jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: David E. Keyes
Source: Communications of the ACM (article, 2021-03-22)
Field: Materials Science — Materials Chemistry

The Arab world prepares the exascale workforce THE ARAB WORLD is currently host to eight supercomputers in the Top500 globally, including the current #10 and a former #7. Hardware can become a honeypot for talent attraction—senior talent from abroad, and rising talent from within. Good return on investment from leading-edge hardware motivates forging collaborative ties to global supercomputing leaders, which leads to integration into the global&#13;\ncampaigns that supercomputing excels in, such as predicting climate change and developing sustainable energy resources for its mitigation,&#13;\npositing properties of new materials and catalysis by design, repurposing already-certified drugs and discovering new ones, and big data analytics and machine learning applied to science and to society. While the petroleum industry has been the historical motivation for supercomputing in the Arab World with its workloads of seismic imaging and reservoir modeling, the attraction today is universal. Citation ; Workforce ; Computer science ; Political science ; Library science ; Law Graphene research and applications ; Ferroelectric and Negative Capacitance Devices
Authors: Shanzhuo Zhang | Lihang Liu | Sheng Gao | Donglong He | Xiaomin Fang | Weibin Li | Zhengjie Huang | Weiyue Su | Wenjin Wang
Source: arXiv (Cornell University) (preprint, 2021-06-28)
Field: Materials Science — Materials Chemistry

LiteGEM: Lite Geometry Enhanced Molecular Representation Learning for Quantum Property Prediction In this report, we (SuperHelix team) present our solution to KDD Cup 2021-PCQM4M-LSC, a large-scale quantum chemistry dataset on predicting HOMO-LUMO gap of molecules. Our solution, Lite Geometry Enhanced Molecular representation learning (LiteGEM) achieves a mean absolute error (MAE) of 0.1204 on the test set with the help of deep graph neural networks and various self-supervised learning tasks. The code of the framework can be found in https://github.com/PaddlePaddle/PaddleHelix/tree/dev/competition/kddcup2021-PCQM4M-LSC/. Representation (politics) ; Property (philosophy) ; Geometry ; Quantum ; Computer science ; Physics ; Mathematics ; Quantum mechanics ; Political science ; Philosophy ; Epistemology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Dominikus Brian | Xiang Sun
Source:  (preprint, 2021-11-08)
Field: Materials Science — Materials Chemistry

Charge Transfer Landscape Manifesting Structure-Rate Relationship in the Condensed Phase via Machine Learning In this work, we develop a machine learning (ML) strategy to map molecular structure to condensed-phase charge transfer (CT) properties including CT rate constants, energy levels, electronic couplings, energy gaps, reorganization energies, and reaction free energies, which are called CT fingerprints. The CT fingerprints of selected landmark structures covering the conformation space of an organic photovoltaic molecule dissolved in explicit solvent are computed and used to train ML models using kernel ridge regression. The ML models show high predictive power with R2&gt;0.97, and both mean absolute error and root mean square error within chemical accuracy. The CT landscape for millions of molecular dynamics sampled structures is thus constructed, which allows for instant prediction of CT rate properties given any molecular structure. The unprecedented CT landscape will shed light on real-time CT dynamics in nanoscale and mesoscale condensed-phase systems, and the optimal fabrication design for homogeneous and heterogeneous optoelectronic devices. Energy landscape ; Phase (matter) ; Molecular dynamics ; Charge (physics) ; Chemical physics ; Nanoscopic scale ; Molecule ; Mean squared error ; Materials science ; Statistical physics ; Computer science ; Physics ; Nanotechnology ; Thermodynamics ; Quantum mechanics ; Mathematics Machine Learning in Materials Science ; Perovskite Materials and Applications ; Spectroscopy and Quantum Chemical Studies
Authors: Jon Eunan Quinlivan Domínguez | Konstantin M. Neyman | Albert Bruix
Source:  (preprint, 2021-10-19)
Field: Materials Science — Materials Chemistry

