jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Saientan Bag | Melissa K. Meinel | Florian Müller‐Plathe
Source: Journal of Chemical Theory and Computation (article, 2022-11-30)
Field: Materials Science — Materials Chemistry

Toward a Mobility-Preserving Coarse-Grained Model: A Data-Driven Approach Coarse-grained molecular dynamics (MD) simulation is a promising alternative to all-atom MD simulation for the fast calculation of system properties, which is imperative in designing materials with a specific target property. There have been several coarse-graining strategies developed over the past few years that provide accurate structural properties of the system. However, these coarse-grained models share a major drawback in that they introduce an artificial acceleration in molecular mobility. In this paper, we report a data-driven approach to generate coarse-grained models that preserve the all-atom molecular mobility. We designed a machine learning model in the form of an artificial neural network, which directly predicts the simulation-ready mobility-preserving coarse-grained potential as an output given the all-atom force field (FF) parameters as inputs. As a proof of principle, we took 2,3,4-trimethylpentane as a model system and described the development of machine learning models in detail. We quantify the artificial acceleration in molecular mobility by defining the acceleration factor as the ratio of the coarse-grained and the all-atom diffusion coefficient. The predicted coarse-grained potential generated by the best machine learning model can bring down the acceleration factor to a value of ∼2, which could be otherwise as large as 7 for a typical value of 3 × 10<sup>-9</sup> m<sup>2</sup> s<sup>-1</sup> for the all-atom diffusion coefficient. We believe our method will be of interest in the community as a route to generating coarse-grained potentials with accurate dynamics. Computer science ; Acceleration ; Molecular dynamics ; Granularity ; Diffusion ; Atom (system on chip) ; Force field (fiction) ; Artificial neural network ; Value (mathematics) ; Field (mathematics) ; Algorithm ; Artificial intelligence ; Statistical physics ; Simulation ; Machine learning ; Physics ; Computational chemistry ; Mathematics ; Chemistry ; Parallel computing ; Thermodynamics Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Fuel Cells and Related Materials
Authors: Andrea Thorn
Source: Current Opinion in Structural Biology (review, 2022-04-15)
Field: Materials Science — Materials Chemistry

Artificial intelligence in the experimental determination and prediction of macromolecular structures Machine learning methods, in particular convolutional neural networks, have been applied to a variety of problems in cryo-EM and macromolecular crystallographic structure solution. However, they still have only limited acceptance by the community, mainly in areas where they replace repetitive work and allow for easy visual checking, such as particle picking, crystal centering or crystal recognition. With Artificial Intelligence (AI) based protein fold prediction currently revolutionizing the field, it is clear that their scope could be much wider. However, whether we will be able to exploit this potential fully will depend on the manner in which we use machine learning: training data must be well-formulated, methods need to utilize appropriate architectures, and outputs must be critically assessed, which may even require explaining AI decisions. Exploit ; Computer science ; Artificial intelligence ; Convolutional neural network ; Scope (computer science) ; Machine learning ; Field (mathematics) ; Variety (cybernetics) ; Artificial neural network ; Deep learning ; Computer security Enzyme Structure and Function ; Machine Learning in Materials Science ; Protein Structure and Dynamics
Authors: Veronika Jurásková | Frédéric Célerse | Rubén Laplaza | Clémence Corminbœuf
Source: The Journal of Chemical Physics (article, 2022-03-29)
Field: Materials Science — Materials Chemistry

