jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Rose K. Cersonsky | Maria Pakhnova | Edgar A. Engel | Michele Ceriotti
Source: Chemical Science (article, 2023-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

A data-driven interpretation of the stability of organic molecular crystals Due to the subtle balance of intermolecular interactions that govern structure-property relations, predicting the stability of crystal structures formed from molecular building blocks is a highly non-trivial scientific problem. A particularly active and fruitful approach involves classifying the different combinations of interacting chemical moieties, as understanding the relative energetics of different interactions enables the design of molecular crystals and fine-tuning of their stabilities. While this is usually performed based on the empirical observation of the most commonly encountered motifs in known crystal structures, we propose to apply a combination of supervised and unsupervised machine-learning techniques to automate the construction of an extensive library of molecular building blocks. We introduce a structural descriptor tailored to the prediction of the binding (lattice) energy and apply it to a curated dataset of organic crystals, exploiting its atom-centered nature to obtain a data-driven assessment of the contribution of different chemical groups to the lattice energy of the crystal. We then interpret this library using a low-dimensional representation of the structure-energy landscape and discuss selected examples of the insights into crystal engineering that can be extracted from this analysis, providing a complete database to guide the design of molecular materials. Interpretation (philosophy) ; Crystal (programming language) ; Stability (learning theory) ; Balance (ability) ; Chemical stability ; Crystal engineering ; Materials science ; Chemistry ; Computational chemistry ; Chemical physics ; Molecule ; Biological system ; Nanotechnology ; Computer science ; Organic chemistry ; Machine learning ; Psychology ; Supramolecular chemistry ; Biology Crystallography and molecular interactions ; Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Ramón Frey | Bastien F. Grosso | Jean Pascal Fandré | Benjamin Mächler | Nicola A. Spaldin | Aria Mansouri Tehrani
Source: Physical Review Research (article, 2023-05-25)
Field: Materials Science — Materials Chemistry

Accelerated search for new ferroelectric materials We report the development of a combined machine learning and high-throughput density functional theory (DFT) framework to accelerate the search for new ferroelectric materials. The framework can predict potential ferroelectric compounds using only elemental composition as input. A series of machine-learning algorithms initially predict the possible stable and insulating stoichiometries with polar crystal structures, necessary for ferroelectricity, within a given chemical composition space. A classification model then predicts the point groups of these stoichiometries. A subsequent series of high-throughput DFT calculations finds the lowest-energy crystal structure within the point group. As a final step, nonpolar parent structures are identified using group theory considerations, and the values of the spontaneous polarization are calculated using DFT. By predicting the crystal structures and the polarization values, this method provides a powerful tool to explore new ferroelectric materials beyond those in existing databases. Ferroelectricity ; Density functional theory ; Polarization (electrochemistry) ; Stoichiometry ; Polar ; Materials science ; Series (stratigraphy) ; Crystal structure ; Crystal structure prediction ; Chemical physics ; Computational chemistry ; Algorithm ; Computer science ; Crystallography ; Chemistry ; Physical chemistry ; Physics ; Optoelectronics ; Quantum mechanics ; Dielectric Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Inorganic Chemistry and Materials
Authors: Mohammad Khatamirad | Edvin Fako | Chiara Boscagli | Matthias Müller | Fabian Ebert | Raoul Naumann d’Alnoncourt | Ansgar Schaefer | Stephan A. Schunk | Ivana Jevtovikj | Frank Rosowski | Sandip De
Source: Catalysis Science & Technology (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

A data-driven high-throughput workflow applied to promoted In-oxide catalysts for CO<sub>2</sub> hydrogenation to methanol To facilitate accelerated catalyst design, a combined computation and experimental workflow based on machine learning algorithms is proposed, which detects key performance-related descriptors in a CO 2 to methanol reaction, for In 2 O 3 -based catalysts. Workflow ; Catalysis ; Methanol ; Throughput ; Computation ; Key (lock) ; Computer science ; Oxide ; Chemistry ; Database ; Organic chemistry ; Algorithm ; Operating system Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Machine Learning in Materials Science
Authors: Marvin Alberts | Teodoro Laino | Alain C. Vaucher
Source: ChemRxiv (preprint, 2023-05-14)
Field: Chemistry — Analytical Chemistry

