jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Julia Westermayr | Joe Gilkes | Rhyan Barrett | Reinhard J. Maurer
Source: arXiv (Cornell University) (preprint, 2022-07-04)
Field: Materials Science — Materials Chemistry

High-throughput property-driven generative design of functional organic molecules The design of molecules and materials with tailored properties is challenging, as candidate molecules must satisfy multiple competing requirements that are often difficult to measure or compute. While molecular structures, produced through generative deep learning, will satisfy those patterns, they often only possess specific target properties by chance and not by design, which makes molecular discovery via this route inefficient. In this work, we predict molecules with (pareto)-optimal properties by combining a generative deep learning model that predicts three dimensional conformations of molecules with a supervised deep learning model that takes these as inputs and predicts their electronic structure. Optimization of (multiple) molecular properties is achieved by screening newly generated molecules for desirable electronic properties and reusing hit molecules to retrain the generative model with a bias. The approach is demonstrated to find optimal molecules for organic electronics applications. Our method is generally applicable and eliminates the need for quantum chemical calculations during predictions, making it suitable for high-throughput screening in materials and catalyst design. Generative grammar ; Property (philosophy) ; Throughput ; Organic molecules ; Computer science ; Molecule ; Artificial intelligence ; Chemistry ; Organic chemistry ; Telecommunications ; Philosophy Machine Learning in Materials Science ; Scientific Computing and Data Management ; Photonic and Optical Devices
Authors: Alexandre Duval | Victor Schmidt | Santiago Miret | Yoshua Bengio | Álex Hernández-García | David Rolnick
Source: arXiv (Cornell University) (preprint, 2022-11-22)
Field: Materials Science — Materials Chemistry

PhAST: Physics-Aware, Scalable, and Task-specific GNNs for Accelerated Catalyst Design Mitigating the climate crisis requires a rapid transition towards lower-carbon energy. Catalyst materials play a crucial role in the electrochemical reactions involved in numerous industrial processes key to this transition, such as renewable energy storage and electrofuel synthesis. To reduce the energy spent on such activities, we must quickly discover more efficient catalysts to drive electrochemical reactions. Machine learning (ML) holds the potential to efficiently model materials properties from large amounts of data, accelerating electrocatalyst design. The Open Catalyst Project OC20 dataset was constructed to that end. However, ML models trained on OC20 are still neither scalable nor accurate enough for practical applications. In this paper, we propose task-specific innovations applicable to most architectures, enhancing both computational efficiency and accuracy. This includes improvements in (1) the graph creation step, (2) atom representations, (3) the energy prediction head, and (4) the force prediction head. We describe these contributions, referred to as PhAST, and evaluate them thoroughly on multiple architectures. Overall, PhAST improves energy MAE by 4 to 42$\%$ while dividing compute time by 3 to 8$\times$ depending on the targeted task/model. PhAST also enables CPU training, leading to 40$\times$ speedups in highly parallelized settings. Python package: \url{https://phast.readthedocs.io}. Task (project management) ; Scalability ; Computer science ; Catalysis ; Systems engineering ; Chemistry ; Engineering ; Database ; Organic chemistry Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Cloud Computing and Resource Management
Authors: Xiao‐Jun Pei | Haojun Shu | Yi‐Si Feng
Source: Scientific Programming (article, 2022-05-20)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Preparation of Nitrogen-Doped Carbon-Based Bimetallic Copper-Cobalt Catalysis Based on Deep Learning and Its Monitoring Application in Furfural Hydrogenation In the field of catalysis, the support of the catalyst is often composed of hollow carbon materials. In order to monitor the preparation of nitrogen-doped carbon-based bimetallic copper-cobalt catalysis and its hydrogenation reaction in furfural, using m-aminophenol as the nitrogen source, formaldehyde as a carbon source, and P123 as a template agent, a nitrogen-doped bimetallic copper-cobalt mesoporous carbon catalyst Cu-Co@N-MPC-500 was synthesized by the hydrothermal method. The morphology, structure, and chemical composition of the catalyst were analyzed by means of TEM, XRD, BET, and XPS, respectively. The results show that the nitrogen-doped mesoporous carbon has a stable structure, uniform pore size distribution, and the nano-copper-cobalt particles are uniformly dispersed in the mesoporous carbon surface. Through furfural hydrogenation, the catalyst selectivity and cycle stability were discussed. Under the furfural conversion rate of 96.1%, the yield of cyclopentanone could reach 76.2%. After 5 cycles, the catalytic efficiency of the catalyst did not decrease significantly. It shows that Cu-Co@N-MPC-500 has excellent application prospects in the field of industrial production. Furfural ; Catalysis ; Cobalt ; Bimetallic strip ; Materials science ; Carbon fibers ; Inorganic chemistry ; Chemical engineering ; Copper ; Mesoporous material ; Chemistry ; Organic chemistry ; Metallurgy ; Composite material Supercapacitor Materials and Fabrication ; Nanomaterials for catalytic reactions ; Electrocatalysts for Energy Conversion
Authors: Yao Yang | Jihan Zhou | Zipeng Zhao | Geng Sun | Saman Moniri | Colin Ophus | Yongsoo Yang | Ziyang Wei | Yakun Yuan | Cheng Zhu | Yang Liu | Qiang Sun | Qingying Jia | Hendrik Heinz | Jim Ciston | Peter Ercius | Philippe Sautet | Yu Huang | Jianwei Miao
Source: Research Square (preprint, 2022-08-04)
Field: Materials Science — Materials Chemistry