Stability of oxidized states of ceria-supported PtOx particles under a wide range of gas-phase conditions Nanostructured materials based on non-inert oxides CeO2 and PtyOx play a fundamental role in catalyst design. However, their characterization is often challenging due to their structural complexity and the tendency of the materials to change under reaction conditions. In this work, we combine calculations based on the density functional theory, a machine-learning assisted global optimization method (GOFEE) and ab initio thermodynamics to characterize stable oxidation states of ceria-supported PtyOx clusters in different environments. The collection of global minima for different stoichiometries resulting from the global optimisation effort is used to assess the effect of temperature, oxygen pressure, and support interactions on the phase diagrams, oxidation states, and structural properties of the PtyOx particles. We thus identify favoured structural motifs and O/Pt ratios, revealing that oxidized states of ceria-supported particles are more stable than reduced ones under a wide range of conditions. These results indicate that studies rationalizing activity of ceria-supported Pt clusters must consider such oxidized states, and that previous understanding of such materials obtained only with fully reduced Pt clusters may be incomplete. Stoichiometry ; Materials science ; Density functional theory ; Catalysis ; Ab initio ; Work (physics) ; Maxima and minima ; Chemical physics ; Thermodynamics ; Nanotechnology ; Computational chemistry ; Chemistry ; Physical chemistry ; Physics ; Mathematics Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Electrocatalysts for Energy Conversion
Authors: Andrew E. Sifain | Levi Lystrom | Richard A. Messerly | Justin S. Smith | Benjamin Nebgen | Kipton Barros | Sergei Tretiak | Nicholas Lubbers | Brendan J. Gifford
Source: ChemRxiv (preprint, 2021-04-28)
Field: Materials Science — Materials Chemistry

Predicting Phosphorescence Energies and Inferring Wavefunction Localization with Machine Learning Phosphorescence is commonly utilized for applications including light-emitting diodes and photovoltaics. Machine learning (ML) approaches trained on ab-initio datasets of singlet-triplet energy gaps may expedite the discovery of phosphorescent compounds with the desired emission energies. However, we show that standard ML approaches for modeling potential energy surfaces inaccurately predict singlet-triplet energy gaps due to the failure to account for spatial localities of spin transitions. To solve this, we introduce localization layers in a neural network model that weight atomic contributions to the energy gap, thereby allowing the model to isolate the most determinative chemical environments. Trained on the singlet-triplet energy gaps of organic molecules, we apply our method to an out-of-sample test set of large phosphorescent compounds and demonstrate the substantial improvement that localization layers have on predicting their phosphorescence energies. Remarkably, the inferred localization weights have a strong relationship with the ab-initio spin density of the singlet-triplet transition, and thus infer localities of the molecule that determine the spin transition, despite the fact that no direct electronic information was provided during training. The use of localization layers is expected to improve the modeling of many localized, non-extensive phenomena and could be implemented in any atom-centered neural network model. Phosphorescence ; Singlet state ; Ab initio ; Artificial neural network ; Spin (aerodynamics) ; Physics ; Chemistry ; Chemical physics ; Computer science ; Materials science ; Atomic physics ; Machine learning ; Excited state ; Quantum mechanics ; Thermodynamics ; Fluorescence Machine Learning in Materials Science ; bioluminescence and chemiluminescence research ; Analytical Chemistry and Sensors
Authors: Tanusri Saha‐Dasgupta
Source: Bulletin of Materials Science (article, 2021-10-23)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Understanding and prediction of quantum materials via modelling and computation Computation ; Materials science ; Quantum ; Quantum computer ; Magnet ; Spin (aerodynamics) ; Property (philosophy) ; Superconductivity ; Crossover ; Statistical physics ; Computer science ; Nanotechnology ; Condensed matter physics ; Artificial intelligence ; Quantum mechanics ; Physics ; Thermodynamics ; Algorithm Magnetic and transport properties of perovskites and related materials ; Advanced Condensed Matter Physics ; Machine Learning in Materials Science
Authors: Gracie Chaney | Akram Ibrahim | Fatih Ersan | Deniz Çakır | Can Ataca
Source: arXiv (Cornell University) (preprint, 2021-07-21)
Field: Materials Science — Materials Chemistry