Leveraging Infrared Spectroscopy for Automated Structure Elucidation The application of machine learning models in chemistry has made remarkable strides in recent years. While the field of analytical chemistry has also received considerable interest from machine learning practitioners, very few models have been adopted into everyday use. Among the analytical instruments available to chemists, Infrared (IR) spectroscopy is one of the cheapest, easiest and most accessible. So far the use of IR has been limited to the identification of a select few functional groups with well-known vibrational frequencies with the interpretation of most peaks lying outside of human capabilities. We present a novel machine learning model that enables chemists to leverage the complete information contained within an IR spectrum to directly predict the molecular structure. To achieve this, we developed a transformer model trained on IR spectra that predicts the molecular structure as a SMILES string. To cover a vast portion of chemical space, we generated a training set of 634,585 simulated IR spectra using molecular dynamics. Our approach achieved a top-1 accuracy of 45.33% and a top-10 accuracy of 78.5% on a test set sampled from PubChem with a heavy atom count ranging from 6 to 13. The model is useful also in cases where an incorrect structure is predicted, as it is capable of predicting the correct scaffold in 77.01% of cases as the top-1 prediction and in 91.54% in the top-10 predictions. In addition, the model outperforms other models solely trained to predict the functional group from the IR spectrum. Leverage (statistics) ; Computer science ; PubChem ; Infrared spectroscopy ; Artificial intelligence ; Machine learning ; Test set ; Spectroscopy ; Chemical space ; Pattern recognition (psychology) ; Chemistry ; Physics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Metabolomics and Mass Spectrometry Studies
Authors: Raquel López-Ríos de Castro | Robert M. Ziolek | Christian D. Lorenz
Source: Nanoscale (article, 2023-01-01)
Field: Chemistry — Organic Chemistry

Topology-controlled self-assembly of amphiphilic block copolymers Contemporary synthetic chemistry approaches can be used to yield a range of distinct polymer topologies with precise control. The topology of a polymer strongly influences its self-assembly into complex nanostructures however a clear mechanistic understanding of the relationship between polymer topology and self-assembly has not yet been developed. In this work, we use atomistic molecular dynamics simulations to provide a nanoscale picture of the self-assembly of three poly(ethylene oxide)-poly(methyl acrylate) block copolymers with different topologies into micelles. We find that the topology affects the ability of the micelle to form a compact hydrophobic core, which directly affects its stability. Also, we apply unsupervised machine learning techniques to show that the topology of a polymer affects its ability to take a conformation in response to the local environment within the micelles. This work provides foundations for the rational design of polymer nanostructures based on their underlying topology. Micelle ; Topology (electrical circuits) ; Polymer ; Amphiphile ; Copolymer ; Materials science ; Nanostructure ; Network topology ; Ethylene oxide ; Self-assembly ; Nanotechnology ; Block (permutation group theory) ; Computer science ; Chemistry ; Organic chemistry ; Mathematics Advanced Polymer Synthesis and Characterization ; Block Copolymer Self-Assembly ; Surfactants and Colloidal Systems
Authors: Jinlong Hu | Yuting Zuo | Yuzhou Hao | Guoyu Shu | Yang Wang | Minxuan Feng | Xuejie Li | Xiaoying Wang | Jun Sun | Xiangdong Ding | Zhibin Gao | Guimei Zhu | Baowen Li
Source: Chinese Physics B (article, 2023-02-10)
Field: Materials Science — Materials Chemistry