Atomic-scale identification of the active sites of nanocatalysts Nanomaterial-based catalyst ; Identification (biology) ; Scale (ratio) ; Atomic units ; Nanotechnology ; Materials science ; Geography ; Physics ; Biology ; Cartography ; Ecology Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalytic Processes in Materials Science
Authors: Pablo Sánchez-Palencia | Said Hamad | Pablo Palacios | Ricardo Grau‐Crespo | Keith T. Butler
Source: Digital Discovery (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Spinel nitride solid solutions: charting properties in the configurational space with explainable machine learning The thermodynamic and electronic properties of solid solutions are highly tuneable, based on ion arrangement. We use interpretable machine learning to explore the structure-property landscape of a spinel nitride and understand how crystal chemistry affects physical properties. Spinel ; Nitride ; Space (punctuation) ; Property (philosophy) ; Materials science ; Ion ; Solid solution ; Crystal structure ; Computer science ; Nanotechnology ; Chemistry ; Crystallography ; Physics ; Metallurgy ; Quantum mechanics ; Epistemology ; Philosophy Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Jiahui Yu | Chengwei Zhang | Yingying Cheng | Yun‐Fang Yang | Yuanbin She | Fengfan Liu | Weike Su | An Su
Source: ChemRxiv (preprint, 2022-10-05)
Field: Materials Science — Materials Chemistry

SolvBERT for solvation free energy and solubility prediction: a demonstration of an NLP model for predicting the properties of molecular complexes Deep learning models based on NLP, mainly the Transformer family, have been successfully applied to solve many chemistry-related problems, but their applications are mostly limited to chemical reactions. Meanwhile, solvation is an important concept in physical and organic chemistry, describing the interaction of solutes and solvents. This interaction leads to a solvation complex, a molecular complex similar to a reactant-reagent complex. In this study, we introduced the SolvBERT model, which reads the solute and solvents through the SMILES representation of the solvation complex. SolvBERT is pretrained in an unsupervised learning fashion using a large database of computational solvation free energies. The pretrained model can be used to predict the experimental solvation free energy or solubility, depending on the fine-tuning database. To the best of our knowledge, this multi-task prediction capability has not been observed in previously developed graph-based models for predicting the properties of molecular complexes. Furthermore, the performance of our SolvBERT in predicting solvation free energy is comparable to the state-of-the-art graph-based model DMPNN, mainly due to the clustering feature of the pretraining phase of the model, as demonstrated by the TMAP visualization algorithm. Solvation ; Implicit solvation ; Computer science ; Artificial intelligence ; Molecular graph ; Chemistry ; Machine learning ; Graph ; Molecule ; Theoretical computer science ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Text Analysis Techniques
Authors: Jiahui Yu | Chengwei Zhang | Yingying Cheng | Yun‐Fang Yang | Yuanbin She | Fengfan Liu | Weike Su | An Su
Source: ChemRxiv (preprint, 2022-07-27)
Field: Materials Science — Materials Chemistry

