jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Amol Thakkar | Alain C. Vaucher | Andrea Byekwaso | Philippe Schwaller | Alessandra Toniato | Teodoro Laino
Source: ChemRxiv (preprint, 2022-09-20)
Field: Materials Science — Materials Chemistry

Unbiasing Retrosynthesis Language Models with Disconnection Prompts Data-driven approaches to retrosynthesis have thus far been limited in user interaction, in the diversity of their predictions, and the recommendation of unintuitive disconnection strategies. Herein, we extend the notions of prompt- based inference in natural language processing to the task of chemical language modeling. We show that by using a prompt describing the disconnection site in a molecule, we can steer the model to propose a wider set of precursors, overcoming training data biases in retrosynthetic recommendations and achiev- ing a 39 % performance improvement over the baseline. For the first time, the use of a disconnection prompt empowers chemists by giving them back greater control over the disconnection predictions, resulting in more diverse and creative recommendations. In addition, in place of a human-in-the-loop strategy, we propose a schema for automatic identification of disconnection sites, followed by prediction of reactant sets, achieving a 100 % improvement in class diversity as compared to the baseline. The approach is effective in mitigating prediction biases deriving from training data. In turn, this provides a larger variety of usable building blocks, which improves the end-user digital experience. We demonstrate its application to different chemistry domains, from traditional to enzymatic reactions, in which substrate specificity is key. Disconnection ; Computer science ; Retrosynthetic analysis ; USable ; Artificial intelligence ; Baseline (sea) ; Code refactoring ; Natural language processing ; Schema (genetic algorithms) ; Machine learning ; Training set ; Human–computer interaction ; Software ; Programming language ; World Wide Web ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Hao Zhang | Yubing Duan | Maojie Xie | Guoqing Ma | Pengfei Li | Jiafeng Qin
Source: Frontiers in Energy Research (article, 2022-09-06)
Field: Materials Science — Materials Chemistry

The effects of TiO2 nanoparticles on the temperature-dependent electrical and dielectric properties of polypropylene Nanoparticles are found to have the capacity to improve the specific properties of polypropylene (PP) for recyclable cable insulation material. However, previous studies have focused on the modification effect at room temperature, and few studies, on the nano-modification effect under high temperature environments. This article aims to study the effects of nanoparticles on the electrical and dielectric properties of PP at different high temperatures. Both TiO 2 nanoparticles and PP/TiO 2 nanocomposite samples are prepared in the laboratory by the wet chemical method and surface modification. Then, the AC and DC breakdown strength, conductivity, permittivity, and dielectric loss of the PP/TiO 2 nanocomposite are measured at different temperatures. The results show that TiO 2 nanoparticles introduce a small amount of charge traps into PP to restrain the conductivity and improve the breakdown strength of PP. But due to the additional interfacial polarization between TiO 2 nanoparticles and the PP matrix, the permittivity and dielectric loss of the PP/TiO 2 nanocomposite are enhanced, especially at low frequency. Materials science ; Polypropylene ; Dielectric ; Nanocomposite ; Nanoparticle ; Dielectric loss ; Composite material ; Permittivity ; Dielectric strength ; Surface modification ; Conductivity ; Electrical resistivity and conductivity ; Relative permittivity ; Chemical engineering ; Nanotechnology ; Chemistry ; Electrical engineering ; Optoelectronics High voltage insulation and dielectric phenomena ; Dielectric materials and actuators ; Polymer Nanocomposite Synthesis and Irradiation
Authors: Changsu Kim | Jiyong Kim
Source: International Journal of Energy Research (article, 2022-03-14)
Field: Materials Science — Materials Chemistry