Assessing the persistence of chalcogen bonds in solution with neural network potentials Non-covalent bonding patterns are commonly harvested as a design principle in the field of catalysis, supramolecular chemistry, and functional materials to name a few. Yet, their computational description generally neglects finite temperature and environment effects, which promote competing interactions and alter their static gas-phase properties. Recently, neural network potentials (NNPs) trained on density functional theory (DFT) data have become increasingly popular to simulate molecular phenomena in condensed phase with an accuracy comparable to ab initio methods. To date, most applications have centered on solid-state materials or fairly simple molecules made of a limited number of elements. Herein, we focus on the persistence and strength of chalcogen bonds involving a benzotelluradiazole in condensed phase. While the tellurium-containing heteroaromatic molecules are known to exhibit pronounced interactions with anions and lone pairs of different atoms, the relevance of competing intermolecular interactions, notably with the solvent, is complicated to monitor experimentally but also challenging to model at an accurate electronic structure level. Here, we train direct and baselined NNPs to reproduce hybrid DFT energies and forces in order to identify what the most prevalent non-covalent interactions occurring in a solute-Cl<sup>-</sup>-THF mixture are. The simulations in explicit solvent highlight the clear competition with chalcogen bonds formed with the solvent and the short-range directionality of the interaction with direct consequences for the molecular properties in the solution. The comparison with other potentials (e.g., AMOEBA, direct NNP, and continuum solvent model) also demonstrates that baselined NNPs offer a reliable picture of the non-covalent interaction interplay occurring in solution. Chalcogen ; Chemical physics ; Intermolecular force ; Non-covalent interactions ; Supramolecular chemistry ; Chemistry ; Computational chemistry ; Lone pair ; Covalent bond ; Density functional theory ; Molecule ; Ab initio ; Hydrogen bond ; Crystallography ; Organic chemistry Machine Learning in Materials Science ; Crystallography and molecular interactions ; Computational Drug Discovery Methods
Authors: Federico Zipoli | Victor Viterbo | Oliver Schilter | Leonid Kahle | Teodoro Laino
Source: Industrial & Engineering Chemistry Research (article, 2022-05-23)
Field: Materials Science — Materials Chemistry

Prediction of Phase Diagrams and Associated Phase Structural Properties The knowledge of phase diagrams is crucial to understand materials and to design new ones with better properties. However, elucidating phase diagrams is a difficult task, both experimentally and theoretically. In this work, we address the problem of predicting phase diagrams and crystal structures using a data-driven approach. We used machine learning methods to predict the stable phases using composition, temperature, and pressure extracted from a set of diagrams contained in the NIST database. We used the extracted data to train machine learning algorithms to predict the stable phases and the chemical formula of the stable compounds, including structural features such as Bravais lattices, crystal types, and the local atomic environments of the inorganic compounds contained in the ICSD database. The computationally efficient data-driven methods presented in this paper will aid material scientists in estimating the structure of virtually any mixture of elements in any proportion over a wide temperature range. NIST ; Phase diagram ; Computer science ; Phase (matter) ; Experimental data ; Set (abstract data type) ; Task (project management) ; Bravais lattice ; Range (aeronautics) ; Algorithm ; Crystal structure ; Biological system ; Data mining ; Artificial intelligence ; Machine learning ; Materials science ; Chemistry ; Crystallography ; Mathematics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Catalysis and Oxidation Reactions
Authors: Zong-Rong Ye | Sheng-Hsuan Hung | Berlin Chen | Ming‐Kang Tsai
Source: ACS Engineering Au (article, 2022-04-22)
Field: Materials Science — Materials Chemistry

