Computational pathology: A survey review and the way forward.

Computational Pathology (CPath) is an interdisciplinary science that augments developments of computational approaches to analyze and model medical histopathology images. The main objective for CPath is to develop infrastructure and workflows of digital diagnostics as an assistive CAD system for clinical pathology, facilitating transformational changes in the diagnosis and treatment of cancer that are mainly address by CPath tools. With evergrowing developments in deep learning and computer vision algorithms, and the ease of the data flow from digital pathology, currently CPath is witnessing a paradigm shift. Despite the sheer volume of engineering and scientific works being introduced for cancer image analysis, there is still a considerable gap of adopting and integrating these algorithms in clinical practice. This raises a significant question regarding the direction and trends that are undertaken in CPath. In this article we provide a comprehensive review of more than 800 papers to address the challenges faced in problem design all-the-way to the application and implementation viewpoints. We have catalogued each paper into a model-card by examining the key works and challenges faced to layout the current landscape in CPath. We hope this helps the community to locate relevant works and facilitate understanding of the field's future directions. In a nutshell, we oversee the CPath developments in cycle of stages which are required to be cohesively linked together to address the challenges associated with such multidisciplinary science. We overview this cycle from different perspectives of data-centric, model-centric, and application-centric problems. We finally sketch remaining challenges and provide directions for future technical developments and clinical integration of CPath. For updated information on this survey review paper and accessing to the original model cards repository, please refer to GitHub. Updated version of this draft can also be found from arXiv.

Permutationally Invariant Deep Learning Approach to Molecular Fingerprinting with Application to Compound Mixtures.

Recent advancements in deep learning have led to widespread applications of its algorithms to synthetic planning and reaction predictions in the field of chemistry. One major area, known as supervised learning, is being explored for predicting certain properties such as reaction yields and types. Many chemical descriptors known as fingerprints are being explored as potential candidates for reaction properties prediction. However, there are few studies that describe the permutational invariance of chemical fingerprints, which are concatenated at some stage before being fed to deep learning architecture. In this work, we show that by utilizing permutational invariance, we consistently see improved results in terms of accuracy relative to previously published studies. Furthermore, we are able to accurately predict hydrogen peroxide loss with our own dataset, which consists of more than 20 ingredients in each chemical formulation.

Ligand-Stabilized Reduced-Dimensionality Perovskites.

Metal halide perovskites have rapidly advanced thin-film photovoltaic performance; as a result, the materials' observed instabilities urgently require a solution. Using density functional theory (DFT), we show that a low energy of formation, exacerbated in the presence of humidity, explains the propensity of perovskites to decompose back into their precursors. We find, also using DFT, that intercalation of phenylethylammonium between perovskite layers introduces quantitatively appreciable van der Waals interactions. These drive an increased formation energy and should therefore improve material stability. Here we report reduced-dimensionality (quasi-2D) perovskite films that exhibit improved stability while retaining the high performance of conventional three-dimensional perovskites. Continuous tuning of the dimensionality, as assessed using photophysical studies, is achieved by the choice of stoichiometry in materials synthesis. We achieve the first certified hysteresis-free solar power conversion in a planar perovskite solar cell, obtaining a 15.3% certified PCE, and observe greatly improved performance longevity.

Quantum-dot-in-perovskite solids.

Heteroepitaxy-atomically aligned growth of a crystalline film atop a different crystalline substrate-is the basis of electrically driven lasers, multijunction solar cells, and blue-light-emitting diodes. Crystalline coherence is preserved even when atomic identity is modulated, a fact that is the critical enabler of quantum wells, wires, and dots. The interfacial quality achieved as a result of heteroepitaxial growth allows new combinations of materials with complementary properties, which enables the design and realization of functionalities that are not available in the single-phase constituents. Here we show that organohalide perovskites and preformed colloidal quantum dots, combined in the solution phase, produce epitaxially aligned 'dots-in-a-matrix' crystals. Using transmission electron microscopy and electron diffraction, we reveal heterocrystals as large as about 60 nanometres and containing at least 20 mutually aligned dots that inherit the crystalline orientation of the perovskite matrix. The heterocrystals exhibit remarkable optoelectronic properties that are traceable to their atom-scale crystalline coherence: photoelectrons and holes generated in the larger-bandgap perovskites are transferred with 80% efficiency to become excitons in the quantum dot nanocrystals, which exploit the excellent photocarrier diffusion of perovskites to produce bright-light emission from infrared-bandgap quantum-tuned materials. By combining the electrical transport properties of the perovskite matrix with the high radiative efficiency of the quantum dots, we engineer a new platform to advance solution-processed infrared optoelectronics.

Perovskite-fullerene hybrid materials suppress hysteresis in planar diodes.

Solution-processed planar perovskite devices are highly desirable in a wide variety of optoelectronic applications; however, they are prone to hysteresis and current instabilities. Here we report the first perovskite-PCBM hybrid solid with significantly reduced hysteresis and recombination loss achieved in a single step. This new material displays an efficient electrically coupled microstructure: PCBM is homogeneously distributed throughout the film at perovskite grain boundaries. The PCBM passivates the key PbI3(-) antisite defects during the perovskite self-assembly, as revealed by theory and experiment. Photoluminescence transient spectroscopy proves that the PCBM phase promotes electron extraction. We showcase this mixed material in planar solar cells that feature low hysteresis and enhanced photovoltage. Using conductive AFM studies, we reveal the memristive properties of perovskite films. We close by positing that PCBM, by tying up both halide-rich antisites and unincorporated halides, reduces electric field-induced anion migration that may give rise to hysteresis and unstable diode behaviour.

Solar cells. Low trap-state density and long carrier diffusion in organolead trihalide perovskite single crystals.

The fundamental properties and ultimate performance limits of organolead trihalide MAPbX3 (MA = CH3NH3(+); X = Br(-) or I(-)) perovskites remain obscured by extensive disorder in polycrystalline MAPbX3 films. We report an antisolvent vapor-assisted crystallization approach that enables us to create sizable crack-free MAPbX3 single crystals with volumes exceeding 100 cubic millimeters. These large single crystals enabled a detailed characterization of their optical and charge transport characteristics. We observed exceptionally low trap-state densities on the order of 10(9) to 10(10) per cubic centimeter in MAPbX3 single crystals (comparable to the best photovoltaic-quality silicon) and charge carrier diffusion lengths exceeding 10 micrometers. These results were validated with density functional theory calculations.

Materials processing routes to trap-free halide perovskites.

Photovoltaic devices based on lead iodide perovskite films have seen rapid advancements, recently achieving an impressive 17.9% certified solar power conversion efficiency. Reports have consistently emphasized that the specific choice of growth conditions and chemical precursors is central to achieving superior performance from these materials; yet the roles and mechanisms underlying the selection of materials processing route is poorly understood. Here we show that films grown under iodine-rich conditions are prone to a high density of deep electronic traps (recombination centers), while the use of a chloride precursor avoids the formation of key defects (Pb atom substituted by I) responsible for short diffusion lengths and poor photovoltaic performance. Furthermore, the lowest-energy surfaces of perovskite crystals are found to be entirely trap-free, preserving both electron and hole delocalization to a remarkable degree, helping to account for explaining the success of polycrystalline perovskite films. We construct perovskite films from I-poor conditions using a lead acetate precursor, and our measurement of a long (600 ± 40 nm) diffusion length confirms this new picture of the importance of growth conditions.

Molecular polarizabilities in aqueous proton transfer reactions.

Dipole polarizabilities of individual ions and molecules are computed from first principles in three condensed-phase systems: pure water, pure hydrofluoric acid, and an equimolar mixture of water and hydrofluoric acid in which HF is mostly ionized. We find that the polarizability of fluorine and oxygen centers varies linearly with the value of the bond order, which measures the local degree of advancement of the ionization reaction F-H+H(2)O<==>[F(delta-).H.(delta+)OH(2)]<==>F(-)+H(3)O(+). This observation explains the validity of the Lorentz-Lorenz formula for mixtures of acids and water and could have important practical consequences concerning the construction of empirical polarizable reactive force fields. Our results are consistent with the Mulliken charge-transfer picture of proton transfer reactions. The present results also suggest that the average isotropic polarizability of a chemical entity changes substantially only when that entity is involved in charge-transfer processes.