Comparative evaluation of artificial neural networks for the performance prediction of Pt‐based catalysts in water gas shift reaction Artificial neural networks (ANNs) methods have recently been used for modeling and predicting catalysis, which has been conventionally described using reaction kinetics. ANNs-based techniques for analyzing complex catalytic systems have shown a high level of accuracy and dependability. In contrast, there is still a lack of strategies to select an appropriate ANNs algorithm according to size and quality of data, the complexity of the reaction, and catalyst characteristics. In this study, four different ANNs were proposed for the performance prediction of Pt-based catalysts in water gas shift reaction: multilayer perceptron, long short-term memory, recurrent neural network, and gated recurrent unit. By identifying the optimal ANN structure such as training/testing dataset ratio, topology, and epochs, the capability of the ANNs is comparatively evaluated in terms of prediction accuracy, computational load, and the quantity of required data for model training. As a case study, the effect of types and contents of promoters and supports in Pt-based catalysts on CO conversion was analyzed. As a result, it was revealed that the multilayer perceptron model shows better performance than the others with the highest accuracy (mean square error = 0.0068) and lowest computation time. In addition, it was identified using the multiplayer perceptron model that Pt/Ca/TiO2 of 10 wt% Ca is the most favorable catalyst by achieving CO conversion over 90% at an operating temperature of 300°C. Artificial neural network ; Multilayer perceptron ; Mean squared error ; Performance prediction ; Catalysis ; Computer science ; Computation ; Water-gas shift reaction ; Artificial intelligence ; Biological system ; Algorithm ; Chemistry ; Mathematics ; Simulation ; Statistics Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalytic Processes in Materials Science
Authors: Beomchang Kang | Chaok Seok | Juyong Lee
Source: Bulletin of the Korean Chemical Society (article, 2022-01-10)
Field: Materials Science — Materials Chemistry

A benchmark study of machine learning methods for molecular electronic transition: Tree‐based ensemble learning versus graph neural network Abstract Fluorophores play crucial roles in chemical and biological imaging. An efficient computational model that evaluates the electronic properties of molecules accurately would be a useful tool for discovering novel fluorophores. Tree‐based ensemble and graph neural network (GNN) methods have been regarded as attractive models for predicting molecular properties. Here, we present a benchmark test using three tree‐based ensemble methods (Random Forest, LightGBM, and XGBoost) and three GNNs (directed message passing neural network [D‐MPNN], attention message passing neural network [AMPNN], and DimeNet++) for predicting electronic transition properties such as excitation energies and oscillator strengths. From our benchmark, DimeNet++ was identified as the most accurate model to predict electronic transition properties. The average root mean square error of DimeNet++ for predicting the HOMO–LUMO gap was 0.11 eV whereas those of the other methods exceeded 0.3 eV. D‐MPNN predicted fastest without sacrificing accuracy. Our results show that DimeNet++ and D‐MPNN may serve as helpful evaluators for novel fluorophore design when combined with molecular generation methods. Benchmark (surveying) ; Artificial neural network ; Computer science ; Ensemble learning ; Artificial intelligence ; Random forest ; Tree (set theory) ; Graph ; Machine learning ; Deep learning ; Theoretical computer science ; Mathematics Machine Learning in Materials Science ; Analytical Chemistry and Sensors ; Electrochemical Analysis and Applications
Authors: Kun Ni | Fei Pan | Yanwu Zhu
Source: Advanced Functional Materials (article, 2022-08-07)
Field: Chemistry — Organic Chemistry