Assessment of Predicting Frontier Orbital Energies for Small Organic Molecules Using Knowledge-Based and Structural Information A systematic comparison is demonstrated for the predictions of frontier orbital energies─highest occupied molecular orbital (HOMO) (EH), lowest unoccupied molecular orbital (LUMO) (EL), and energy gap (ΔEHL) of the molecules in the QM9 dataset, where it contains 120k-plus three-dimensional organic molecule structures determined by first-principles simulations. The target molecular properties (EH, EL, and ΔEHL) are predicted using linear regression (LR), machine learning (random forest, RF), and continuous-filter convolutional neural network (SchNET) approaches. LR and RF models built upon various knowledge-based descriptors, being derived from SMILES of the molecules, can provide predictivity of the target properties with the mean absolute errors (MAEs) 4–6 times the chemical accuracy (0.043 eV). The best approach, SchNET, using the graph representation derived from molecular Cartesian coordinates, is confirmed to provide MAEs of EH, EL, and ΔEHL at 0.051, 0.041, and 0.076 eV, respectively. With the introduction of bond-step matrix representation with the SchNET model, the computational cost of dataset preparation can be substantially reduced, and the corresponding MAEs increase moderately to 2–3 times the chemical accuracy. The chemical interpretation of the important descriptors identified in the LR and RF models appears to align with the chemical knowledge of describing these molecular electronic properties but is accompanied with tolerable prediction errors. The combination of bond-step representation and the SchNET model can provide an assessable and balanced option for the high-throughput screening of organic molecules and the development of the data science approach. HOMO/LUMO ; Molecular descriptor ; Molecular orbital ; Representation (politics) ; Molecule ; Computer science ; Molecular graph ; Cartesian coordinate system ; Convolutional neural network ; Artificial intelligence ; Chemistry ; Graph ; Computational chemistry ; Biological system ; Machine learning ; Physics ; Mathematics ; Quantitative structure–activity relationship ; Theoretical computer science ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Cristian Gabellini | Maria Şologan | Elena Pellizzoni | Domenico Marson | Mario Daka | Paola Franchi | Luca Bignardi | Stefano Franchi | Zbyšek Posel | Alessandro Baraldi | Paolo Pengo | Marco Lucarini | Lucia Pasquato | Paola Posocco
Source: ACS Nano (article, 2022-12-02)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Spotting Local Environments in Self-Assembled Monolayer-Protected Gold Nanoparticles Organic-inorganic (O-I) nanomaterials are versatile platforms for an incredible high number of applications, ranging from heterogeneous catalysis to molecular sensing, cell targeting, imaging, and cancer diagnosis and therapy, just to name a few. Much of their potential stems from the unique control of organic environments around inorganic sites within a single O-I nanomaterial, which allows for new properties that were inaccessible using purely organic or inorganic materials. Structural and mechanistic characterization plays a key role in understanding and rationally designing such hybrid nanoconstructs. Here, we introduce a general methodology to identify and classify local (supra)molecular environments in an archetypal class of O-I nanomaterials, i.e., self-assembled monolayer-protected gold nanoparticles (SAM-AuNPs). By using an atomistic machine-learning guided workflow based on the Smooth Overlap of Atomic Positions (SOAP) descriptor, we analyze a collection of chemically different SAM-AuNPs and detect and compare local environments in a way that is agnostic and automated, i.e., with no need of <i>a priori</i> information and minimal user intervention. In addition, the computational results coupled with experimental electron spin resonance measurements prove that is possible to have more than one local environment inside SAMs, being the thickness of the organic shell and solvation primary factors in the determining number and nature of multiple coexisting environments. These indications are extended to complex mixed hydrophilic-hydrophobic SAMs. This work demonstrates that it is possible to spot and compare local molecular environments in SAM-AuNPs exploiting atomistic machine-learning approaches, establishes ground rules to control them, and holds the potential for the rational design of O-I nanomaterials instructed from data. Monolayer ; Colloidal gold ; Nanotechnology ; Materials science ; Self-assembled monolayer ; Nanoparticle ; Spotting ; Computer science ; Artificial intelligence Gold and Silver Nanoparticles Synthesis and Applications ; Machine Learning in Materials Science ; nanoparticles nucleation surface interactions
Authors: Jumpei Kurokawa | Hiroki Kondo | Takayoshi Tsutsumi | Kenji Ishikawa | Makoto Sekine | Masaru Hori
Source: Vacuum (article, 2022-08-02)
Field: Materials Science — Materials Chemistry

Effects of deposition precursors of hydrogenated amorphous carbon films on the plasma etching resistance based on mass spectrometer measurements and machine learning analysis Radical ; Plasma-enhanced chemical vapor deposition ; Chemistry ; Quadrupole mass analyzer ; Ellipsometry ; Analytical Chemistry (journal) ; Chemical vapor deposition ; Amorphous solid ; Reactive-ion etching ; Mass spectrometry ; Amorphous carbon ; Etching (microfabrication) ; Thin film ; Materials science ; Nanotechnology ; Organic chemistry ; Chromatography Diamond and Carbon-based Materials Research ; Ion-surface interactions and analysis ; Mass Spectrometry Techniques and Applications
Authors: Rong Chen | Fu Liu | Yuchao Tang | Yanjie Liu | Ziqiang Dong | Zhen‐Yan Deng | Xinluo Zhao | Yi Liu
Source: Applied Surface Science (article, 2022-02-12)
Field: Materials Science — Materials Chemistry

Combined first-principles and machine learning study of the initial growth of carbon nanomaterials on metal surfaces Electronegativity ; Adsorption ; Catalysis ; Hydrocarbon ; Nanomaterials ; Acetylene ; Benzene ; Materials science ; Transition metal ; Molecule ; Valence (chemistry) ; Alloy ; Metal ; Physical chemistry ; Carbonization ; Chemical engineering ; Carbon fibers ; Binding energy ; Chemistry ; Nanotechnology ; Organic chemistry ; Metallurgy ; Composite material ; Atomic physics Carbon Nanotubes in Composites ; Machine Learning in Materials Science ; Graphene research and applications
Authors: J. Thorben Frank | Oliver T. Unke | Klaus‐Robert Müller
Source: arXiv (Cornell University) (preprint, 2022-05-28)
Field: Materials Science — Materials Chemistry