SolvBERT for solvation free energy and solubility prediction: a demonstration of an NLP model for predicting the properties of molecular complexes Deep learning models based on NLP, mainly the Transformer family, have been successfully applied to solve many chemistry-related problems, but their applications are mostly limited to chemical reactions. Meanwhile, solvation is an important concept in physical and organic chemistry, describing the interaction of solutes and solvents. This interaction leads to a solvation complex, a molecular complex similar to a reactant-reagent complex. In this study, we introduced the SolvBERT model, which reads the solute and solvents through the SMILES representation of the solvation complex. SolvBERT is pretrained in an unsupervised learning fashion using a large database of computational solvation free energies. The pretrained model can be used to predict the experimental solvation free energy or solubility, depending on the fine-tuning database. To the best of our knowledge, this multi-task prediction capability has not been observed in previously developed graph-based models for predicting the properties of molecular complexes. Furthermore, the performance of our SolvBERT in predicting solvation free energy is comparable to the state-of-the-art graph-based model DMPNN, mainly due to the clustering feature of the pretraining phase of the model, as demonstrated by the TMAP visualization algorithm. Solvation ; Implicit solvation ; Computer science ; Artificial intelligence ; Solubility ; Chemistry ; Machine learning ; Molecule ; Physical chemistry ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Text Analysis Techniques
Authors: Byungchan Han
Source: Science Talks (article, 2022-03-28)
Field: Materials Science — Materials Chemistry

DFT calculations and machine learning approach to predict catalytic properties of nanoscale electrocatalysts in solution for clean fuel generation A nanocatalyst is at the central position as a promoter of various chemical. Innovative design of highly functional catalyst materials has been, however, delayed. In molecular level computational electrochemistry new research paradigm has been established, which substantially incorporates IT-based artificial intelligence (AI) technology into machine learning algorism. Using the new computational methodology high-throughput screening of promising nanoparticle candidates has been attempted for various desired applications. Whether the frontier approach is successful or not is significantly controlled by the reliability and accuracy of input database. It is true that substantial amounts of the data are come by previous literatures and often ab-initio calculations with idealized model systems. The conditions in which the data were generated may be so different from the operando circumstances of the target materials. To secure extreme-level integrity of the database the in-situ measurement of nanoparticle structures should be carried out, from which the reliable correlation of the structure-performance-design principle can be identified. Using first-principles calculations we studied nanoparticles with adsorbate ligands in liquid solution to establish three-dimensional (3D) structure and property database, which are, then, analyzed through AI-based neural-network approach with high speed and accuracy. The information includes sizes, lattice distortions, and defects with picometer resolution under non-vacuum conditions. The computational outcomes are rigorously validated from the 3D liquid-cell electron microscopy. The approach is indeed ‘knowledge-based’ AI, which can be expected to make groundbreaking ways toward the quantum nanoarchitecture for hybrid interface materials. Computer science ; Nanoparticle ; Algorism ; Artificial neural network ; Multiscale modeling ; Density functional theory ; Nanotechnology ; Artificial intelligence ; Materials science ; Computational science ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Electrochemical Analysis and Applications
Authors: Boyu Zhang | Shuo Wang | Fuchang Gao
Source: SN Computer Science (article, 2022-09-03)
Field: Materials Science — Materials Chemistry