Comprehensive Study of Lithium Adsorptionand Diffusion on Janus Mo/WXY (X, Y= S,Se, Te) using First Principles and MachineLearning Approaches The structural asymmetry of two-dimensional (2D) Janus transition metal dichalcogenides (TMDs) produces internal dipole moments that result in interesting electronic properties. These properties differ from the regular (symmetric) TMD structures that the Janus structures are derived from. In this study, we, at first, examine adsorption and diffusion of a single Li atom on regular MX2and Janus MXY (M = Mo, W; XY =S, Se, Te) TMD structures at various concentrations using first principles calculations within density functional theory. To gain more physical insight and prepare for future investigations of regular TMD and Janus materials, we applied a supervised machine learning (ML) model that uses cluster-wise linear regression to predict the adsorption energies of Li on top of 2D TMDs. We developed a universal representation with few descriptors that take into account the intrinsic dipole moment and the electronic structure of regular and Janus 2D layers, the side where the adsorption takes place and the concentration dependence of adatom doping. This representation can easily be generalized to be used for other impurities and 2D layer combinations, including alloys as well. At last, we focus on analyzing these structures as possible anodes in battery applications. We conducted Li diffusion, open-circuit-voltage and storage capacity simulations. We report that Lithium atoms are found to easily migrate between transition metal (Mo, W) top sites for each considered case, and in these respects many of the examined Janus materials are comparable or superior to graphene and to regular TMDs. The results imply that theexamined Janus structures should perform well as electrodes in Li-ion batteries. Janus ; Dipole ; Lithium (medication) ; Materials science ; Diffusion ; Representation (politics) ; Electronic structure ; Transition metal ; Atom (system on chip) ; Graphene ; Chemical physics ; Condensed matter physics ; Nanotechnology ; Chemistry ; Physics ; Thermodynamics ; Computer science ; Quantum mechanics 2D Materials and Applications ; Machine Learning in Materials Science ; Advanced Photocatalysis Techniques
Authors: Ritaban Dutta | Cherry Chen | D. Renshaw | Daniel Liang
Source: Research Square (preprint, 2021-12-02)
Field: Materials Science — Materials Chemistry

XGBoost automates the characterisation of reversibly actuating planar-flow-casted NiTi shape memory alloy foil Abstract Nickel-Titanium (NiTi) shape memory alloys (SMAs) are smart materials able to recover their original shape under thermal stimulus. Near-net-shape NiTi SMA foils of 2 meters in length and width of 30 mm have been successfully produced by a planar flow casting facility at CSIRO, opening possibilities of wider applications of SMA foils. The study also focuses on establishing a fully automated experimental system for the characterisation of their reversible actuation, significantly improving SMA foils adaptation into real applications. Artificial Intelligence involving Computer Vision and Machine Learning based methods were successfully employed in the development of the automation SMA characterisation process. The study finds that an Extreme Gradient Boosting (XGBoost) Regression model based predictive system experimented with over 175,000 video samples could achieve 99% overall prediction accuracy. Generalisation capability of the proposed system makes a significant contribution towards the efficient optimisation of the material design to produce high quality 30 mm SMA foils. FOIL method ; Nickel titanium ; Planar ; Shape-memory alloy ; Materials science ; Alloy ; Flow (mathematics) ; Metallurgy ; Composite material ; Computer science ; Mechanics ; Computer graphics (images) ; Physics Shape Memory Alloy Transformations ; Advanced Memory and Neural Computing
Authors: Martin P. Bircher | Andreas Singraber | Christoph Dellago
Source: Machine Learning Science and Technology (preprint, 2021-04-15)
Field: Materials Science — Materials Chemistry

