistry ; Fourier transform infrared spectroscopy ; Polymer ; Nuclear chemistry ; Pulp and paper industry ; Materials science ; Organic chemistry ; Chemical engineering Advanced Cellulose Research Studies ; Biofuel production and bioconversion ; Lignin and Wood Chemistry
Authors: Shuhui Guan | Cheng Shang | Zhi‐Pan Liu
Source: The Journal of Physical Chemistry C (article, 2020-06-22)
Field: Materials Science — Materials Chemistry

Resolving the Temperature and Composition Dependence of Ion Conductivity for Yttria-Stabilized Zirconia from Machine Learning Simulation The temperature and composition dependence of the ion conductivity for yttria-stabilized zirconia (YSZ) have been hotly studied over the past 50 years. Due to the sluggish oxygen anion diffusion and the low doping of oxide, the computation of ion conductivity traditionally has to be performed with empirical force field potentials in order to achieve the required long timescale, which, however, fails to reproduce some critical observations in experiment, e.g., the conductivity maximum achieved at 8 mol % YSZ at the operating temperatures. Here by using our recently developed Y–Zr–O global neural network (G-NN) potential, we are able to carry out a series of long-time molecular dynamics simulations for YSZ at 6.7, 8, 10, and 14.3 mol % over a wide temperature range (800–2000 K). This finally quantitatively resolves the effects of temperature and composition on the ion conductivity. We confirm the key experimental findings that (i) 8 mol % YSZ has the highest ion conductivity, 0.16–0.51 S/cm, at 1200–1600 K, agreeing with 0.16–0.55 S/cm in experiment, and the maximum conductivity shifts to the higher Y composition above 1600 K and (ii) over the wide temperature range (800–2000 K) the ion conductivity of 8YSZ exhibits the non-Arrhenius behaviors with two different activation energies. The physical origin for these peculiar phenomena is revealed at the atomic level by analyzing the MD pathways. The presence of monoclinic phase and the aggregation of oxygen vacancy along ⟨112⟩ are two key factors to retard oxygen diffusion. For 8 mol % YSZ, the oxygen movement is dominated by local vibrations below 1000 K but becomes delocalized above 1000 K, which results in the gradual aggregation of oxygen vacancy along a new ⟨112⟩ direction. Our results demonstrate that the G-NN potential from unbiased machine learning of the global potential energy surface can meet the high standard in both accuracy and speed required for material simulation. Conductivity ; Yttria-stabilized zirconia ; Materials science ; Arrhenius equation ; Ion ; Atmospheric temperature range ; Diffusion ; Analytical Chemistry (journal) ; Activation energy ; Cubic zirconia ; Thermodynamics ; Chemistry ; Physical chemistry ; Composite material ; Ceramic ; Physics Advancements in Solid Oxide Fuel Cells ; Fuel Cells and Related Materials ; Machine Learning in Materials Science
Authors: C. Lawrence Zitnick | Lowik Chanussot | Abhishek Das | Siddharth Goyal | Javier Heras‐Domingo | Caleb Ho | Weihua Hu | Thibaut Lavril | Aini Palizhati | Morgane Rivière | Muhammed Shuaibi | Anuroop Sriram | Kevin Tran | Brandon C. Wood | Junwoong Yoon | Devi Parikh | Zachary W. Ulissi
Source: arXiv (Cornell University) (preprint, 2020-10-14)
Field: Materials Science — Materials Chemistry

An Introduction to Electrocatalyst Design using Machine Learning for\n Renewable Energy Storage Scalable and cost-effective solutions to renewable energy storage are\nessential to addressing the world's rising energy needs while reducing climate\nchange. As we increase our reliance on renewable energy sources such as wind\nand solar, which produce intermittent power, storage is needed to transfer\npower from times of peak generation to peak demand. This may require the\nstorage of power for hours, days, or months. One solution that offers the\npotential of scaling to nation-sized grids is the conversion of renewable\nenergy to other fuels, such as hydrogen or methane. To be widely adopted, this\nprocess requires cost-effective solutions to running electrochemical reactions.\nAn open challenge is finding low-cost electrocatalysts to drive these reactions\nat high rates. Through the use of quantum mechanical simulations (density\nfunctional theory), new catalyst structures can be tested and evaluated.\nUnfortunately, the high computational cost of these simulations limits the\nnumber of structures that may be tested. The use of machine learning may\nprovide a method to efficiently approximate these calculations, leading to new\napproaches in finding effective electrocatalysts. In this paper, we provide an\nintroduction to the challenges in finding suitable electrocatalysts, how\nmachine learning may be applied to the problem, and the use of the Open\nCatalyst Project OC20 dataset for model training.\n Renewable energy ; Energy storage ; Scalability ; Electrocatalyst ; Computer science ; Process engineering ; Wind power ; Process (computing) ; Power (physics) ; Engineering ; Electrical engineering ; Electrochemistry ; Chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Hybrid Renewable Energy Systems