Contrastive Metric Learning for Lithium Super-ionic Conductor Screening Conductor ; Metric (unit) ; Generalization ; Electrical conductor ; Battery (electricity) ; Computer science ; Lithium (medication) ; Artificial intelligence ; Sample (material) ; Ionic bonding ; Metric space ; Feature (linguistics) ; Machine learning ; Materials science ; Ion ; Engineering ; Power (physics) ; Electrical engineering ; Mathematics ; Chemistry ; Physics ; Psychology ; Chromatography Machine Learning in Materials Science ; Advanced Battery Technologies Research ; Fuel Cells and Related Materials
Authors: Shomik Verma | Miguel Rivera | David O. Scanlon | Aron Walsh
Source: ChemRxiv (preprint, 2022-01-11)
Field: Materials Science — Materials Chemistry

Machine learned calibrations to high-throughput molecular excited state calculations Understanding the excited state properties of molecules provides insights into how they interact with light. These interactions can be exploited to design compounds for photochemical applications, including enhanced spectral conversion of light to increase the efficiency of photovoltaic cells. While chemical discovery is time- and resource-intensive experimentally, computational chemistry can be used to screen large-scale databases for molecules of interest in a procedure known as high-throughput virtual screening. The first step usually involves a high-speed but low-accuracy method to screen large numbers of molecules (potentially millions) so only the best candidates are evaluated with expensive methods. However, use of a coarse first-pass screening method can potentially result in high false positive or false negative rates. Therefore, this study uses machine learning to calibrate a high-throughput technique (xTB-sTDA) against a higher accuracy one (TD-DFT). Testing the calibration model shows a ~5-fold decrease in error in-domain and a ~3-fold decrease out-of-domain. The resulting mean absolute error of ~0.14 eV is in line with previous work in machine learning calibrations and out-performs previous work in linear calibration of xTB-sTDA. We then apply the calibration model to screen a 250k molecule database and map inaccuracies of xTB-sTDA in chemical space. We also show generalizability of the workflow by calibrating against a higher-level technique (CC2), yielding a similarly low error. Overall, this work demonstrates machine learning can be used to develop a both cheap and accurate method for large-scale excited state screening, enabling accelerated molecular discovery across a variety of disciplines. Generalizability theory ; Calibration ; Computer science ; Excited state ; Throughput ; Workflow ; Algorithm ; Domain (mathematical analysis) ; Artificial intelligence ; Machine learning ; Mathematics ; Physics ; Database ; Statistics ; Atomic physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Michael Scherbela | Rafael Reisenhofer | Leon Gerard | Philipp Marquetand | Philipp Grohs
Source: Nature Computational Science (preprint, 2022-05-19)
Field: Materials Science — Materials Chemistry

Solving the electronic Schrödinger equation for multiple nuclear geometries with weight-sharing deep neural networks Curse of dimensionality ; Artificial neural network ; Wave function ; Leverage (statistics) ; Computer science ; Artificial intelligence ; Deep learning ; Quantum Monte Carlo ; Schrödinger equation ; Statistical physics ; Quantum ; Constraint (computer-aided design) ; Monte Carlo method ; Machine learning ; Mathematical optimization ; Algorithm ; Applied mathematics ; Physics ; Mathematics ; Quantum mechanics ; Geometry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Yanzhou Wang | Zheyong Fan | Ping Qian | Miguel A. Caro | Tapio Ala-Nissilä
Source: arXiv (Cornell University) (preprint, 2022-06-15)
Field: Materials Science — Materials Chemistry