Prediction of lattice thermal conductivity with two-stage interpretable machine learning Thermoelectric and thermal materials are essential in achieving carbon neutrality. However, the high cost of lattice thermal conductivity calculations and the limited applicability of classical physical models have led to the inefficient development of thermoelectric materials. In this study, we proposed a two-stage machine learning framework with physical interpretability incorporating domain knowledge to calculate high/low thermal conductivity rapidly. Specifically, crystal graph convolutional neural network (CGCNN) is constructed to predict the fundamental physical parameters related to lattice thermal conductivity. Based on the above physical parameters, an interpretable machine learning model–sure independence screening and sparsifying operator (SISSO), is trained to predict the lattice thermal conductivity. We have predicted the lattice thermal conductivity of all available materials in the open quantum materials database (OQMD) ( https://www.oqmd.org/ ). The proposed approach guides the next step of searching for materials with ultra-high or ultra-low lattice thermal conductivity and promotes the development of new thermal insulation materials and thermoelectric materials. Thermal conductivity ; Lattice (music) ; Materials science ; Thermoelectric effect ; Thermoelectric materials ; Machine learning ; Computer science ; Artificial intelligence ; Interpretability ; Convolutional neural network ; Thermal conduction ; Condensed matter physics ; Thermodynamics ; Physics ; Composite material Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; Thermal properties of materials
Authors: E Weinan | Huan Lei | Pinchen Xie | Linfeng Zhang
Source: Journal of Mathematical Physics (article, 2023-07-01)
Field: Materials Science — Materials Chemistry

Machine learning-assisted multi-scale modeling Neural network-based machine learning is capable of approximating functions in very high dimension with unprecedented efficiency and accuracy. This has opened up many exciting new possibilities, one of which is to use machine learning algorithms to assist multi-scale modeling. In this review, we use three examples to illustrate the process involved in using machine learning in multi-scale modeling: ab initio molecular dynamics, ab initio meso-scale models, such as Landau models and generalized Langevin equation, and hydrodynamic models for non-Newtonian flows. Scale (ratio) ; Computer science ; Ab initio ; Process (computing) ; Artificial neural network ; Artificial intelligence ; Statistical physics ; Machine learning ; Dimension (graph theory) ; Physics ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Protein Structure and Dynamics
Authors: Roohollah Hafizi | Jan Elsner | Jochen Blumberger
Source: Journal of Chemical Theory and Computation (article, 2023-06-22)
Field: Materials Science — Materials Chemistry

Ultrafast Electronic Coupling Estimators: Neural Networks versus Physics-Based Approaches Fast and accurate estimation of electronic coupling matrix elements between molecules is essential for the simulation of charge transfer phenomena in chemistry, materials science, and biology. Here we investigate neural-network-based coupling estimators combined with different protocols for sampling reference data (random, farthest point, and query by committee) and compare their performance to the physics-based analytic overlap method (AOM), introduced previously. We find that neural network approaches can give smaller errors than AOM, in particular smaller maximum errors, while they require an order of magnitude more reference data than AOM, typically one hundred to several hundred training points, down from several thousand required in previous ML works. A Δ-ML approach taking AOM as a baseline is found to give the best overall performance at a relatively small computational overhead of about a factor of 2. Highly flexible π-conjugated organic molecules like non-fullerene acceptors are found to be a particularly challenging case for ML because of the varying (de)localization of the frontier orbitals for different intramolecular geometries sampled along molecular dynamics trajectories. Here the local symmetry functions used in ML are insufficient, and long-range descriptors are expected to give improved performance. Estimator ; Computer science ; Coupling (piping) ; Intramolecular force ; Artificial neural network ; Massively parallel ; Vibronic coupling ; Statistical physics ; Sampling (signal processing) ; Algorithm ; Artificial intelligence ; Theoretical computer science ; Physics ; Molecule ; Quantum mechanics ; Materials science ; Mathematics ; Statistics Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Molecular Junctions and Nanostructures
Authors: Zhi‐Hui Zhang | Chengwei Zhang | Yutao Zhang | Shengwei Deng | Yun‐Fang Yang | An Su | Yuanbin She
Source: RSC Advances (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Predicting band gaps of MOFs on small data by deep transfer learning with data augmentation strategies Porphyrin-based MOFs combine the unique photophysical and electrochemical properties of metalloporphyrins with the catalytic efficiency of MOF materials, making them an important candidate for light energy harvesting and conversion. However, accurate prediction of the band gap of porphyrin-based MOFs is hampered by their complex structure-function relationships. Although machine learning (ML) has performed well in predicting the properties of MOFs with large training datasets, such ML applications become challenging when the training data size of the materials is small. In this study, we first constructed a dataset of 202 porphyrin-based MOFs using DFT computations and increased the training data size using two data augmentation strategies. After that, four state-of-the-art neural network models were pre-trained with the recognized open-source database QMOF and fine-tuned with our augmented self-curated datasets. The GCN models predicted the band gaps of the porphyrin-based materials with the lowest RMSE of 0.2767 eV and MAE of 0.1463 eV. In addition, the data augmentation strategy rotation and mirroring effectively decreased the RMSE by 38.51% and MAE by 50.05%. This study demonstrates that, when proper transfer learning and data augmentation strategies are applied, machine learning models can predict the properties of MOFs using small training data. Mirroring ; Porphyrin ; Computer science ; Artificial intelligence ; Deep learning ; Artificial neural network ; Band gap ; Materials science ; Mean squared error ; Transfer of learning ; Machine learning ; Pattern recognition (psychology) ; Chemistry ; Optoelectronics ; Mathematics ; Statistics Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; Advanced Photocatalysis Techniques
Authors: Yusuf Shaidu | Alex Smith | Eric Taw | Jeffrey B. Neaton
Source: PRX Energy (article, 2023-05-10)
Field: Chemistry — Inorganic Chemistry

Carbon Capture Phenomena in Metal-Organic Frameworks with Neural Network Potentials Diamine-appended metal-organic frameworks are ultraporous materials exhibiting selective and cooperative CO_{2} adsorption mechanisms, leading to chemically tunable step-shaped isotherms and isobars that enable a large fraction of their full CO_{2} capacity to be captured or released with a modest change in temperature or pressure. Although progress has been made in elucidating carbon capture phenomena in this system, its thermal properties are poorly understood. Here, we develop density-functional-theory-derived neural network potentials for amine-appended Mg_{2}(dobpdc), metal-organic frameworks made up of Mg cations and dobpdc [dobpdc^{4−} = 4,4’-dihydroxy-(1,1’-biphenyl)-3,3’-dicarboxylic acid] linkers. These potentials are constructed with an active learning approach where their accuracy and transferability are improved through iterative generation of training datasets based on molecular dynamics. Our potentials predict adsorption energy, mechanical properties, vibrational and thermal properties with and without CO_{2}, reaching ab initio accuracy at a fraction of the computational cost. We compute the temperature-dependent heat capacity and lattice thermal expansion of Mg_{2}(dobpdc), with and without amine appendages and with and without CO_{2}, quantitatively capturing measured trends where available and explaining their molecular origins. Furthermore, we show that these potentials can be incorporated into a simulated annealing approach to identify starting structures for energy minimization needed to compute accurate binding enthalpies of these and other complex systems. Our density-functional-theory-derived neural network potentials explain the temperature evolution of the structure and properties of an important class of metal-organic frameworks at molecular length scales, while providing a necessary foundation for future studies of the chemical dynamics of carbon capture. Metal-organic framework ; Density functional theory ; Ab initio ; Materials science ; Molecular dynamics ; Adsorption ; Chemistry ; Thermodynamics ; Computational chemistry ; Chemical physics ; Physical chemistry ; Organic chemistry ; Physics Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies ; Membrane Separation and Gas Transport
Authors: Paulo Neves | Kelly J. McClure | Jonas Verhoeven | Natalia Dyubankova | Ramil Nugmanov | Andrey Gedich | Sairam Menon | Zhicai Shi | Jörg K. Wegner
Source: Journal of Cheminformatics (article, 2023-02-11)
Field: Materials Science — Materials Chemistry

Global reactivity models are impactful in industrial synthesis applications Artificial Intelligence is revolutionizing many aspects of the pharmaceutical industry. Deep learning models are now routinely applied to guide drug discovery projects leading to faster and improved findings, but there are still many tasks with enormous unrealized potential. One such task is the reaction yield prediction. Every year more than one fifth of all synthesis attempts result in product yields which are either zero or too low. This equates to chemical and human resources being spent on activities which ultimately do not progress the programs, leading to a triple loss when accounting for the cost of opportunity in time wasted. In this work we pre-train a BERT model on more than 16 million reactions from 4 different data sources, and fine tune it to achieve an uncertainty calibrated global yield prediction model. This model is an improvement upon state of the art not just from the increase in pre-train data but also by introducing a new embedding layer which solves a few limitations of SMILES and enables integration of additional information such as equivalents and molecule role into the reaction encoding, the model is called BERT Enriched Embedding (BEE). The model is benchmarked on an open-source dataset against a state-of-the-art synthesis focused BERT showing a near 20-point improvement in r2 score. The model is fine-tuned and tested on an internal company data benchmark, and a prospective study shows that the application of the model can reduce the total number of negative reactions (yield under 5%) ran in Janssen by at least 34%. Lastly, we corroborate the previous results through experimental validation, by directly deploying the model in an on-going drug discovery project and showing that it can also be used successfully as a reagent recommender due to its fast inference speed and reliable confidence estimation, a critical feature for industry application. Benchmark (surveying) ; Computer science ; Task (project management) ; Embedding ; Yield (engineering) ; Deep learning ; Artificial intelligence ; Data science ; Machine learning ; Industrial engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: João Paulo Almeida de Mendonça | Lorenzo A. Mariano | Émilie Devijver | N. Jakse | Roberta Poloni
Source: Journal of Chemical Theory and Computation (article, 2023-10-16)
Field: Materials Science — Materials Chemistry

Artificial Neural Network-Based Density Functional Approach for Adiabatic Energy Differences in Transition Metal Complexes During the past decades, approximate Kohn-Sham density functional theory schemes have garnered many successes in computational chemistry and physics, yet the performance in the prediction of spin state energetics is often unsatisfactory. By means of a machine learning approach, an enhanced exchange and correlation functional is developed to describe adiabatic energy differences in transition metal complexes. The functional is based on the computationally efficient revision of the regularized, strongly constrained, and appropriately normed functional and improved by an artificial neural network correction trained over a small data set of electronic densities, atomization energies, and/or spin state energetics. The training process, performed using a bioinspired nongradient-based approach adapted for this work from the particle swarm optimization, is analyzed and discussed extensively. The resulting machine learned <i>meta</i>-generalized gradient approximation functional is shown to outperform most known density functionals in the prediction of adiabatic energy differences for a diverse set of transition metal complexes with varying local coordinations and metal choices. Density functional theory ; Adiabatic process ; Artificial neural network ; Energy functional ; Hybrid functional ; Statistical physics ; Computer science ; Spin (aerodynamics) ; Set (abstract data type) ; Time-dependent density functional theory ; Artificial intelligence ; Physics ; Machine learning ; Quantum mechanics ; Thermodynamics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Electrocatalysts for Energy Conversion
Authors: H. Mohit | Sanjay Mavinkere Rangappa | Laongdaw Techawinyutham | Suchart Siengchin | Abeer Nasser Al‐Romaizan | Mahmoud A. Hussein | Anish Khan | Abdullah M. Asiri
Source: Construction and Building Materials (article, 2023-06-07)
Field: Materials Science — Polymers and Plastics

Banana/ coir biofibers and carbon/innegra fabrics and BN/MWCNT nanoparticles reinforced UV resistant polyester hybrid composites Materials science ; Composite material ; Coir ; Thermogravimetric analysis ; Ultimate tensile strength ; Flexural strength ; Polyester ; Differential scanning calorimetry ; Carbon nanotube ; Chemical engineering Natural Fiber Reinforced Composites ; Carbon Nanotubes in Composites ; Electrospun Nanofibers in Biomedical Applications