So3krates: Equivariant attention for interactions on arbitrary length-scales in molecular systems The application of machine learning methods in quantum chemistry has enabled the study of numerous chemical phenomena, which are computationally intractable with traditional ab-initio methods. However, some quantum mechanical properties of molecules and materials depend on non-local electronic effects, which are often neglected due to the difficulty of modeling them efficiently. This work proposes a modified attention mechanism adapted to the underlying physics, which allows to recover the relevant non-local effects. Namely, we introduce spherical harmonic coordinates (SPHCs) to reflect higher-order geometric information for each atom in a molecule, enabling a non-local formulation of attention in the SPHC space. Our proposed model So3krates - a self-attention based message passing neural network - uncouples geometric information from atomic features, making them independently amenable to attention mechanisms. Thereby we construct spherical filters, which extend the concept of continuous filters in Euclidean space to SPHC space and serve as foundation for a spherical self-attention mechanism. We show that in contrast to other published methods, So3krates is able to describe non-local quantum mechanical effects over arbitrary length scales. Further, we find evidence that the inclusion of higher-order geometric correlations increases data efficiency and improves generalization. So3krates matches or exceeds state-of-the-art performance on popular benchmarks, notably, requiring a significantly lower number of parameters (0.25 - 0.4x) while at the same time giving a substantial speedup (6 - 14x for training and 2 - 11x for inference) compared to other models. Speedup ; Generalization ; Computer science ; Quantum ; Euclidean geometry ; Artificial neural network ; Theoretical computer science ; Algorithm ; Statistical physics ; Artificial intelligence ; Mathematics ; Physics ; Quantum mechanics ; Parallel computing ; Geometry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Robin Gaumard | Dominik Dragún | Jesús N. Pedroza‐Montero | Bruno Alonso | Hazar Guesmi | Irina Malkin Ondík | Tzonka Mineva
Source: Computation (article, 2022-05-13)
Field: Chemistry — Spectroscopy

Regression Machine Learning Models Used to Predict DFT-Computed NMR Parameters of Zeolites Machine learning approaches can drastically decrease the computational time for the predictions of spectroscopic properties in materials, while preserving the quality of the computational approaches. We studied the performance of kernel-ridge regression (KRR) and gradient boosting regressor (GBR) models trained on the isotropic shielding values, computed with density-functional theory (DFT), in a series of different known zeolites containing out-of-frame metal cations or fluorine anion and organic structure-directing cations. The smooth overlap of atomic position descriptors were computed from the DFT-optimised Cartesian coordinates of each atoms in the zeolite crystal cells. The use of these descriptors as inputs in both machine learning regression methods led to the prediction of the DFT isotropic shielding values with mean errors within 0.6 ppm. The results showed that the GBR model scales better than the KRR model. Density functional theory ; Isotropy ; Artificial intelligence ; Zeolite ; Machine learning ; Electromagnetic shielding ; Chemistry ; Mathematics ; Algorithm ; Computer science ; Computational chemistry ; Physics ; Quantum mechanics Advanced NMR Techniques and Applications ; Machine Learning in Materials Science ; Solid-state spectroscopy and crystallography
Authors: Hongwei Niu | Junqing Zhao | Huyang Li | Yi Sun | Jae Hyun Park | Yuhang Jing | Weiqi Li | Jianqun Yang | Xingji Li
Source: Computational Materials Science (article, 2022-12-14)
Field: Materials Science — Materials Chemistry

A machine-learning interatomic potential to understand primary radiation damage of silicon Radiation damage ; Excited state ; Silicon ; Semiconductor ; Radiation ; Materials science ; Molecular dynamics ; Displacement (psychology) ; Atomic physics ; Interatomic potential ; Irradiation ; Chemical physics ; Molecular physics ; Chemistry ; Optoelectronics ; Computational chemistry ; Physics ; Optics ; Nuclear physics Machine Learning in Materials Science ; Silicon and Solar Cell Technologies ; Thin-Film Transistor Technologies
Authors: Binggang Xiao | Yichun Wang | Wanjun Cai | Lihua Xiao
Source: Optics Express (article, 2022-04-12)
Field: Materials Science — Materials Chemistry