Structural Evolution of C<sub>60</sub> Molecular Crystal Predicted by Neural Network Potential Abstract The preparation of fullerene and a wide range of derivatives has attracted much attention in past decades. Understanding the structural evolution starting from fullerene is critical to guide the experimental exploration but has been paid less attention yet. Using ab initio molecular dynamics or global structural search algorithm accompanied with density functional theory calculations can give a glance of the potential energy surface (PES), but suffers high cost and low efficiency. Herein, by using neural network potentials and stochastic surface walking global structural search method, an accelerated energy calculation, and the higher efficient PES mapping starting from C 60 molecular crystal are reported. The structural evolution is found to follow an order of molecular crystal → polymers → opened caged ordered structures → 3D) curved carbon and graphite under zero pressure. Under high pressures, e.g., 50 GPa, the evolution follows a pathway of fullerene → sp 2 and sp 3 hybrid carbon → sp 3 amorphous carbon and crystal diamond. The electronic property calculation of the obtained structures in the evolution pathway shows a band gap depending on the order parameter of the generated structures, suggesting that more novel carbons can be potentially prepared in experiments, starting from C 60 . Materials science ; Molecular dynamics ; Fullerene ; Crystal (programming language) ; Amorphous solid ; Carbon fibers ; Ab initio ; Diamond ; Crystal structure prediction ; Potential energy surface ; Density functional theory ; Chemical physics ; Electronic structure ; Crystal structure ; Graphite ; Nanotechnology ; Computational chemistry ; Crystallography ; Computer science ; Physics ; Chemistry Fullerene Chemistry and Applications ; Diamond and Carbon-based Materials Research ; High-pressure geophysics and materials
Authors: Lina Kuhn | Taavi Repän | Carsten Rockstuhl
Source: Scientific Reports (article, 2022-11-08)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Inverse design of core-shell particles with discrete material classes using neural networks The design of scatterers on demand is a challenging task that requires the investigation and development of novel and flexible approaches. In this paper, we propose a machine learning-assisted optimization framework to design multi-layered core-shell particles that provide a scattering response on demand. Artificial neural networks can learn to predict the scattering spectrum of core-shell particles with high accuracy and can act as fully differentiable surrogate models for a gradient-based design approach. To enable the fabrication of the particles, we consider existing materials and introduce a novel two-step optimization to treat continuous geometric parameters and discrete feasible materials simultaneously. Moreover, we overcome the non-uniqueness of the problem and expand the design space to particles of varying numbers of shells, i.e., different number of optimization parameters, with a classification network. Our method is 1-2 orders of magnitudes faster than conventional approaches in both forward prediction and inverse design and is potentially scalable to even larger and more complex scatterers. Computer science ; Scalability ; Inverse ; Shell (structure) ; Differentiable function ; Artificial neural network ; Mathematical optimization ; Core (optical fiber) ; Inverse problem ; Algorithm ; Artificial intelligence ; Mathematics ; Geometry ; Mechanical engineering ; Engineering ; Mathematical analysis Metamaterials and Metasurfaces Applications ; Acoustic Wave Phenomena Research ; Photonic Crystals and Applications
Authors: Lixue Cheng | Jiace Sun | J. Emiliano Deustua | Vignesh C. Bhethanabotla | Thomas F. Miller
Source: The Journal of Chemical Physics (article, 2022-10-21)
Field: Materials Science — Materials Chemistry

Molecular-orbital-based machine learning for open-shell and multi-reference systems with kernel addition Gaussian process regression We introduce a novel machine learning strategy, kernel addition Gaussian process regression (KA-GPR), in molecular-orbital-based machine learning (MOB-ML) to learn the total correlation energies of general electronic structure theories for closed- and open-shell systems by introducing a machine learning strategy. The learning efficiency of MOB-ML(KA-GPR) is the same as the original MOB-ML method for the smallest criegee molecule, which is a closed-shell molecule with multi-reference characters. In addition, the prediction accuracies of different small free radicals could reach the chemical accuracy of 1 kcal/mol by training on one example structure. Accurate potential energy surfaces for the H<sub>10</sub> chain (closed-shell) and water OH bond dissociation (open-shell) could also be generated by MOB-ML(KA-GPR). To explore the breadth of chemical systems that KA-GPR can describe, we further apply MOB-ML to accurately predict the large benchmark datasets for closed- (QM9, QM7b-T, and GDB-13-T) and open-shell (QMSpin) molecules. Open shell ; Kriging ; Ground-penetrating radar ; Dissociation (chemistry) ; Support vector machine ; Computer science ; Artificial intelligence ; Benchmark (surveying) ; Molecule ; Kernel (algebra) ; Gaussian ; Shell (structure) ; Regression ; Machine learning ; Chemistry ; Computational chemistry ; Physics ; Materials science ; Atomic physics ; Mathematics ; Physical chemistry ; Quantum mechanics ; Statistics ; Discrete mathematics ; Radar Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Pin Chen | Jianwen Chen | Yan Hui | Qing Mo | Zexin Xu | Jinyu Liu | Wenqing Zhang | Yuedong Yang | Yutong Lu
Source: The Journal of Physical Chemistry C (article, 2022-08-17)
Field: Materials Science — Materials Chemistry