Improved description of atomic environments using low-cost polynomial functions with compact support Abstract The prediction of chemical properties using machine learning techniques calls for a set of appropriate descriptors that accurately describe atomic and, on a larger scale, molecular environments. A mapping of conformational information on a space spanned by atom-centred symmetry functions (SF) has become a standard technique for energy and force predictions using high-dimensional neural network potentials (HDNNP). An appropriate choice of SFs is particularly crucial for accurate force predictions. Established atom-centred SFs, however, are limited in their flexibility, since their functional form restricts the angular domain that can be sampled without introducing problematic derivative discontinuities. Here, we introduce a class of atom-centred SFs based on polynomials with compact support called polynomial symmetry functions (PSF), which enable a free choice of both, the angular and the radial domain covered. We demonstrate that the accuracy of PSFs is either on par or considerably better than that of conventional, atom-centred SFs. In particular, a generic set of PSFs with an intuitive choice of the angular domain inspired by organic chemistry considerably improves prediction accuracy for organic molecules in the gaseous and liquid phase, with reductions in force prediction errors over a test set approaching 50% for certain systems. Contrary to established atom-centred SFs, computation of PSF does not involve any exponentials, and their intrinsic compact support supersedes use of separate cutoff functions, facilitating the choice of their free parameters. Most importantly, the number of floating point operations required to compute polynomial SFs introduced here is considerably lower than that of other state-of-the-art SFs, enabling their efficient implementation without the need of highly optimised code structures or caching, with speedups with respect to other state-of-the-art SFs reaching a factor of 4.5 to 5. This low-effort performance benefit substantially simplifies their use in new programs and emerging platforms such as graphical processing units. Overall, polynomial SFs with compact support improve accuracy of both, energy and force predictions with HDNNPs while enabling significant speedups compared to their well-established counterparts. Polynomial ; Atom (system on chip) ; Computer science ; Function (biology) ; Symmetry (geometry) ; Flexibility (engineering) ; Set (abstract data type) ; Domain (mathematical analysis) ; Algorithm ; Topology (electrical circuits) ; Mathematics ; Geometry ; Mathematical analysis ; Parallel computing Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Jingbai Li | Steven A. Lopez
Source: ChemRxiv (preprint, 2021-12-03)
Field: Chemistry — Organic Chemistry

Excited-state distortions control the reactivities and regioselectivities of photochemical 4π-electrocyclizations of fluorobenzenes The photochemistry of benzene is complex and non-selective because numerous mechanistic pathways are accessible in the ground- and excited-states. Fluorination is a known strategy to increase the chemoselectivities for Dewar-benzenes via 4π-disrotatory electrocyclization. However, the origin of the chemo- and regioselectivities of fluorobenzenes remains unexplained because of experimental limitations in resolving the excited-state structures on ultrafast timescales. The computational cost of multiconfigurational nonadiabatic molecular dynamics simulations is also generally prohibitive. We now provide high-fidelity structural information and reaction outcome predictions with machine-learning-accelerated photodynamics simulations of a series of fluorobenzenes, C6F6-nHn, n=0–3 to study their S1→S0 decay in 4 ns. We trained neural networks with XMS-CASPT2(6,7)/aug-cc-pVDZ calculations, which reproduced the S1 absorption features with mean absolute errors of 0.04 eV (< 2 nm). The predicted S1 excited-state lifetimes for C6F4H2, C6F6, C6F5H, and C6F3H3 are 64, 40, 18, and 8 ps, respectively. The trend is in excellent agreement with the experimental lifetimes. Our calculations show that the pseudo Jahn-Teller distortions create the S1 minimum region that prolongs the excited-state lifetime of fluorobenzenes. The pseudo Jahn-Teller distortions reduce when fluorination decreases. Characterization of the surface hopping structures suggests that the S1 relaxation first involves a cis-trans isomerization of a 𝜋C-C-bond in the benzene ring, promoted by the pseudo-Jahn-Teller distortions. A branching plane analysis revealed that the conical intersections favoring 4π-electrocyclization are less energetically accessible through the S1 relaxation; lower-energy conical intersections resemble the reactant and favor reversion. Excited state ; Surface hopping ; Chemistry ; Photoexcitation ; Photochemistry ; Relaxation (psychology) ; Isomerization ; Computational chemistry ; Molecular dynamics ; Molecular physics ; Chemical physics ; Atomic physics ; Physics Radical Photochemical Reactions ; Fluorine in Organic Chemistry ; Oxidative Organic Chemistry Reactions
Authors: Negin Sohrabi | Réza Mohammadi | Hamid Reza Ghassemzadeh | Seyed Sadegh Seyedloo Heris
Source: SSRN Electronic Journal (article, 2021-01-01)
Field: Chemistry — Analytical Chemistry

Design and Synthesis of a New Magnetic Molecularly Imprinted Polymer Nanocomposite for Specific Adsorption and Simultaneous Determination of Diazinon Insecticides from Aqueous Media Adsorption ; Response surface methodology ; Diazinon ; Materials science ; Molecularly imprinted polymer ; Polymer ; Chemical engineering ; Aqueous solution ; Nanocomposite ; Central composite design ; Nuclear chemistry ; Chromatography ; Composite material ; Chemistry ; Organic chemistry ; Pesticide ; Selectivity Analytical chemistry methods development ; Pesticide Residue Analysis and Safety ; Electrochemical sensors and biosensors
Authors: Mojtaba Haghighatlari | Jie Li | Xingyi Guan | Oufan Zhang | Akshaya Kumar Das | Christopher J. Stein | Farnaz Heidar‐Zadeh | Meili Liu | Martin Head‐Gordon | Luke W. Bertels | Hongxia Hao | Itai Leven | Teresa Head‐Gordon
Source: arXiv (Cornell University) (preprint, 2021-08-06)
Field: Materials Science — Materials Chemistry

NewtonNet: A Newtonian message passing network for deep learning of interatomic potentials and forces We report a new deep learning message passing network that takes inspiration from Newton's equations of motion to learn interatomic potentials and forces. With the advantage of directional information from trainable latent force vectors, and physics-infused operators that are inspired by the Newtonian physics, the entire model remains rotationally equivariant, and many-body interactions are inferred by more interpretable physical features. We test NewtonNet on the prediction of several reactive and non-reactive high quality ab initio data sets including single small molecule dynamics, a large set of chemically diverse molecules, and methane and hydrogen combustion reactions, achieving state-of-the-art test performance on energies and forces with far greater data and computational efficiency than other deep learning models. Newtonian fluid ; Motion (physics) ; Ab initio ; Computer science ; Statistical physics ; Set (abstract data type) ; Classical mechanics ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Yu Qi Zheng | Kaiyue Zheng | Ma Luyao | Zhe Liu
Source: Matter (article, 2021-08-01)
Field: Materials Science — Materials Chemistry

Robots are taking up the challenges in photovoltaics R&amp;D Photovoltaics ; Renewable energy ; Efficient energy use ; Throughput ; Computer science ; Robot ; Organic solar cell ; Duration (music) ; Nanotechnology ; Artificial intelligence ; Simulation ; Engineering ; Electrical engineering ; Telecommunications ; Materials science ; Photovoltaic system ; Physics ; Wireless Machine Learning in Materials Science ; Economic and Technological Innovation ; Chalcogenide Semiconductor Thin Films
Authors: Omri Abarbanel | Geoffrey Hutchison
Source: ChemRxiv (preprint, 2021-07-05)
Field: Materials Science — Materials Chemistry

Machine Learning to Accelerate Screening for Marcus Reorganization Energies Understanding and predicting the charge transport properties of π-conjugated materials is an important challenge for designing new organic electronic applications, including solar cells, plastic transistors, light-emitting devices, and chemical sensors. A key component of the hopping mechanism of charge transfer in these materials is the Marcus reorganization energy, which serves as an activation barrier to hole or electron transfer. While modern density functional methods have proven to accurately predict trends in intramolecular reorganization energy, such calculations are computationally expensive. In this work, we outline active machine learning methods to predict computed intramolecular reorganization energies of a wide range of polythiophenes and their use towards screening new compounds with low internal reorganization energies. Our models have an overall root mean square error of ±0.113 eV, but a much smaller RMSE of only ±0.036 eV on the new screening set. Since the larger error derives from high-reorganization energy compounds, the new method is highly effective to screen for compounds with potentially efficient charge transport parameters. Intramolecular force ; Charge (physics) ; Marcus theory ; Work (physics) ; Density functional theory ; Computer science ; Transistor ; Range (aeronautics) ; Materials science ; Chemical physics ; Chemistry ; Physics ; Computational chemistry ; Voltage ; Thermodynamics ; Quantum mechanics Machine Learning in Materials Science ; Molecular Junctions and Nanostructures ; Conducting polymers and applications