Design and prediction of PIT devices through deep learning Graphene material has excellent performance and unique variable carrier density characteristics, making it an excellent mid-infrared material. And deep learning makes it possible to quickly design mid-infrared band devices with good performance. A graphene nano-ring-symmetric sector-shaped disk array structure based on the PIT principle is proposed here for sensing. The influence of structural parameters and Fermi energy changes are studied. And its FOM (Figure Of Merit) can reach 28.7; the sensitivity is 574 cm<sup>-1</sup> / RIU (Refractive Index Unit). At the same time, we designed a six-layer deep learning network that can predict structural parameters and curve predictions. When predicting structural parameters, its MAPE (Mean Absolute Percentage Error) converges to 0.5. In curve prediction, MSE (Mean Square Error) converges to 1.2. It shows that predictions can be made very well. This paper proposes a symmetrical sector disk array structure and a 6-layer deep learning network. And the deep neural network designed based on the device data has good prediction accuracy under the premise of ensuring the network is simple. This will lay a good foundation for future sensor design and device acceleration optimization design. Deep learning ; Artificial neural network ; Sensitivity (control systems) ; Mean absolute percentage error ; Energy (signal processing) ; Mean squared error ; Curve fitting ; Acceleration 2D Materials and Applications ; Graphene research and applications ; Thermal properties of materials
Authors: Richa Dubey | Velmathi Guruviah
Source: Ionics (article, 2022-02-16)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A data-driven approach for evaluation of electrolyte informatics on electrochemical performance of carbon-based electrode materials Electrolyte ; Electrochemistry ; Materials science ; Supercapacitor ; Mean squared error ; Electrode ; Work (physics) ; Energy storage ; Ion ; Chemistry ; Thermodynamics ; Statistics ; Mathematics Supercapacitor Materials and Fabrication ; Conducting polymers and applications ; Electrochemical sensors and biosensors
Authors: Qin Wu | Talin Avanesian | Xiaohui Qu | Hubertus J. J. van Dam
Source: The Journal of Chemical Physics (article, 2022-09-23)
Field: Materials Science — Materials Chemistry

PolyODENet: Deriving mass-action rate equations from incomplete transient kinetics data Kinetics of a reaction network that follows mass-action rate laws can be described with a system of ordinary differential equations (ODEs) with polynomial right-hand side. However, it is challenging to derive such kinetic differential equations from transient kinetic data without knowing the reaction network, especially when the data are incomplete due to experimental limitations. We introduce a program, PolyODENet, toward this goal. Based on the machine-learning method Neural ODE, PolyODENet defines a generative model and predicts concentrations at arbitrary time. As such, it is possible to include unmeasurable intermediate species in the kinetic equations. Importantly, we have implemented various measures to apply physical constraints and chemical knowledge in the training to regularize the solution space. Using simple catalytic reaction models, we demonstrate that PolyODENet can predict reaction profiles of unknown species and doing so even reveal hidden parts of reaction mechanisms. Ode ; Ordinary differential equation ; Transient (computer programming) ; Action (physics) ; Artificial neural network ; Mass action law ; Kinetic energy ; Simple (philosophy) ; Rate equation ; Applied mathematics ; Computer science ; Mathematics ; Differential equation ; Kinetics ; Physics ; Thermodynamics ; Mathematical analysis ; Artificial intelligence ; Classical mechanics Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Protein Structure and Dynamics
Authors: Hao Wang | Xiao-Long Pan | Yufeng Wang | Xiang-Rong Chen | Yixian Wang | Hua-Yun Geng
Source: Journal of Nuclear Materials (article, 2022-09-11)
Field: Materials Science — Materials Chemistry

Lattice dynamics and elastic properties of α-U at high-temperature and high-pressure by machine learning potential simulations Phonon ; Anharmonicity ; Elastic modulus ; Materials science ; Condensed matter physics ; Crystallite ; Anisotropy ; Softening ; Molecular dynamics ; Lattice (music) ; Extrapolation ; Force field (fiction) ; Chemistry ; Physics ; Composite material ; Computational chemistry ; Optics Nuclear Materials and Properties ; Nuclear Physics and Applications ; Machine Learning in Materials Science