Improving Material Property Prediction by Leveraging the Large-Scale Computational Database and Deep Learning Predicting physical and chemical properties of materials based on structures is critical for bottom-up material design. Many property prediction models and material training databases have been proposed, but accurately predicting properties is still challenging. Here, we report a package of “Matgen + CrystalNet” approach to improve material property prediction. We construct a large-scale material genome database (Matgen) containing 76k materials collected from an experimentally observed database and compute their properties through the density functional theory method with the Perdew–Burke–Ernzerhof (PBE) functional. Our database achieves the same computation accuracy by comparing part of our results with those from the open Material Project and Open Quantum Materials Database, all with PBE computations, and contains more diverse chemical species and big-sized structures. Based on the computed properties of our comprehensive data set, we have developed a new graph neural network (GNN) model, namely, CrystalNet, by strengthening the message passing between atoms and bonds to mimic physical and chemical interactions. The model is shown to outperform other GNN prediction models. The proof-of-concept applications, such as fine-tuning data on experimental values to improve prediction accuracy and bandgap prediction on hypothetical materials, showcase the usability and potential capacity of our package of “database + model” to improve material design. Computer science ; Property (philosophy) ; Database ; Computation ; Usability ; Set (abstract data type) ; Big data ; Scale (ratio) ; Construct (python library) ; Artificial intelligence ; Data mining ; Algorithm Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Surface Chemistry and Catalysis
Authors: Ludwig Winkler | Klaus‐Robert Müller | Huziel E. Sauceda
Source: Machine Learning Science and Technology (article, 2022-05-11)
Field: Materials Science — Materials Chemistry

High-fidelity molecular dynamics trajectory reconstruction with bi-directional neural networks Abstract Molecular dynamics (MD) simulations are a cornerstone in science, enabling the investigation of a system’s thermodynamics all the way to analyzing intricate molecular interactions. In general, creating extended molecular trajectories can be a computationally expensive process, for example, when running ab-initio simulations. Hence, repeating such calculations to either obtain more accurate thermodynamics or to get a higher resolution in the dynamics generated by a fine-grained quantum interaction can be time- and computational resource-consuming. In this work, we explore different machine learning methodologies to increase the resolution of MD trajectories on-demand within a post-processing step. As a proof of concept, we analyse the performance of bi-directional neural networks (NNs) such as neural ODEs, Hamiltonian networks, recurrent NNs and long short-term memories, as well as the uni-directional variants as a reference, for MD simulations (here: the MD17 dataset). We have found that Bi-LSTMs are the best performing models; by utilizing the local time-symmetry of thermostated trajectories they can even learn long-range correlations and display high robustness to noisy dynamics across molecular complexity. Our models can reach accuracies of up to 10 −4 Å in trajectory interpolation, which leads to the faithful reconstruction of several unseen high-frequency molecular vibration cycles. This renders the comparison between the learned and reference trajectories indistinguishable. The results reported in this work can serve (1) as a baseline for larger systems, as well as (2) for the construction of better MD integrators. Computer science ; Molecular dynamics ; Artificial neural network ; Artificial intelligence ; Robustness (evolution) ; Algorithm ; Statistical physics ; Physics ; Computational chemistry ; Chemistry Machine Learning in Materials Science ; Protein Structure and Dynamics ; Topic Modeling
Authors: Xiao Zhu | Srinivasan S. Iyengar
Source: Journal of Chemical Theory and Computation (article, 2022-08-22)
Field: Materials Science — Materials Chemistry