Quantum-corrected thickness-dependent thermal conductivity in amorphous silicon predicted by machine-learning molecular dynamics simulations Amorphous silicon (a-Si) is an important thermal-management material and also serves as an ideal playground for studying heat transport in strongly disordered materials. Theoretical prediction of the thermal conductivity of a-Si in a wide range of temperatures and sample sizes is still a challenge. Herein we present a systematic investigation of the thermal transport properties of a-Si by employing large-scale molecular dynamics (MD) simulations with an accurate and efficient machine-learned neuroevolution potential (NEP) trained against abundant reference data calculated at the quantum-mechanical density-functional-theory level. The high efficiency of NEP allows us to study the effects of finite size and quenching rate in the formation of a-Si in great detail. We find that it requires a simulation cell up to $64,000$ atoms (a cubic cell with a linear size of 11 nm) and a quenching rate down to $10^{11}$ K s$^{-1}$ for fully convergent thermal conductivity. Structural properties, including short- and medium-range order as characterized by the pair correlation function, angular distribution function, coordination number, ring statistics and structure factor are studied to demonstrate the accuracy of NEP and to further evaluate the role of quenching rate. Using both the heterogeneous and the homogeneous nonequilibrium MD methods and the related spectral decomposition techniques, we calculate the temperature- and thickness-dependent thermal conductivity values of a-Si and show that they agree well with available experimental results from 10 K to room temperature. Our results also highlight the importance of quantum effects in the calculated thermal conductivity and support the quantum correction method based on the spectral thermal conductivity. Thermal conductivity ; Quenching (fluorescence) ; Materials science ; Amorphous solid ; Molecular dynamics ; Amorphous silicon ; Atmospheric temperature range ; Quantum ; Silicon ; Thermodynamics ; Physics ; Chemistry ; Computational chemistry ; Composite material ; Optics ; Crystalline silicon ; Crystallography ; Optoelectronics Thermal properties of materials ; Thin-Film Transistor Technologies ; Machine Learning in Materials Science
Authors: Eugen Hruška | Ariel Gale | Xiao Huang | Fang Liu
Source: ChemRxiv (preprint, 2022-01-10)
Field: Materials Science — Materials Chemistry

AutoSolvate: A Toolkit for Automating Quantum Chemistry Design and Discovery of Solvated Molecules The availability of large, high-quality data sets is crucial for artificial intelligence design and discovery in chemistry. Despite the essential roles of solvents in chemistry, the rapid computational data set generation of solution-phase molecular properties at the quantum mechanical level of theory was previously hampered by the complicated simulation procedure. Software toolkits that can automate the procedure to set up high-throughput explicit-solvent quantum chemistry (QC) calculations for arbitrary solutes and solvents in an open-source framework are still lacking. We developed AutoSolvate, an open-source toolkit to streamline the workflow for QC calculation of explicitly solvated molecules. It automates the solvated-structure generation, force field fitting, configuration sampling, and the final extraction of microsolvated cluster structures that QC packages can readily use to predict molecular properties of interest. AutoSolvate is available through both a command line interface and a graphical user interface, making it accessible to the broader scientific community. To improve the quality of the initial structures generated by AutoSolvate, we investigated the dependence of solute-solvent closeness on solute/solvent identities and trained a machine learning model to predict the closeness and guide initial structure generation. Finally, we tested the capability of AutoSolvate for rapid data set curation by calculating the outer-sphere reorganization energy of a large data set of 166 redox couples, which demonstrated the promise of the AutoSolvate package for chemical discovery efforts. Computer science ; Workflow ; Closeness ; Interface (matter) ; Software ; Set (abstract data type) ; Chemistry ; Computational science ; Theoretical computer science ; Molecule ; Database ; Programming language Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Konstantin Gubaev | Viktor Zaverkin | Prashanth Sriniva | Andrew Ian Duff | Johannes Kästner | Blazej Grabowski
Source: Research Square (preprint, 2022-11-29)
Field: Materials Science — Materials Chemistry

Exploring the limits of machine-learned potentials for chemically complex multicomponent systems Computer science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics