jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Michael Craig | Max García‐Melchor
Source: Molecules (article, 2021-10-21)
Field: Materials Science — Materials Chemistry

Applying Active Learning to the Screening of Molecular Oxygen Evolution Catalysts The oxygen evolution reaction (OER) can enable green hydrogen production; however, the state-of-the-art catalysts for this reaction are composed of prohibitively expensive materials. In addition, cheap catalysts have associated overpotentials that render the reaction inefficient. This impels the search to discover novel catalysts for this reaction computationally. In this communication, we present machine learning algorithms to enhance the hypothetical screening of molecular OER catalysts. By predicting calculated binding energies using Gaussian process regression (GPR) models and applying active learning schemes, we provide evidence that our algorithm can improve computational efficiency by guiding simulations towards candidates with promising OER descriptor values. Furthermore, we derive an acquisition function that, when maximized, can identify catalysts that can exhibit theoretical overpotentials that circumvent the constraints imposed by linear scaling relations by attempting to enforce a specific mechanism. Finally, we provide a brief perspective on the appropriate sets of molecules to consider when screening complexes that could be stable and active for this reaction. Catalysis ; Oxygen evolution ; Computer science ; Function (biology) ; Scaling ; Hydrogen molecule ; Gaussian process ; Molecule ; Chemistry ; Biochemical engineering ; Gaussian ; Computational chemistry ; Mathematics ; Physical chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; CO2 Reduction Techniques and Catalysts
Authors: Shuwen Yang | Ziyao Li | Guojie Song | Lingsheng Cai
Source: arXiv (Cornell University) (preprint, 2021-11-28)
Field: Materials Science — Materials Chemistry

Deep Molecular Representation Learning via Fusing Physical and Chemical Information Molecular representation learning is the first yet vital step in combining deep learning and molecular science. To push the boundaries of molecular representation learning, we present PhysChem, a novel neural architecture that learns molecular representations via fusing physical and chemical information of molecules. PhysChem is composed of a physicist network (PhysNet) and a chemist network (ChemNet). PhysNet is a neural physical engine that learns molecular conformations through simulating molecular dynamics with parameterized forces; ChemNet implements geometry-aware deep message-passing to learn chemical / biomedical properties of molecules. Two networks specialize in their own tasks and cooperate by providing expertise to each other. By fusing physical and chemical information, PhysChem achieved state-of-the-art performances on MoleculeNet, a standard molecular machine learning benchmark. The effectiveness of PhysChem was further corroborated on cutting-edge datasets of SARS-CoV-2. Benchmark (surveying) ; Computer science ; Representation (politics) ; Artificial intelligence ; Enhanced Data Rates for GSM Evolution ; Artificial neural network ; Deep learning ; Parameterized complexity ; Physics engine ; Algorithm Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Elif Sorkun | Qi Zhang | Abhishek Khetan | murat cihan sorkun | Süleyman Er
Source: ChemRxiv (preprint, 2021-04-12)
Field: Chemistry — Electrochemistry

RedDB, a Computational Database of Electroactive Molecules for Aqueous Redox Flow Batteries An increasing number of electroactive compounds have recently been explored for their use in high-performance redox flow batteries for grid-scale energy storage. Given the vast and highly diverse chemical space of the candidate compounds, it is alluring to access their physicochemical properties in a speedy way. High-throughput virtual screening approaches, which use powerful combinatorial techniques for systematic enumerations of large virtual chemical libraries and respective property evaluations, are indispensable tools for an agile exploration of the designated chemical space. Herein, RedDB: a computational database that contains 31,677 molecules from two prominent classes of organic electroactive compounds, quinones and aza-aromatics, has been presented. RedDB incorporates miscellaneous physicochemical property information of the compounds that can potentially be employed as battery performance descriptors. RedDB’s development steps, including: i)chemical library generation, ii) molecular property prediction based on quantum chemical calculations, iii) aqueous solubility prediction using machine learning, and iv) data processing and database creation, have been described. Chemical space ; Computer science ; Virtual screening ; Redox ; Quantum chemical ; Property (philosophy) ; Chemical database ; Database ; Combinatorial chemistry ; Chemistry ; Biochemical engineering ; Molecule ; Drug discovery ; Organic chemistry Electrochemical Analysis and Applications ; Electrocatalysts for Energy Conversion ; Advanced battery technologies research
Authors: Daniel Probst | Philippe Schwaller | Jean‐Louis Reymond
Source:  (preprint, 2021-07-06)
Field: Materials Science — Materials Chemistry

Reaction Classification and Yield Prediction using the Differential Reaction Fingerprint DRFP Predicting the nature and outcome of reactions using computational methods is a crucial tool to accelerate chemical research. The recent application of deep learning-based learned fingerprints to reaction classification and reaction yield prediction has shown an impressive increase in performance compared to previous methods such as DFT- and structure-based fingerprints. However, learned fingerprints require large training data sets, are inherently biased, and are based on complex deep learning architectures. Here we present the differential reaction fingerprint \textit{DRFP}. The \textit{DRFP} algorithm takes a reaction SMILES as an input and creates a binary fingerprint based on the symmetric difference of two sets containing the circular molecular n-grams generated from the molecules listed left and right from the reaction arrow, respectively, without the need for distinguishing between reactants and reagents. We show that \textit{DRFP} outperforms DFT-based fingerprints in reaction yield prediction and other structure-based fingerprints in reaction classification, reaching the performance of state-of-the-art learned fingerprints in both tasks while being data-independent. Computer science ; Fingerprint (computing) ; Yield (engineering) ; Binary number ; Artificial intelligence ; Pattern recognition (psychology) ; Binary classification ; Differential (mechanical device) ; Algorithm ; Mathematics ; Support vector machine ; Physics ; Thermodynamics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Kevin Tran | Willie Neiswanger | Kirby Broderick | Eric Xing | Jeff Schneider | Zachary W. Ulissi
Source: The Journal of Chemical Physics (article, 2021-03-25)
Field: Materials Science — Materials Chemistry

Computational catalyst discovery: Active classification through myopic multiscale sampling The recent boom in computational chemistry has enabled several projects aimed at discovering useful materials or catalysts. We acknowledge and address two recurring issues in the field of computational catalyst discovery. First, calculating macro-scale catalyst properties is not straightforward when using ensembles of atomic-scale calculations [e.g., density functional theory (DFT)]. We attempt to address this issue by creating a multi-scale model that estimates bulk catalyst activity using adsorption energy predictions from both DFT and machine learning models. The second issue is that many catalyst discovery efforts seek to optimize catalyst properties, but optimization is an inherently exploitative objective that is in tension with the explorative nature of early-stage discovery projects. In other words, why invest so much time finding a "best" catalyst when it is likely to fail for some other, unforeseen problem? We address this issue by relaxing the catalyst discovery goal into a classification problem: "What is the set of catalysts that is worth testing experimentally?" Here, we present a catalyst discovery method called myopic multiscale sampling, which combines multiscale modeling with automated selection of DFT calculations. It is an active classification strategy that seeks to classify catalysts as "worth investigating" or "not worth investigating" experimentally. Our results show an ∼7-16 times speedup in catalyst classification relative to random sampling. These results were based on offline simulations of our algorithm on two different datasets: a larger, synthesized dataset and a smaller, real dataset. Catalysis ; Field (mathematics) ; Set (abstract data type) ; Boom ; Speedup ; Selection (genetic algorithm) Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; CO2 Reduction Techniques and Catalysts
Authors: Nan Tang | Lizabeth Quigley | Walker L. Boldman | Cameron S. Jorgensen | Rémi Koch | Daniel J. O’Leary | Hugh Medal | Philip D. Rack | Dustin A. Gilbert
Source: Physical Review Materials (article, 2021-11-17)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Magnetism in metastable and annealed compositionally complex alloys Compositionally complex materials (CCMs) present a potential paradigm shift in the design of magnetic materials. These alloys exhibit long-range structural order coupled with limited or no chemical order. As a result, extreme local environments exist with a large variations in the magnetic energy terms, which can manifest large changes in the magnetic behavior. In the current work, the magnetic properties of (Cr, Mn, Fe, Ni) alloys are presented. These materials were prepared by room-temperature combinatorial sputtering, resulting in a range of compositions with a single bcc structural phase and no chemical ordering. The combinatorial growth technique allows CCMs to be prepared outside of their thermodynamically stable phase, enabling the exploration of otherwise inaccessible order. The mixed ferromagnetic and antiferromagnetic interactions in these alloys causes frustrated magnetic behavior, which results in an extremely low coercivity $(&lt;1\phantom{\rule{0.16em}{0ex}}\mathrm{mT})$, which increases rapidly at 50 K. At low temperatures, the coercivity achieves values of nearly 500 mT, which is comparable to some high-anisotropy magnetic materials. Commensurate with the divergent coercivity is an atypical drop in the temperature dependent magnetization. These effects are explained by a mixed magnetic phase model, consisting of ferro-, antiferro-, and frustrated magnetic regions, and are rationalized by simulations. A machine-learning algorithm is employed to visualize the parameter space and inform the development of subsequent compositions. Annealing the samples at 600 \ifmmode^\circ\else\textdegree\fi{}C orders the sample, more-than doubling the Curie temperature and increasing the saturation magnetization by as much as 5\ifmmode\times\else\texttimes\fi{}. Simultaneously, the large coercivities are suppressed, resulting in magnetic behavior that is largely temperature independent over a range of 350 K. The ability to transform from a hard magnet to a soft magnet over a narrow temperature range makes these materials promising for heat-assisted recording technologies. Materials science ; Metastability ; Magnetism ; Condensed matter physics ; Ferromagnetism ; Engineering physics Magnetic Properties of Alloys ; Magnetic and transport properties of perovskites and related materials ; High-pressure geophysics and materials
Authors: Heather J. Kulik
Source: Israel Journal of Chemistry (article, 2021-04-15)
Field: Materials Science — Materials Chemistry

What's Left for a Computational Chemist To Do in the Age of Machine Learning? Abstract Machine learning (ML) has become a central focus of the computational chemistry community. I will first discuss my personal history in the field. Then I will provide a broader view of how this resurgence in ML interest echoes and advances upon earlier efforts. Although numerous changes have brought about this latest wave, one of the most significant is the increased accuracy and efficiency of low‐cost methods (e. g., density functional theory or DFT) that have made it possible to generate large data sets for ML models. ML has also been used to bypass, guide, or improve DFT. The field of computational chemistry thus finds itself at a crossroads as ML both augments and supersedes traditional efforts. I will present what I believe the role of the computational chemist will be in this evolving landscape, with specific focus on my experience in the development of autonomous workflows in computational materials discovery for open‐shell transition‐metal chemistry. Chemistry ; Chemist ; Workflow ; Focus (optics) ; Field (mathematics) ; Computational model ; Artificial intelligence ; Data science ; Nanotechnology ; Computer science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Monami Tsunawaki | Satoru Fukuhara | Yasushi Shibuta
Source: MATERIALS TRANSACTIONS (article, 2021-05-13)
Field: Materials Science — Materials Chemistry

Hierarchical Clustering of Structural and Electronic Characteristics Obtained from Molecular Dynamics Simulation of Catalytic Reaction on Metal Nanoparticle Unsupervised machine learning (ML) is examined for the result of molecular dynamics (MD) simulation to extract characteristics of catalytic reaction. O–H bond dissociation of ethanol on Fe–Co nanoparticle in ab initio MD simulation [S. Fukuhara et al., Chem. Phys. Lett. 731 (2019) 136619] is employed as an example. Hierarchical clustering of radial distribution function successfully classifies coordinates on reaction in the dendrogram. Moreover, receiver operating characteristic curve reveals the distance to the farthest-neighbor atom from the target atom is a dominant descriptor for the clustering. An optimum structure of catalytic nanoparticle is predicted based on these automated analyses. This study shows a new way of post-process of results of MD simulations based on the unsupervised learning technique and it paves the way for a new possibility of ML-based materials design. Cluster analysis ; Molecular dynamics ; Catalysis ; Materials science ; Nanoparticle ; Dissociation (chemistry) ; Hierarchical clustering ; Atom (system on chip) ; Chemical physics ; Computational chemistry ; Nanotechnology ; Computer science ; Physical chemistry ; Artificial intelligence ; Chemistry ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Electron and X-Ray Spectroscopy Techniques
Authors: Sangeeth Balakrishnan | Francis G. VanGessel | Zois Boukouvalas | Brian C. Barnes | Mark Fuge | Peter W. Chung
Source: Molecular Informatics (article, 2021-04-28)
Field: Materials Science — Materials Chemistry

Locally Optimizable Joint Embedding Framework to Design Nitrogen‐rich Molecules that are Similar but Improved Deep learning has shown great potential for generating molecules with desired properties. But the cost and time required to obtain relevant property data have limited study to only a few classes of materials for which extensive data have already been collected. We develop a deep learning method that combines a generative model with a property prediction model to fuse small data of one class of molecules with larger data in another class. Common low-level physicochemical properties are jointly embedded into a latent space that can be used to design molecules in the smaller class. The chemical space around the molecules in the training set is explored through local gradient ascent optimization. Based on nine molecules from the original training set, nine new molecules are found to have improved properties while remaining structurally similar to the training molecules thereby easing requirements for entirely new synthesis routes. Validation is performed using an equilibrium thermochemistry code to verify the molecules and target properties. A specific example targeting the Chapman-Jouguet velocity and small data for nitrogen-rich molecules is shown. Despite the relative lack of nitrogen-rich molecule data, the results demonstrate that fusing and joint embedding with plentiful low nitrogen molecular data can produce higher generative performance than using the scarce data alone. Chemical space ; Embedding ; Thermochemistry ; Molecule ; Class (philosophy) ; Set (abstract data type) ; Data set ; Computer science ; Property (philosophy) ; Cheminformatics ; Generative model ; Biological system ; Artificial intelligence ; Generative grammar ; Chemistry ; Computational chemistry ; Drug discovery Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Joelle Medinger | Miroslava Nedyalkova | Marco Furlan | Thomas Lüthi | Jürgen Hofmann | A. Neels | Marco Lattuada
Source: ACS Applied Materials & Interfaces (article, 2021-10-01)
Field: Materials Science — Biomaterials

Preparation and Machine-Learning Methods of Nacre-like Composites from the Self-Assembly of Magnetic Colloids Exposed to Rotating Magnetic Fields Composite materials designed by nature, such as nacre, can display unique mechanical properties and have therefore been often mimicked by scientists. In this work, we prepared composite materials mimicking the nacre structure in two steps. First, we synthesized a silica gel skeleton with a layered structure using a bottom-up approach by modifying a sol-gel synthesis. Magnetic colloids were added to the sol solution, and a rotating magnetic field was applied during the sol-gel transition. When exposed to a rotating magnetic field, magnetic colloids organize in layers parallel to the plane of rotation of the field and template the growing silica phase, resulting in a layered anisotropic silica network mimicking the nacre's inorganic phase. Heat treatment has been applied to further harden the silica monoliths. The final nacre-inspired composite is created by filling the porous structure with a monomer, leading to a soft elastomer upon polymerization. Compression tests of the platelet-structured composite show that the mechanical properties of the nacre-like composite material far exceed those of nonstructured composite materials with an identical chemical composition. Increased toughness and a nearly 10-fold increase in Young's modulus were achieved. The natural brittleness and low elastic deformation of silica monoliths could be overcome by mimicking the natural architecture of nacre. Pattern recognition obtained with a classification of machine learning algorithms was applied to achieve a better understanding of the physical and chemical parameters that have the highest impact on the mechanical properties of the monoliths. Multivariate statistical analysis was performed to show that the structural control and the heat treatment have a very strong influence on the mechanical properties of the monoliths. Materials science ; Composite material ; Composite number ; Toughness ; Elastomer ; Brittleness ; Sol-gel ; Nanocomposite ; Nanotechnology Calcium Carbonate Crystallization and Inhibition ; Paleontology and Stratigraphy of Fossils ; Bone Tissue Engineering Materials
Authors: Sauradeep Majumdar | Seyed Mohamad Moosavi | Kevin Maik Jablonka | Daniele Ongari | Berend Smit
Source: ChemRxiv (preprint, 2021-09-28)
Field: Chemistry — Inorganic Chemistry

Diversifying databases of metal organic frameworks for high-throughput computational screening By combining metal nodes and organic linkers, an infinite number of metal organic frameworks (MOFs) can be designed in silico. When making new databases of such hypothetical MOFs, we need to assure that they not only contribute towards the growth of the count of structures but also add different chemistry to existing databases. In this study, we designed a database of ~20,000 hypothetical MOFs which are diverse in terms of their chemical design space—metal nodes, organic linkers, functional groups and pore geometries. Using Machine Learning techniques, we visualized and quantified the diversity of these structures. We find that on adding the structures of our database, the overall diversity metrics of hypothetical databases improve, especially in terms of the chemistry of metal nodes. We then assessed the usefulness of diverse structures by evaluating their performance, using grand-canonical Monte Carlo simulations, in two important environmental applications—post combustion carbon capture and hydrogen storage. We find that many of these structures perform better than widely used benchmark materials such as Zeolite-13X (for post combustion carbon capture) and MOF-5 (for hydrogen storage). Benchmark (surveying) ; Database ; Computer science ; Metal-organic framework ; Chemical space ; Hydrogen storage ; Combustion ; Hydrogen ; Chemistry ; Drug discovery Metal-Organic Frameworks: Synthesis and Applications ; Zeolite Catalysis and Synthesis ; Machine Learning in Materials Science
Authors: Zhantao Chen | Nina Andrejevic | Tess Smidt | Zhiwei Ding | Qian Xu | Yen‐Ting Chi | Quynh T. Thanh Nguyen | Ahmet Alatas | Jing Kong | Mingda Li
Source: Advanced Science (article, 2021-06-01)
Field: Materials Science — Materials Chemistry

Machine Learning: Direct Prediction of Phonon Density of States With Euclidean Neural Networks (Adv. Sci. 12/2021) Phonon density‐of‐states is a key property that governs materials thermal properties but is nontrivial to compute or measure. A neural network that carries full crystal symmetry allows a prediction of phonon density‐of‐states using a small volume of training data, approaching ab initio accuracy but with significantly increased efficiency, as demonstrated in article number 2004214, by Mingda Li, Zhantao Chen, Nina Andrejevic, Tess Smidt, and co‐workers. This work enables direct structure‐property design of materials with superior thermal properties. [Image: see text] Phonon ; Artificial neural network ; Measure (data warehouse) ; Ab initio ; Work (physics) ; Condensed matter physics ; Thermal ; Euclidean geometry ; Symmetry (geometry) ; Density of states ; Materials science ; Statistical physics ; Physics ; Computer science ; Machine learning ; Mathematics ; Quantum mechanics ; Thermodynamics ; Data mining ; Geometry Machine Learning in Materials Science
Authors: Qin Deng | Bin Lin
Source: Materials Today Communications (article, 2021-06-26)
Field: Materials Science — Materials Chemistry

Exploring structure-composition relationships of cubic perovskite oxides via extreme feature engineering and automated machine learning Ionic radius ; Crystal structure ; Materials science ; Lattice constant ; Ionic bonding ; Interpretability ; Lattice (music) ; Machine learning ; Electronegativity ; Perovskite (structure) ; Algorithm ; Artificial intelligence ; Computer science ; Chemical physics ; Diffraction ; Crystallography ; Ion ; Physics ; Chemistry ; Optics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Photocatalysis Techniques