Graph Theoretic Molecular Fragmentation for Multidimensional Potential Energy Surfaces Yield an Adaptive and General Transfer Machine Learning Protocol Over a series of publications we have introduced a graph-theoretic description for molecular fragmentation. Here, a system is divided into a set of nodes, or vertices, that are then connected through edges, faces, and higher-order simplexes to represent a collection of spatially overlapping and locally interacting subsystems. Each such subsystem is treated at two levels of electronic structure theory, and the result is used to construct many-body expansions that are then embedded within an ONIOM-scheme. These expansions converge rapidly with many-body order (or graphical rank) of subsystems and have been previously used for ab initio molecular dynamics (AIMD) calculations and for computing multidimensional potential energy surfaces. Specifically, in all these cases we have shown that CCSD and MP2 level AIMD trajectories and potential surfaces may be obtained at density functional theory cost. The approach has been demonstrated for gas-phase studies, for condensed phase electronic structure, and also for basis set extrapolation-based AIMD. Recently, this approach has also been used to derive new quantum-computing algorithms that enormously reduce the quantum circuit depth in a circuit-based computation of correlated electronic structure. In this publication, we introduce (a) a <i>family of neural networks</i> that act in parallel to represent, efficiently, the post-Hartree-Fock electronic structure energy contributions for all simplexes (fragments), and (b) a new k-means-based tessellation strategy to glean training data for high-dimensional molecular spaces and minimize the extent of training needed to construct this family of neural networks. The approach is particularly useful when coupled cluster accuracy is desired and when fragment sizes grow in order to capture nonlocal interactions accurately. The unique multidimensional k-means tessellation/clustering algorithm used to determine our training data for all fragments is shown to be extremely efficient and reduces the needed training to only 10% of data for all fragments to obtain accurate neural networks for each fragment. These fully connected dense neural networks are then used to extrapolate the potential energy surface for all molecular fragments, and these are then combined as per our graph-theoretic procedure to <i>transfer</i> the learning process to a full system energy for the entire AIMD trajectory at less than one-tenth the cost as compared to a regular fragmentation-based AIMD calculation. Computer science ; Electronic structure ; Simplex ; Theoretical computer science ; Topology (electrical circuits) ; Statistical physics ; Mathematics ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Spectroscopy and Quantum Chemical Studies
Authors: Ran Fu | Yihua Xu | Qiao Shi | Yisi Liu | Yan‐Wen Lin | Yang Li | Zhisen Zhang | Jianyang Wu
Source: Journal of Physics Condensed Matter (article, 2022-08-05)
Field: Chemistry — Organic Chemistry

Size-dependent melting of onion-like fullerenic carbons: a molecular dynamics and machine learning study The melting thermodynamic characteristics of 2- to 20-layered onion-like fullerenes (OLF<i><sub>n</sub></i>) (C<sub>60</sub>@C<sub>240</sub>to C<sub>60</sub>@···@C<sub>6000</sub>···@C<sub>24000</sub>) are comprehensively explored using first-principles-based ReaxFF atomistic simulations and random forest machine learning (RF ML). It is revealed that OLF<i><sub>n</sub></i>shows lower thermal stability than the counterparts of single-walled fullerenes (SWF<i><sub>n</sub></i>). The melting point of SWF<i><sub>n</sub></i>increases monotonically with increasing size, whereas for OLF<i><sub>n</sub></i>, an unusual size-dependent melting point is observed; OLF<i><sub>n</sub></i>with intermediate size shows the highest melting point. For small OLF<i><sub>n</sub></i>, the melting occurs from the inner to the outer, whereas for large OLF<i><sub>n</sub></i>, it nucleates from the inner to the outer and to intermediate fullerenes. The melting and erosion behaviors of both SWF<i><sub>n</sub></i>and OLF<i><sub>n</sub></i>are mainly characterized by the nucleation of non-hexagons, nanovoids, carbon chains and emission of C<sub>2</sub>. RF ML model is developed to predict the melting points of both SWF<i><sub>n</sub></i>and OLF<i><sub>n</sub></i>. Moreover, the analysis of the feature importance reveals that the Stone-Wales transformation is a critical pathway in the melting of SWF<i><sub>n</sub></i>and OLF<i><sub>n</sub></i>. This study provides new insights and perspectives into the thermodynamics and pyrolysis chemistry of fullerenic carbons, and also may shed some lights onto the understanding of thermally-induced erosion of carbon-based resources and spacecraft materials. ReaxFF ; Melting point ; Fullerene ; Molecular dynamics ; Nucleation ; Materials science ; Melting-point depression ; Melting temperature ; Carbon fibers ; Chemical physics ; Pyrolysis ; Nanotechnology ; Thermodynamics ; Chemical engineering ; Chemistry ; Computational chemistry ; Composite material ; Physics ; Composite number ; Interatomic potential ; Organic chemistry Fullerene Chemistry and Applications ; Carbon Nanotubes in Composites ; Diamond and Carbon-based Materials Research